From 9f551327b0cc88fea15c1f38c11e93bd1b4a8b22 Mon Sep 17 00:00:00 2001
From: Madelyn Olson <madelyneolson@gmail.com>
Date: Thu, 2 Apr 2026 15:02:11 -0700
Subject: [PATCH 1/7] Add blog post type tags with filtering and build-time
 validation

Signed-off-by: Madelyn Olson <madelyneolson@gmail.com>
---
 config.toml                                   |  3 +
 content/blog/2024-04-12-hello-world.md        |  1 +
 content/blog/2024-04-16-valkey-7-2-5-out.md   |  1 +
 content/blog/2024-04-26-modules-101.md        |  1 +
 content/blog/2024-05-24-may-roundup.md        |  1 +
 .../index.md                                  |  1 +
 .../blog/2024-07-07-unlock-one-million-rps.md |  1 +
 content/blog/2024-07-31-valkey-8-0-0-rc1.md   |  1 +
 ...2024-08-29-valkey-memory-efficiency-8-0.md |  1 +
 ...2024-09-13-unlock-one-million-rps-part2.md |  1 +
 content/blog/2024-09-16-valkey-8-ga.md        |  1 +
 .../2024-11-21-testing-the-limits/index.md    |  1 +
 .../2024-12-20-2024-year-of-valkey/index.md   |  1 +
 .../blog/2024-12-22-az-affinity-strategy.md   |  1 +
 .../blog/2025-03-28-new-hash-table/index.md   |  1 +
 .../2025-03-4-go-client-in-public-preview.md  |  1 +
 content/blog/2025-04-02-valkey-8-1-0-ga.md    |  1 +
 .../2025-04-09-introducing-bloom-filters.md   |  1 +
 ...5-04-27-valkey-modules-rust-sdk-updates.md |  1 +
 .../blog/2025-05-14-upgrade-stories-vol1.md   |  1 +
 .../index.md                                  |  1 +
 .../index.md                                  |  1 +
 .../index.md                                  |  1 +
 ...ng-enhanced-json-capabilities-in-valkey.md |  2 +
 .../blog/2025-07-10-keyspace-save-the-date.md |  1 +
 content/blog/2025-07-21-valkey-on-k0rdent.md  |  2 +
 content/blog/2025-08-04-valkey-swift/index.md |  1 +
 content/blog/2025-08-12-keyspace-schedule.md  |  2 +
 ...-09-11-valkey-investment-in-open-source.md |  1 +
 .../2025-09-25-numbered-databases/index.md    |  1 +
 .../index.md                                  |  1 +
 .../blog/2025-10-02-50-million-zsets/index.md |  1 +
 ...-properly-secure-your-valkey-deployment.md |  1 +
 .../blog/2025-10-20-1-billion-rps/index.md    |  1 +
 .../2025-10-21-introducing-valkey-9/index.md  |  1 +
 .../2025-10-27-atomic-slot-migration/index.md |  1 +
 .../2026-01-06-valkey-helm-chart/index.md     |  1 +
 .../blog/2026-01-22-2025-year-end/index.md    |  2 +
 .../2026-02-04-valkey-newsletter-new/index.md |  2 +
 .../2026-02-19-operational-lessons/index.md   |  1 +
 .../index.md                                  |  1 +
 .../2026-03-10-valkey-search-1_2/index.md     |  2 +
 .../2026-03-17-spring-data-valkey/index.md    |  1 +
 .../2026-03-27-valkey-tooling-primitives.md   |  1 +
 content/blog/README                           | 54 ++++++++++++++++++
 content/blog/whats-new-june-2024.md           |  1 +
 sass/_valkey.scss                             | 41 ++++++++++++++
 templates/blog-page.html                      |  1 +
 templates/blog.html                           | 55 ++++++++++++++++++-
 49 files changed, 203 insertions(+), 1 deletion(-)
 create mode 100644 content/blog/README

diff --git a/config.toml b/config.toml
index 9bbae71b..2656f25c 100644
--- a/config.toml
+++ b/config.toml
@@ -53,3 +53,6 @@ publish_hold = [
     "/topics/rdd/"
 ]
 
+
+[extra.blog_types]
+types = ["Technical Deep Dive", "Community Highlight", "How-to", "Announcements", "Events"]
diff --git a/content/blog/2024-04-12-hello-world.md b/content/blog/2024-04-12-hello-world.md
index a308c124..19ac947e 100644
--- a/content/blog/2024-04-12-hello-world.md
+++ b/content/blog/2024-04-12-hello-world.md
@@ -8,6 +8,7 @@ description= "Welcome! For the inaugural blog post on valkey.io, I’d like to r
 authors= ["kyledvs"]
 
 [extra]
+type = "Announcements"
 categories= "update"
 featured = false
 featured_image = "/assets/media/featured/random-05.webp"
diff --git a/content/blog/2024-04-16-valkey-7-2-5-out.md b/content/blog/2024-04-16-valkey-7-2-5-out.md
index 0d5cd190..ce771309 100644
--- a/content/blog/2024-04-16-valkey-7-2-5-out.md
+++ b/content/blog/2024-04-16-valkey-7-2-5-out.md
@@ -8,6 +8,7 @@ description= "Exciting times!I'm pleased to announce that you can start using th
 authors= ["kyledvs"]
 
 [extra]
+type = "Announcements"
 categories= "update"
 featured = false
 featured_image = "/assets/media/featured/random-04.webp"
diff --git a/content/blog/2024-04-26-modules-101.md b/content/blog/2024-04-26-modules-101.md
index 6780dd57..b5e6e81c 100644
--- a/content/blog/2024-04-26-modules-101.md
+++ b/content/blog/2024-04-26-modules-101.md
@@ -8,6 +8,7 @@ description= "The idea of modules is to allow adding extra features (such as new
 authors= ["dmitrypol"]
 
 [extra]
+type = "How-to"
 categories= "modules"
 featured = false
 featured_image = "/assets/media/featured/random-03.webp"
diff --git a/content/blog/2024-05-24-may-roundup.md b/content/blog/2024-05-24-may-roundup.md
index ec1ad6e4..8c1af7c4 100644
--- a/content/blog/2024-05-24-may-roundup.md
+++ b/content/blog/2024-05-24-may-roundup.md
@@ -8,6 +8,7 @@ description= "It's become clear that people want to talk about Valkey and have b
 authors= ["kyledvs"]
 
 [extra]
+type = "Community Highlight"
 categories= "update"
 featured = false
 featured_image = "/assets/media/featured/random-02.webp"
diff --git a/content/blog/2024-06-27-using-bitnami-valkey-chart/index.md b/content/blog/2024-06-27-using-bitnami-valkey-chart/index.md
index 4e407f0d..9aba7b61 100644
--- a/content/blog/2024-06-27-using-bitnami-valkey-chart/index.md
+++ b/content/blog/2024-06-27-using-bitnami-valkey-chart/index.md
@@ -7,6 +7,7 @@ aliases= [
 ]
 authors=["rafariossaa"]
 [extra]
+type = "How-to"
 featured = false
 featured_image = "/assets/media/featured/random-01.webp"
 
diff --git a/content/blog/2024-07-07-unlock-one-million-rps.md b/content/blog/2024-07-07-unlock-one-million-rps.md
index b04f96e9..397ee529 100644
--- a/content/blog/2024-07-07-unlock-one-million-rps.md
+++ b/content/blog/2024-07-07-unlock-one-million-rps.md
@@ -14,6 +14,7 @@ description= "Learn about the new performance improvements in Valkey 8 which red
 # Each author corresponds to a biography file (more info later in this document)
 authors= [ "dantouitou", "uriyagelnik"]
 [extra]
+type = "Technical Deep Dive"
 featured = true
 featured_image = "/assets/media/featured/random-01.webp"
 +++
diff --git a/content/blog/2024-07-31-valkey-8-0-0-rc1.md b/content/blog/2024-07-31-valkey-8-0-0-rc1.md
index 306ecb05..3c7290a7 100644
--- a/content/blog/2024-07-31-valkey-8-0-0-rc1.md
+++ b/content/blog/2024-07-31-valkey-8-0-0-rc1.md
@@ -14,6 +14,7 @@ description= "The first release candidate of Valkey 8.0 is now available! Come l
 # Each author corresponds to a biography file (more info later in this document)
 authors= [ "pingxie", "madolson" ]
 [extra]
+type = "Announcements"
 featured = false
 featured_image = "/assets/media/featured/random-07.webp"
 +++
diff --git a/content/blog/2024-08-29-valkey-memory-efficiency-8-0.md b/content/blog/2024-08-29-valkey-memory-efficiency-8-0.md
index 2794ca25..993a398d 100644
--- a/content/blog/2024-08-29-valkey-memory-efficiency-8-0.md
+++ b/content/blog/2024-08-29-valkey-memory-efficiency-8-0.md
@@ -4,6 +4,7 @@ date= 2024-09-04 01:01:01
 description= "Learn about the new memory efficiency improvements in Valkey 8 which reduces memory overhead, which allows more data to be stored in the same amount of memory."
 authors= [ "hpatro"]
 [extra]
+type = "Technical Deep Dive"
 featured = true
 featured_image = "/assets/media/featured/random-05.webp"
 +++
diff --git a/content/blog/2024-09-13-unlock-one-million-rps-part2.md b/content/blog/2024-09-13-unlock-one-million-rps-part2.md
index eba88064..0ea58be1 100644
--- a/content/blog/2024-09-13-unlock-one-million-rps-part2.md
+++ b/content/blog/2024-09-13-unlock-one-million-rps-part2.md
@@ -14,6 +14,7 @@ description= "Maximize the performance of your hardware with memory access amort
 # Each author corresponds to a biography file (more info later in this document)
 authors= [ "dantouitou", "uriyagelnik"]
 [extra]
+type = "Technical Deep Dive"
 featured = false
 featured_image = "/assets/media/featured/random-06.webp"
 +++
diff --git a/content/blog/2024-09-16-valkey-8-ga.md b/content/blog/2024-09-16-valkey-8-ga.md
index ec7f1c5e..0ebc1366 100644
--- a/content/blog/2024-09-16-valkey-8-ga.md
+++ b/content/blog/2024-09-16-valkey-8-ga.md
@@ -4,6 +4,7 @@ description = "Today marks a milestone for the Valkey project: the first major r
 date= 2024-09-16 01:01:01
 authors= ["kyledvs"]
 [extra]
+type = "Announcements"
 featured = false
 featured_image = "/assets/media/featured/random-05.webp"
 
diff --git a/content/blog/2024-11-21-testing-the-limits/index.md b/content/blog/2024-11-21-testing-the-limits/index.md
index 9b71df21..6cb97150 100644
--- a/content/blog/2024-11-21-testing-the-limits/index.md
+++ b/content/blog/2024-11-21-testing-the-limits/index.md
@@ -4,6 +4,7 @@ description = "While most people won't go to production on a Raspberry Pi, we'll
 date= 2024-11-21 01:01:01
 authors= ["dtaivpp"]
 [extra]
+type = "Technical Deep Dive"
 featured = false
 featured_image = "/assets/media/featured/random-07.webp"
 +++
diff --git a/content/blog/2024-12-20-2024-year-of-valkey/index.md b/content/blog/2024-12-20-2024-year-of-valkey/index.md
index c05a1dd7..5a9bf175 100644
--- a/content/blog/2024-12-20-2024-year-of-valkey/index.md
+++ b/content/blog/2024-12-20-2024-year-of-valkey/index.md
@@ -5,6 +5,7 @@ description = "The end of the calendar year is a great time to reflect, but for
 date= 2024-12-20 01:01:01
 authors= ["kyledvs"]
 [extra]
+type = "Community Highlight"
 featured = false
 featured_image = "/assets/media/featured/random-05.webp"
 
diff --git a/content/blog/2024-12-22-az-affinity-strategy.md b/content/blog/2024-12-22-az-affinity-strategy.md
index 9bfbb74f..5cdba6ea 100644
--- a/content/blog/2024-12-22-az-affinity-strategy.md
+++ b/content/blog/2024-12-22-az-affinity-strategy.md
@@ -4,6 +4,7 @@ date= 2025-01-08 01:01:01
 description= "By implementing AZ affinity routing in Valkey and using GLIDE, you can achieve lower latency and cost savings by routing requests to replicas in the same AZ as the client."
 authors= [ "asafporatstoler", "adarovadya", "muhammadawawdi"]
 [extra]
+type = "Technical Deep Dive"
 featured = true
 featured_image = "/assets/media/featured/random-04.webp"
 +++
diff --git a/content/blog/2025-03-28-new-hash-table/index.md b/content/blog/2025-03-28-new-hash-table/index.md
index c83c66b5..760c8135 100644
--- a/content/blog/2025-03-28-new-hash-table/index.md
+++ b/content/blog/2025-03-28-new-hash-table/index.md
@@ -4,6 +4,7 @@ date= 2025-03-28 00:00:00
 description= "Designing a state-of-the art hash table"
 authors= ["zuiderkwast"]
 [extra]
+type = "Technical Deep Dive"
 featured = false
 featured_image = "/assets/media/featured/random-05.webp"
 
diff --git a/content/blog/2025-03-4-go-client-in-public-preview.md b/content/blog/2025-03-4-go-client-in-public-preview.md
index b0d36330..5268e8e2 100644
--- a/content/blog/2025-03-4-go-client-in-public-preview.md
+++ b/content/blog/2025-03-4-go-client-in-public-preview.md
@@ -4,6 +4,7 @@ description = "Valkey Glide now supports GO. Read to learn more about the new cl
 date= 2025-03-04 01:01:01
 authors= [ "niharikabhavaraju"]
 [extra]
+type = "Announcements"
 featured = false
 featured_image = "/assets/media/featured/default.webp"
 +++
diff --git a/content/blog/2025-04-02-valkey-8-1-0-ga.md b/content/blog/2025-04-02-valkey-8-1-0-ga.md
index b7806e8b..12f431b2 100644
--- a/content/blog/2025-04-02-valkey-8-1-0-ga.md
+++ b/content/blog/2025-04-02-valkey-8-1-0-ga.md
@@ -14,6 +14,7 @@ description= "Valkey 8.1 is now generally available! Come learn about the exciti
 # Each author corresponds to a biography file (more info later in this document)
 authors= [ "rdias", "mvisser" ]
 [extra]
+type = "Announcements"
 featured = false
 featured_image = "/assets/media/featured/random-03.webp"
 +++
diff --git a/content/blog/2025-04-09-introducing-bloom-filters.md b/content/blog/2025-04-09-introducing-bloom-filters.md
index a682d5ac..c4c6b13a 100644
--- a/content/blog/2025-04-09-introducing-bloom-filters.md
+++ b/content/blog/2025-04-09-introducing-bloom-filters.md
@@ -5,6 +5,7 @@ date= 2025-04-09 01:01:01
 authors= [ "karthiksubbarao"]
 
 [extra]
+type = "Announcements"
 featured = false
 featured_image = "/assets/media/featured/random-02.webp"
 +++
diff --git a/content/blog/2025-04-27-valkey-modules-rust-sdk-updates.md b/content/blog/2025-04-27-valkey-modules-rust-sdk-updates.md
index ff9edfdd..8181e375 100644
--- a/content/blog/2025-04-27-valkey-modules-rust-sdk-updates.md
+++ b/content/blog/2025-04-27-valkey-modules-rust-sdk-updates.md
@@ -6,6 +6,7 @@ authors= ["dmitrypol"]
 categories= "modules"
 
 [extra]
+type = "How-to"
 categories= "performance"
 featured = false
 featured_image = "/assets/media/featured/random-04.webp"
diff --git a/content/blog/2025-05-14-upgrade-stories-vol1.md b/content/blog/2025-05-14-upgrade-stories-vol1.md
index 58b56232..f483dfeb 100644
--- a/content/blog/2025-05-14-upgrade-stories-vol1.md
+++ b/content/blog/2025-05-14-upgrade-stories-vol1.md
@@ -5,6 +5,7 @@ date= 2025-05-14 01:01:01
 authors= ["kyledvs", "nigel"] 
 
 [extra]
+type = "Community Highlight"
 categories= "update"
 featured = false
 featured_image = "/assets/media/featured/random-04.webp"
diff --git a/content/blog/2025-05-21-performance-optimization-methodology-for-valkey/index.md b/content/blog/2025-05-21-performance-optimization-methodology-for-valkey/index.md
index 7f48cc59..7918bf71 100644
--- a/content/blog/2025-05-21-performance-optimization-methodology-for-valkey/index.md
+++ b/content/blog/2025-05-21-performance-optimization-methodology-for-valkey/index.md
@@ -5,6 +5,7 @@ description= "Performance Optimization Methodology for Valkey"
 authors= ["lipzhu", "guowangy"]
 
 [extra]
+type = "Technical Deep Dive"
 categories= "performance"
 featured = true
 featured_image = "/assets/media/featured/random-04.webp"
diff --git a/content/blog/2025-06-13-introducing-valkey-search/index.md b/content/blog/2025-06-13-introducing-valkey-search/index.md
index 43d52ce9..8005eae7 100644
--- a/content/blog/2025-06-13-introducing-valkey-search/index.md
+++ b/content/blog/2025-06-13-introducing-valkey-search/index.md
@@ -5,6 +5,7 @@ date= 2025-07-07 01:01:01
 authors= ["yairgott", "allenss"] 
 
 [extra]
+type = "Announcements"
 featured = false
 featured_image = "/assets/media/featured/random-08.webp"
 +++
diff --git a/content/blog/2025-06-23-valkey-bundle-one-stop-shop-for-low-latency-modern-applications/index.md b/content/blog/2025-06-23-valkey-bundle-one-stop-shop-for-low-latency-modern-applications/index.md
index 1fd8fd25..ce7176f5 100644
--- a/content/blog/2025-06-23-valkey-bundle-one-stop-shop-for-low-latency-modern-applications/index.md
+++ b/content/blog/2025-06-23-valkey-bundle-one-stop-shop-for-low-latency-modern-applications/index.md
@@ -5,6 +5,7 @@ date= 2025-06-23 00:00:01
 authors= ["rlunar"] 
 
 [extra]
+type = "Announcements"
 featured = false
 featured_image = "/assets/media/featured/random-02.webp"
 +++
diff --git a/content/blog/2025-06-30-introducing-enhanced-json-capabilities-in-valkey.md b/content/blog/2025-06-30-introducing-enhanced-json-capabilities-in-valkey.md
index 49c055e6..b57e9c78 100644
--- a/content/blog/2025-06-30-introducing-enhanced-json-capabilities-in-valkey.md
+++ b/content/blog/2025-06-30-introducing-enhanced-json-capabilities-in-valkey.md
@@ -3,6 +3,8 @@ title = "Introducing Enhanced JSON Capabilities in Valkey"
 description = "Learn about the new Valkey JSON with some real-world examples and application."
 date = "2025-06-30 01:01:01"
 authors= [ "roshkhatri"]
+[extra]
+type = "Announcements"
 +++
 
 JSON is a ubiquitous format for semi-structured data, and developers often expect native support across their technology stack, including in-memory stores like Valkey. Previously, working with JSON in Valkey required serializing entire objects as strings or flattening them into Hashes, limiting nesting. These workarounds added complexity and made updates harder than they should be.
diff --git a/content/blog/2025-07-10-keyspace-save-the-date.md b/content/blog/2025-07-10-keyspace-save-the-date.md
index ccbcb61c..c8b76fee 100644
--- a/content/blog/2025-07-10-keyspace-save-the-date.md
+++ b/content/blog/2025-07-10-keyspace-save-the-date.md
@@ -4,6 +4,7 @@ description = "Join the Valkey crew on August 28, 2025 in Amsterdam for the firs
 date = "2025-07-10 01:01:01"
 authors= [ "kyledvs"]
 [extra]
+type = "Events"
 featured = true
 featured_image = "/assets/media/featured/random-05.webp"
 +++
diff --git a/content/blog/2025-07-21-valkey-on-k0rdent.md b/content/blog/2025-07-21-valkey-on-k0rdent.md
index 652dd441..a0a601a8 100644
--- a/content/blog/2025-07-21-valkey-on-k0rdent.md
+++ b/content/blog/2025-07-21-valkey-on-k0rdent.md
@@ -3,6 +3,8 @@ title = "Using k0rdent MultiClusterService Template for Valkey on Kubernetes"
 description = "Learn how to deploy and manage Valkey across multiple Kubernetes clusters using k0rdent's template-driven approach for simplified multi-cluster application delivery."
 date = "2025-07-21 01:01:42"
 authors= [ "s3rj1k"]
+[extra]
+type = "Community Highlight"
 +++
 
 Managing distributed applications across multiple Kubernetes clusters can be a complex and time-consuming process. This guide demonstrates how to streamline Valkey deployment using [k0rdent's](https://k0rdent.io/) `MultiClusterService` template, providing a practical example of modern multi-cluster application delivery.
diff --git a/content/blog/2025-08-04-valkey-swift/index.md b/content/blog/2025-08-04-valkey-swift/index.md
index 4768bad4..f912d903 100644
--- a/content/blog/2025-08-04-valkey-swift/index.md
+++ b/content/blog/2025-08-04-valkey-swift/index.md
@@ -5,6 +5,7 @@ date= 2025-08-04 00:00:00
 authors= [ "adamfowler"]
 
 [extra]
+type = "Announcements"
 featured = true
 featured_image = "/assets/media/featured/random-04.webp"
 +++
diff --git a/content/blog/2025-08-12-keyspace-schedule.md b/content/blog/2025-08-12-keyspace-schedule.md
index d867a6c5..60f7c4de 100644
--- a/content/blog/2025-08-12-keyspace-schedule.md
+++ b/content/blog/2025-08-12-keyspace-schedule.md
@@ -3,6 +3,8 @@ title = "What you'll see at Keyspace 2025"
 description = "I sat on the selection committee for Keyspace and was floored by the number and quality of submissions we received. It's truly amazing to see the talent and depth of experience that is present in the Valkey community manifest in a slate of presentations aimed to interest both experienced and new Valkey users."
 date = "2025-08-12 01:01:01"
 authors= [ "kyledvs" ]
+[extra]
+type = "Events"
 +++
 
 <style type="text/css">
diff --git a/content/blog/2025-09-11-valkey-investment-in-open-source.md b/content/blog/2025-09-11-valkey-investment-in-open-source.md
index 0f6ae8bf..c99b6541 100644
--- a/content/blog/2025-09-11-valkey-investment-in-open-source.md
+++ b/content/blog/2025-09-11-valkey-investment-in-open-source.md
@@ -5,6 +5,7 @@ date= 2025-09-16 00:00:00
 authors= [ "lorilorusso"]
 
 [extra]
+type = "Community Highlight"
 featured = true
 featured_image = "/assets/media/featured/random-04.webp"
 +++
diff --git a/content/blog/2025-09-25-numbered-databases/index.md b/content/blog/2025-09-25-numbered-databases/index.md
index e4be79e8..0450bb51 100644
--- a/content/blog/2025-09-25-numbered-databases/index.md
+++ b/content/blog/2025-09-25-numbered-databases/index.md
@@ -5,6 +5,7 @@ date= 2025-09-25 00:00:00
 authors= [ "kyledvs"]
 
 [extra]
+type = "Technical Deep Dive"
 featured = true
 featured_image = "/blog/numbered-databases/images/move-db.drawio.png"
 +++
diff --git a/content/blog/2025-09-30-hash-fields-expiration/index.md b/content/blog/2025-09-30-hash-fields-expiration/index.md
index f5c825cb..efb6c6ed 100644
--- a/content/blog/2025-09-30-hash-fields-expiration/index.md
+++ b/content/blog/2025-09-30-hash-fields-expiration/index.md
@@ -11,6 +11,7 @@ description= """
     """
 authors= ["ranshid"]
 [extra]
+type = "Technical Deep Dive"
 featured = true
 featured_image = "/assets/media/featured/random-08.webp"
 +++
diff --git a/content/blog/2025-10-02-50-million-zsets/index.md b/content/blog/2025-10-02-50-million-zsets/index.md
index 5fc5999c..b9278d85 100644
--- a/content/blog/2025-10-02-50-million-zsets/index.md
+++ b/content/blog/2025-10-02-50-million-zsets/index.md
@@ -6,6 +6,7 @@ The latest Valkey 8.1 release introduces a redesigned hash table and other opti
     """
 authors= ["khawaja"]
 [extra]
+type = "Technical Deep Dive"
 featured = true
 featured_image = "/blog/50-million-zsets/hero-used-memory-vs-inserts-new.png"
 +++
diff --git a/content/blog/2025-10-15-properly-secure-your-valkey-deployment.md b/content/blog/2025-10-15-properly-secure-your-valkey-deployment.md
index ec97eae3..1fdf4cde 100644
--- a/content/blog/2025-10-15-properly-secure-your-valkey-deployment.md
+++ b/content/blog/2025-10-15-properly-secure-your-valkey-deployment.md
@@ -5,6 +5,7 @@ date= 2025-12-12 00:00:00
 authors= [ "allenhelton"]
 
 [extra]
+type = "How-to"
 featured = true
 featured_image = "/assets/media/featured/random-06.webp"
 +++
diff --git a/content/blog/2025-10-20-1-billion-rps/index.md b/content/blog/2025-10-20-1-billion-rps/index.md
index c3e5141b..939010ec 100644
--- a/content/blog/2025-10-20-1-billion-rps/index.md
+++ b/content/blog/2025-10-20-1-billion-rps/index.md
@@ -4,6 +4,7 @@ date= 2025-10-20 00:00:00
 description= "Learn about the internal workings of cluster mode and the improvements which helped to scale to 1 Billion RPS with the latest Valkey 9.0 release"
 authors= ["hpatro", "maheshcherukumilli", "sarthakaggarwal97", "sungming2"]
 [extra]
+type = "Technical Deep Dive"
 featured = true
 featured_image = "/assets/media/featured/random-04.webp"
 +++
diff --git a/content/blog/2025-10-21-introducing-valkey-9/index.md b/content/blog/2025-10-21-introducing-valkey-9/index.md
index c493bd5f..3103eed3 100644
--- a/content/blog/2025-10-21-introducing-valkey-9/index.md
+++ b/content/blog/2025-10-21-introducing-valkey-9/index.md
@@ -7,6 +7,7 @@ Read on to find out all the team packed into this release.
     """
 authors= ["kyledvs"]
 [extra]
+type = "Announcements"
 featured = true
 featured_image = "/assets/media/featured/random-08.webp"
 +++
diff --git a/content/blog/2025-10-27-atomic-slot-migration/index.md b/content/blog/2025-10-27-atomic-slot-migration/index.md
index 77e31e43..6073c9e0 100644
--- a/content/blog/2025-10-27-atomic-slot-migration/index.md
+++ b/content/blog/2025-10-27-atomic-slot-migration/index.md
@@ -4,6 +4,7 @@ date= 2025-10-29 00:00:00
 description= "Valkey 9.0 brings big changes and improvements to the underlying mechanisms of resharding. Find out how Atomic Slot Migration works, the benefits it brings, and the headaches it eliminates."
 authors= ["murphyjacob4"]
 [extra]
+type = "Technical Deep Dive"
 featured = true
 featured_image = "/assets/media/featured/random-05.webp"
 +++
diff --git a/content/blog/2026-01-06-valkey-helm-chart/index.md b/content/blog/2026-01-06-valkey-helm-chart/index.md
index 20d5f5cc..8679695d 100644
--- a/content/blog/2026-01-06-valkey-helm-chart/index.md
+++ b/content/blog/2026-01-06-valkey-helm-chart/index.md
@@ -4,6 +4,7 @@ date = 2026-01-06
 description = "A guide on why the new Valkey Helm chart exists, how it helps, and how to migrate from Bitnami." 
 authors =  ["sgissi","maheshcherukumilli"]
 [extra]
+type = "Announcements"
 featured = true
 featured_image = "/assets/media/featured/valkey-helm.webp"
 +++
diff --git a/content/blog/2026-01-22-2025-year-end/index.md b/content/blog/2026-01-22-2025-year-end/index.md
index 9581ea3e..affa938b 100644
--- a/content/blog/2026-01-22-2025-year-end/index.md
+++ b/content/blog/2026-01-22-2025-year-end/index.md
@@ -3,6 +3,8 @@ title = "Valkey 2025 Year-End Review: Reflecting on Progress and Looking Ahead"
 date = 2026-01-22
 description = "This past year marked an important chapter for Valkey, one defined by growth, collaboration, and a shared commitment to building an open, high-performance key-value store for everyone."
 authors= ["kyledvs", "crystalpham"]
+[extra]
+type = "Community Highlight"
 +++
 
 As we kick off 2026, it’s a great moment to pause, reflect, and celebrate what the Valkey project and its community has accomplished together. This past year marked an important chapter for Valkey, one defined by growth, collaboration, and a shared commitment to building an open, high-performance key-value store for everyone.
diff --git a/content/blog/2026-02-04-valkey-newsletter-new/index.md b/content/blog/2026-02-04-valkey-newsletter-new/index.md
index dbdf189d..5b8bdf5c 100644
--- a/content/blog/2026-02-04-valkey-newsletter-new/index.md
+++ b/content/blog/2026-02-04-valkey-newsletter-new/index.md
@@ -3,6 +3,8 @@ title = "Don’t Miss What’s Next in Valkey — Subscribe Now"
 date = 2026-02-04
 description = "Valkey is moving fast — and the easiest way to stay ahead is to subscribe to the official Valkey newsletter. From new releases and roadmap milestones to community highlights and upcoming events, our newsletter is your single source of truth for everything happening across the Valkey ecosystem."
 authors = ["crystalpham"]
+[extra]
+type = "Announcements"
 +++
 
 Valkey is moving fast and the easiest way to stay ahead is to [subscribe](/blog/valkey-newsletter-new/#email-signup) to the official Valkey newsletter.
diff --git a/content/blog/2026-02-19-operational-lessons/index.md b/content/blog/2026-02-19-operational-lessons/index.md
index c8bfa6d0..87d20bea 100644
--- a/content/blog/2026-02-19-operational-lessons/index.md
+++ b/content/blog/2026-02-19-operational-lessons/index.md
@@ -5,6 +5,7 @@ description = "Production lessons on tail latency, migrations, and scale from en
 authors = ["allenhelton", "mikecallahan"]
 
 [extra]
+type = "Community Highlight"
 featured = true
 featured_image = "/assets/media/featured/random-04.webp"
 +++
diff --git a/content/blog/2026-02-25-introducing-valkey-admin/index.md b/content/blog/2026-02-25-introducing-valkey-admin/index.md
index c9b08308..ebad06a6 100644
--- a/content/blog/2026-02-25-introducing-valkey-admin/index.md
+++ b/content/blog/2026-02-25-introducing-valkey-admin/index.md
@@ -5,6 +5,7 @@ date= 2026-02-25 00:00:00
 authors= [ "allenhelton", "arsenykostenko"]
 
 [extra]
+type = "Community Highlight"
 featured = true
 featured_image = "/blog/introducing-valkey-admin/images/hero.jpg"
 +++
diff --git a/content/blog/2026-03-10-valkey-search-1_2/index.md b/content/blog/2026-03-10-valkey-search-1_2/index.md
index 442e6e93..95e80341 100644
--- a/content/blog/2026-03-10-valkey-search-1_2/index.md
+++ b/content/blog/2026-03-10-valkey-search-1_2/index.md
@@ -3,6 +3,8 @@ title= "Beyond Vectors: Introducing Full-Text Search and Aggregations to Valkey"
 description = "Valkey Search now enables querying across text, tags, and numeric data and analyzing results with aggregations."
 date= 2026-03-17 01:00:00
 authors= [ "karthiksubbarao", "allenss", "bcathcart", "cnuthalapati"]
+[extra]
+type = "Announcements"
 +++
 
 Valkey Search now lets you search across text, tag, numeric, and vector attributes in a single query, and analyze results with server-side aggregations at the low latency you expect from Valkey. 
diff --git a/content/blog/2026-03-17-spring-data-valkey/index.md b/content/blog/2026-03-17-spring-data-valkey/index.md
index 121109b1..7ed2d54d 100644
--- a/content/blog/2026-03-17-spring-data-valkey/index.md
+++ b/content/blog/2026-03-17-spring-data-valkey/index.md
@@ -5,6 +5,7 @@ date= 2026-04-01 00:00:00
 authors= ["makubo-aws"]
 
 [extra]
+type = "Announcements"
 featured = false
 +++
 
diff --git a/content/blog/2026-03-27-valkey-tooling-primitives.md b/content/blog/2026-03-27-valkey-tooling-primitives.md
index a4857d40..c52a5c75 100644
--- a/content/blog/2026-03-27-valkey-tooling-primitives.md
+++ b/content/blog/2026-03-27-valkey-tooling-primitives.md
@@ -5,6 +5,7 @@ description = "Valkey ships powerful operational primitives like COMMANDLOG, SLO
 authors = ["kivanow"]
 
 [extra]
+type = "Technical Deep Dive"
 featured = true
 featured_image = "/assets/media/featured/random-04.webp"
 +++
diff --git a/content/blog/README b/content/blog/README
new file mode 100644
index 00000000..f1c7813e
--- /dev/null
+++ b/content/blog/README
@@ -0,0 +1,54 @@
+# Valkey Blog
+
+Blog posts live in `content/blog/`. Each post is either a standalone `.md` file or a directory with an `index.md` (use a directory when you need to include images or other assets).
+
+## Front matter
+
+Every blog post must include TOML front matter between `+++` fences. Example:
+
+```toml
++++
+title = "Your Post Title"
+date = 2026-04-01
+description = "A short summary shown in the blog listing and metadata."
+authors = ["github-username"]
+
+[extra]
+type = "Announcements"
+featured = false
+featured_image = "/assets/media/featured/random-01.webp"
++++
+```
+
+### Required fields
+
+| Field | Description |
+|-------|-------------|
+| `title` | Post title |
+| `date` | Publish date (`YYYY-MM-DD`) |
+| `description` | Short summary for the blog listing |
+| `authors` | List of GitHub usernames |
+| `extra.type` | Post category (see below). **The build will fail if this is missing.** |
+
+### Optional fields
+
+| Field | Description |
+|-------|-------------|
+| `extra.featured` | Set to `true` to show in the Featured sidebar |
+| `extra.featured_image` | Path to a featured image |
+
+### Post types
+
+Valid types are defined in `config.toml` under `[extra.blog_types]`. Current values:
+
+- `Technical Deep Dive` — In-depth technical content
+- `Announcements` — Releases, new features, project news
+- `Community Highlight` — Community stories, year-in-review, ecosystem
+- `How-to` — Tutorials and guides
+- `Events` — Event recaps and announcements
+
+To add a new type, update `config.toml`, `sass/_valkey.scss` (badge color), and this README.
+
+## File naming
+
+Use the format `YYYY-MM-DD-slug.md` (or `YYYY-MM-DD-slug/index.md` for posts with assets).
diff --git a/content/blog/whats-new-june-2024.md b/content/blog/whats-new-june-2024.md
index 1d226e54..dcaffef4 100644
--- a/content/blog/whats-new-june-2024.md
+++ b/content/blog/whats-new-june-2024.md
@@ -5,6 +5,7 @@ description= "What have people been saying about Valkey in June 2024? Read on to
 authors= ["kyledvs"]
 
 [extra]
+type = "Community Highlight"
 categories= "roundup"
 featured = false
 featured_image = "/assets/media/featured/random-01.webp"
diff --git a/sass/_valkey.scss b/sass/_valkey.scss
index 97dd00bf..106e815f 100644
--- a/sass/_valkey.scss
+++ b/sass/_valkey.scss
@@ -911,6 +911,47 @@ aside {
   }
 }
 
+.blog-type-filters {
+  margin-bottom: 1.5rem;
+
+  select {
+    padding: 0.4rem 1rem;
+    border-radius: 8px;
+    border: 1px solid $grey-lt-300;
+    background: $white;
+    color: $text;
+    font-size: 1.3rem;
+    cursor: pointer;
+  }
+}
+
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  border: 0;
+}
+
+.blog-type-badge {
+  display: inline-block;
+  padding: 0.15rem 0.7rem;
+  border-radius: 12px;
+  font-size: 1.1rem;
+  font-weight: 600;
+  color: $white;
+  background-color: $grey-dk-200;
+
+  &[data-type="Technical Deep Dive"] { background-color: $blue-200; }
+  &[data-type="Community Highlight"] { background-color: $green-200; }
+  &[data-type="How-to"] { background-color: $purple-200; }
+  &[data-type="Announcements"] { background-color: $yellow-100; }
+  &[data-type="Events"] { background-color: $red-100; }
+}
+
 .blog-inner {
   
   .blog-list {
diff --git a/templates/blog-page.html b/templates/blog-page.html
index 5c16477a..655f0491 100644
--- a/templates/blog-page.html
+++ b/templates/blog-page.html
@@ -7,6 +7,7 @@
 
 <h1>{{ page.title }}</h1>
 <div class="meta">
+  <span class="blog-type-badge" data-type="{{ page.extra.type }}">{{ page.extra.type }}</span>
   {{ page.date | date(format="%Y-%m-%d") }}
   {% if page.authors %}
     &middot; 
diff --git a/templates/blog.html b/templates/blog.html
index 04e2d933..1227397c 100644
--- a/templates/blog.html
+++ b/templates/blog.html
@@ -8,15 +8,68 @@ <h1>{{ section.title }}</h1>
 {% endblock subhead_content %}
 
 {% block main_content %}
+  {% set blog_types = config.extra.blog_types.types %}
+  <div class="blog-type-filters" hidden>
+    <label for="blog-type-select" class="sr-only">Filter posts by type</label>
+    <select id="blog-type-select">
+      <option value="All">All Types</option>
+      {% for t in blog_types %}
+      <option value="{{ t }}">{{ t }}</option>
+      {% endfor %}
+    </select>
+  </div>
   <ul class="blog-list">
     {% for page in section.pages %}
-    <li>
+    {% if not page.extra.type %}
+      {{ throw(message="Blog post '" ~ page.title ~ "' is missing [extra] type. Add type to its front matter.") }}
+    {% elif blog_types is not containing(page.extra.type) %}
+      {{ throw(message="Blog post '" ~ page.title ~ "' has invalid type '" ~ page.extra.type ~ "'. Check config.toml for valid types.") }}
+    {% endif %}
+    <li data-type="{{ page.extra.type }}">
+      <span class="blog-type-badge" data-type="{{ page.extra.type }}">{{ page.extra.type }}</span>
       <h2><a href="{{ page.permalink }}">{{ page.title  | safe}}</a></h2>
       <div class="meta">{{ page.date | date(format="%A %B %e, %Y") }}</div>
       <p>{{ page.description | safe }}</p>
     </li>
     {% endfor %}
   </ul>
+  <p class="blog-no-results" hidden>No posts match this filter.</p>
+  <script>
+  (function() {
+    const select = document.getElementById('blog-type-select');
+    const items = document.querySelectorAll('.blog-list li');
+    const noResults = document.querySelector('.blog-no-results');
+    const filters = select.closest('.blog-type-filters');
+
+    filters.hidden = false;
+
+    function applyFilter(type) {
+      let visible = 0;
+      items.forEach(function(li) {
+        const show = type === 'All' || li.getAttribute('data-type') === type;
+        li.hidden = !show;
+        if (show) visible++;
+      });
+      noResults.hidden = visible > 0;
+    }
+
+    // Sync filter with URL hash on load
+    const initial = decodeURIComponent(location.hash.slice(1));
+    if (initial) {
+      const option = Array.from(select.options).find(function(o) { return o.value === initial; });
+      if (option) {
+        select.value = initial;
+        applyFilter(initial);
+      }
+    }
+
+    select.addEventListener('change', function() {
+      const type = select.value;
+      applyFilter(type);
+      history.replaceState(null, '', type === 'All' ? location.pathname : '#' + encodeURIComponent(type));
+    });
+  })();
+  </script>
 {% endblock main_content %}
 
 {% block related_content %}

From ee9374d4e51d4f1e0347abb72ef780102e7df988 Mon Sep 17 00:00:00 2001
From: Madelyn Olson <madelyneolson@gmail.com>
Date: Thu, 2 Apr 2026 15:11:17 -0700
Subject: [PATCH 2/7] Use consistent brand colors for blog type badges

Signed-off-by: Madelyn Olson <madelyneolson@gmail.com>
---
 sass/_valkey.scss | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sass/_valkey.scss b/sass/_valkey.scss
index 106e815f..068e34b9 100644
--- a/sass/_valkey.scss
+++ b/sass/_valkey.scss
@@ -945,7 +945,7 @@ aside {
   color: $white;
   background-color: $grey-dk-200;
 
-  &[data-type="Technical Deep Dive"] { background-color: $blue-200; }
+  &[data-type="Technical Deep Dive"] { background-color: #6983ff; }
   &[data-type="Community Highlight"] { background-color: $green-200; }
   &[data-type="How-to"] { background-color: $purple-200; }
   &[data-type="Announcements"] { background-color: $yellow-100; }

From 0110bd7729e0b4553de3206e44e674a5e3497403 Mon Sep 17 00:00:00 2001
From: Madelyn Olson <madelyneolson@gmail.com>
Date: Tue, 7 Apr 2026 08:28:11 -0700
Subject: [PATCH 3/7] Refactor blog post types to use Zola built-in taxonomies

- Replace custom [extra] type with [taxonomies] blog_type
- Add blog_type taxonomy to config.toml (render = false)
- Update blog.html to use get_taxonomy() for filter dropdown
- Update blog-page.html to read from page.taxonomies.blog_type
- Remove [extra.blog_types] config and custom build-time validation
- Wrap decodeURIComponent in try/catch
- Preserve query params when clearing filter

Signed-off-by: Madelyn Olson <madelyneolson@gmail.com>
---
 config.toml                                   |   7 +-
 content/blog/2024-04-12-hello-world.md        |   3 +-
 content/blog/2024-04-16-valkey-7-2-5-out.md   |   3 +-
 content/blog/2024-04-26-modules-101.md        |   3 +-
 content/blog/2024-05-24-may-roundup.md        |   3 +-
 .../index.md                                  |   3 +-
 .../blog/2024-07-07-unlock-one-million-rps.md |   3 +-
 content/blog/2024-07-31-valkey-8-0-0-rc1.md   |   3 +-
 ...2024-08-29-valkey-memory-efficiency-8-0.md |   3 +-
 ...2024-09-13-unlock-one-million-rps-part2.md |   3 +-
 content/blog/2024-09-16-valkey-8-ga.md        |   3 +-
 .../2024-11-21-testing-the-limits/index.md    |   3 +-
 .../2024-12-20-2024-year-of-valkey/index.md   |   3 +-
 .../blog/2024-12-22-az-affinity-strategy.md   |   3 +-
 .../blog/2025-03-28-new-hash-table/index.md   |   3 +-
 .../2025-03-4-go-client-in-public-preview.md  |   3 +-
 content/blog/2025-04-02-valkey-8-1-0-ga.md    |   3 +-
 .../2025-04-09-introducing-bloom-filters.md   |   3 +-
 ...5-04-27-valkey-modules-rust-sdk-updates.md |   3 +-
 .../blog/2025-05-14-upgrade-stories-vol1.md   |   3 +-
 .../index.md                                  |   3 +-
 .../index.md                                  | 287 +++++++++---------
 .../index.md                                  |   3 +-
 ...ng-enhanced-json-capabilities-in-valkey.md |   4 +-
 .../blog/2025-07-10-keyspace-save-the-date.md |   3 +-
 content/blog/2025-07-21-valkey-on-k0rdent.md  |   4 +-
 content/blog/2025-08-04-valkey-swift/index.md |   3 +-
 content/blog/2025-08-12-keyspace-schedule.md  |   4 +-
 ...-09-11-valkey-investment-in-open-source.md |   3 +-
 .../2025-09-25-numbered-databases/index.md    |   3 +-
 .../index.md                                  |   3 +-
 .../blog/2025-10-02-50-million-zsets/index.md |   3 +-
 ...-properly-secure-your-valkey-deployment.md |   3 +-
 .../blog/2025-10-20-1-billion-rps/index.md    |   3 +-
 .../2025-10-21-introducing-valkey-9/index.md  |   3 +-
 .../2025-10-27-atomic-slot-migration/index.md |   3 +-
 .../2026-01-06-valkey-helm-chart/index.md     |   3 +-
 .../blog/2026-01-22-2025-year-end/index.md    |   4 +-
 .../2026-02-04-valkey-newsletter-new/index.md |   4 +-
 .../2026-02-19-operational-lessons/index.md   |   3 +-
 .../index.md                                  |   3 +-
 .../2026-03-10-valkey-search-1_2/index.md     |   4 +-
 .../2026-03-17-spring-data-valkey/index.md    |   3 +-
 .../2026-03-27-valkey-tooling-primitives.md   |   3 +-
 content/blog/README                           |  10 +-
 content/blog/whats-new-june-2024.md           |   3 +-
 content/events/kcd-guadalajara-2026.md        |  10 +
 .../opensearch-valkey-bay-area-april-2026.md  |  10 +
 content/events/oss-na-2026.md                 |  10 +
 content/events/percona-live-2026-bay-area.md  |  10 +
 templates/blog-page.html                      |   6 +-
 templates/blog.html                           |  54 ++--
 52 files changed, 312 insertions(+), 227 deletions(-)
 create mode 100644 content/events/kcd-guadalajara-2026.md
 create mode 100644 content/events/opensearch-valkey-bay-area-april-2026.md
 create mode 100644 content/events/oss-na-2026.md
 create mode 100644 content/events/percona-live-2026-bay-area.md

diff --git a/config.toml b/config.toml
index 2656f25c..7b394410 100644
--- a/config.toml
+++ b/config.toml
@@ -7,6 +7,10 @@ build_search_index = true
 
 generate_feeds = true
 
+taxonomies = [
+    { name = "blog_type", render = false },
+]
+
 [markdown.highlighting]
 theme = "github-dark-default"
 
@@ -54,5 +58,4 @@ publish_hold = [
 ]
 
 
-[extra.blog_types]
-types = ["Technical Deep Dive", "Community Highlight", "How-to", "Announcements", "Events"]
+
diff --git a/content/blog/2024-04-12-hello-world.md b/content/blog/2024-04-12-hello-world.md
index 19ac947e..7d1ba693 100644
--- a/content/blog/2024-04-12-hello-world.md
+++ b/content/blog/2024-04-12-hello-world.md
@@ -7,8 +7,9 @@ aliases = [
 description= "Welcome! For the inaugural blog post on valkey.io, I’d like to recap the story so far, what to look forward to, and then describe how this blog works."
 authors= ["kyledvs"]
 
+[taxonomies]
+blog_type = ["Announcements"]
 [extra]
-type = "Announcements"
 categories= "update"
 featured = false
 featured_image = "/assets/media/featured/random-05.webp"
diff --git a/content/blog/2024-04-16-valkey-7-2-5-out.md b/content/blog/2024-04-16-valkey-7-2-5-out.md
index ce771309..ddcfcda3 100644
--- a/content/blog/2024-04-16-valkey-7-2-5-out.md
+++ b/content/blog/2024-04-16-valkey-7-2-5-out.md
@@ -7,8 +7,9 @@ aliases= [
 description= "Exciting times!I'm pleased to announce that you can start using the first generally available, stable Valkey release today."
 authors= ["kyledvs"]
 
+[taxonomies]
+blog_type = ["Announcements"]
 [extra]
-type = "Announcements"
 categories= "update"
 featured = false
 featured_image = "/assets/media/featured/random-04.webp"
diff --git a/content/blog/2024-04-26-modules-101.md b/content/blog/2024-04-26-modules-101.md
index b5e6e81c..02ba202b 100644
--- a/content/blog/2024-04-26-modules-101.md
+++ b/content/blog/2024-04-26-modules-101.md
@@ -7,8 +7,9 @@ aliases= [
 description= "The idea of modules is to allow adding extra features (such as new commands and data types) to Valkey without making changes to the core code."
 authors= ["dmitrypol"]
 
+[taxonomies]
+blog_type = ["How-to"]
 [extra]
-type = "How-to"
 categories= "modules"
 featured = false
 featured_image = "/assets/media/featured/random-03.webp"
diff --git a/content/blog/2024-05-24-may-roundup.md b/content/blog/2024-05-24-may-roundup.md
index 8c1af7c4..95534edc 100644
--- a/content/blog/2024-05-24-may-roundup.md
+++ b/content/blog/2024-05-24-may-roundup.md
@@ -7,8 +7,9 @@ aliases= [
 description= "It's become clear that people want to talk about Valkey and have been publishing blog posts/articles fervently. Here you'll find a collection of all the post I'm aware of in the last few weeks."
 authors= ["kyledvs"]
 
+[taxonomies]
+blog_type = ["Community Highlight"]
 [extra]
-type = "Community Highlight"
 categories= "update"
 featured = false
 featured_image = "/assets/media/featured/random-02.webp"
diff --git a/content/blog/2024-06-27-using-bitnami-valkey-chart/index.md b/content/blog/2024-06-27-using-bitnami-valkey-chart/index.md
index 9aba7b61..c3051ca6 100644
--- a/content/blog/2024-06-27-using-bitnami-valkey-chart/index.md
+++ b/content/blog/2024-06-27-using-bitnami-valkey-chart/index.md
@@ -6,8 +6,9 @@ aliases= [
     "/blog/2024/06/using-bitnami-valkey-chart/"
 ]
 authors=["rafariossaa"]
+[taxonomies]
+blog_type = ["How-to"]
 [extra]
-type = "How-to"
 featured = false
 featured_image = "/assets/media/featured/random-01.webp"
 
diff --git a/content/blog/2024-07-07-unlock-one-million-rps.md b/content/blog/2024-07-07-unlock-one-million-rps.md
index 397ee529..c4b7ae0a 100644
--- a/content/blog/2024-07-07-unlock-one-million-rps.md
+++ b/content/blog/2024-07-07-unlock-one-million-rps.md
@@ -13,8 +13,9 @@ description= "Learn about the new performance improvements in Valkey 8 which red
 # 'authors' are the folks who wrote or contributed to the post.
 # Each author corresponds to a biography file (more info later in this document)
 authors= [ "dantouitou", "uriyagelnik"]
+[taxonomies]
+blog_type = ["Technical Deep Dive"]
 [extra]
-type = "Technical Deep Dive"
 featured = true
 featured_image = "/assets/media/featured/random-01.webp"
 +++
diff --git a/content/blog/2024-07-31-valkey-8-0-0-rc1.md b/content/blog/2024-07-31-valkey-8-0-0-rc1.md
index 3c7290a7..17f672b2 100644
--- a/content/blog/2024-07-31-valkey-8-0-0-rc1.md
+++ b/content/blog/2024-07-31-valkey-8-0-0-rc1.md
@@ -13,8 +13,9 @@ description= "The first release candidate of Valkey 8.0 is now available! Come l
 # 'authors' are the folks who wrote or contributed to the post.
 # Each author corresponds to a biography file (more info later in this document)
 authors= [ "pingxie", "madolson" ]
+[taxonomies]
+blog_type = ["Announcements"]
 [extra]
-type = "Announcements"
 featured = false
 featured_image = "/assets/media/featured/random-07.webp"
 +++
diff --git a/content/blog/2024-08-29-valkey-memory-efficiency-8-0.md b/content/blog/2024-08-29-valkey-memory-efficiency-8-0.md
index 993a398d..13d79db8 100644
--- a/content/blog/2024-08-29-valkey-memory-efficiency-8-0.md
+++ b/content/blog/2024-08-29-valkey-memory-efficiency-8-0.md
@@ -3,8 +3,9 @@ title= "Storing more with less: Memory Efficiency in Valkey 8"
 date= 2024-09-04 01:01:01
 description= "Learn about the new memory efficiency improvements in Valkey 8 which reduces memory overhead, which allows more data to be stored in the same amount of memory."
 authors= [ "hpatro"]
+[taxonomies]
+blog_type = ["Technical Deep Dive"]
 [extra]
-type = "Technical Deep Dive"
 featured = true
 featured_image = "/assets/media/featured/random-05.webp"
 +++
diff --git a/content/blog/2024-09-13-unlock-one-million-rps-part2.md b/content/blog/2024-09-13-unlock-one-million-rps-part2.md
index 0ea58be1..5ce1ae30 100644
--- a/content/blog/2024-09-13-unlock-one-million-rps-part2.md
+++ b/content/blog/2024-09-13-unlock-one-million-rps-part2.md
@@ -13,8 +13,9 @@ description= "Maximize the performance of your hardware with memory access amort
 # 'authors' are the folks who wrote or contributed to the post.
 # Each author corresponds to a biography file (more info later in this document)
 authors= [ "dantouitou", "uriyagelnik"]
+[taxonomies]
+blog_type = ["Technical Deep Dive"]
 [extra]
-type = "Technical Deep Dive"
 featured = false
 featured_image = "/assets/media/featured/random-06.webp"
 +++
diff --git a/content/blog/2024-09-16-valkey-8-ga.md b/content/blog/2024-09-16-valkey-8-ga.md
index 0ebc1366..0e6ca80b 100644
--- a/content/blog/2024-09-16-valkey-8-ga.md
+++ b/content/blog/2024-09-16-valkey-8-ga.md
@@ -3,8 +3,9 @@ title=  "Generally Available: Valkey 8.0.0"
 description = "Today marks a milestone for the Valkey project: the first major release."
 date= 2024-09-16 01:01:01
 authors= ["kyledvs"]
+[taxonomies]
+blog_type = ["Announcements"]
 [extra]
-type = "Announcements"
 featured = false
 featured_image = "/assets/media/featured/random-05.webp"
 
diff --git a/content/blog/2024-11-21-testing-the-limits/index.md b/content/blog/2024-11-21-testing-the-limits/index.md
index 6cb97150..276090f2 100644
--- a/content/blog/2024-11-21-testing-the-limits/index.md
+++ b/content/blog/2024-11-21-testing-the-limits/index.md
@@ -3,8 +3,9 @@ title = "Pushing the limits of Valkey on a Raspberry Pi"
 description = "While most people won't go to production on a Raspberry Pi, we'll cover how to thoroughly performance test Valkey to understand how it works in production."
 date= 2024-11-21 01:01:01
 authors= ["dtaivpp"]
+[taxonomies]
+blog_type = ["Technical Deep Dive"]
 [extra]
-type = "Technical Deep Dive"
 featured = false
 featured_image = "/assets/media/featured/random-07.webp"
 +++
diff --git a/content/blog/2024-12-20-2024-year-of-valkey/index.md b/content/blog/2024-12-20-2024-year-of-valkey/index.md
index 5a9bf175..7b230712 100644
--- a/content/blog/2024-12-20-2024-year-of-valkey/index.md
+++ b/content/blog/2024-12-20-2024-year-of-valkey/index.md
@@ -4,8 +4,9 @@ title=  "2024: The Year of Valkey"
 description = "The end of the calendar year is a great time to reflect, but for Valkey this particular year-end holds special meaning."
 date= 2024-12-20 01:01:01
 authors= ["kyledvs"]
+[taxonomies]
+blog_type = ["Community Highlight"]
 [extra]
-type = "Community Highlight"
 featured = false
 featured_image = "/assets/media/featured/random-05.webp"
 
diff --git a/content/blog/2024-12-22-az-affinity-strategy.md b/content/blog/2024-12-22-az-affinity-strategy.md
index 5cdba6ea..452cc1a4 100644
--- a/content/blog/2024-12-22-az-affinity-strategy.md
+++ b/content/blog/2024-12-22-az-affinity-strategy.md
@@ -3,8 +3,9 @@ title= "Reducing application latency and lowering Cloud bill by setting up your
 date= 2025-01-08 01:01:01
 description= "By implementing AZ affinity routing in Valkey and using GLIDE, you can achieve lower latency and cost savings by routing requests to replicas in the same AZ as the client."
 authors= [ "asafporatstoler", "adarovadya", "muhammadawawdi"]
+[taxonomies]
+blog_type = ["Technical Deep Dive"]
 [extra]
-type = "Technical Deep Dive"
 featured = true
 featured_image = "/assets/media/featured/random-04.webp"
 +++
diff --git a/content/blog/2025-03-28-new-hash-table/index.md b/content/blog/2025-03-28-new-hash-table/index.md
index 760c8135..e961e46b 100644
--- a/content/blog/2025-03-28-new-hash-table/index.md
+++ b/content/blog/2025-03-28-new-hash-table/index.md
@@ -3,8 +3,9 @@ title= "A new hash table"
 date= 2025-03-28 00:00:00
 description= "Designing a state-of-the art hash table"
 authors= ["zuiderkwast"]
+[taxonomies]
+blog_type = ["Technical Deep Dive"]
 [extra]
-type = "Technical Deep Dive"
 featured = false
 featured_image = "/assets/media/featured/random-05.webp"
 
diff --git a/content/blog/2025-03-4-go-client-in-public-preview.md b/content/blog/2025-03-4-go-client-in-public-preview.md
index 5268e8e2..53c611fd 100644
--- a/content/blog/2025-03-4-go-client-in-public-preview.md
+++ b/content/blog/2025-03-4-go-client-in-public-preview.md
@@ -3,8 +3,9 @@ title= "Introducing the Valkey Glide Go Client: Now in Public Preview!"
 description = "Valkey Glide now supports GO. Read to learn more about the new client designed for performance and developer productivity"
 date= 2025-03-04 01:01:01
 authors= [ "niharikabhavaraju"]
+[taxonomies]
+blog_type = ["Announcements"]
 [extra]
-type = "Announcements"
 featured = false
 featured_image = "/assets/media/featured/default.webp"
 +++
diff --git a/content/blog/2025-04-02-valkey-8-1-0-ga.md b/content/blog/2025-04-02-valkey-8-1-0-ga.md
index 12f431b2..573e4519 100644
--- a/content/blog/2025-04-02-valkey-8-1-0-ga.md
+++ b/content/blog/2025-04-02-valkey-8-1-0-ga.md
@@ -13,8 +13,9 @@ description= "Valkey 8.1 is now generally available! Come learn about the exciti
 # 'authors' are the folks who wrote or contributed to the post.
 # Each author corresponds to a biography file (more info later in this document)
 authors= [ "rdias", "mvisser" ]
+[taxonomies]
+blog_type = ["Announcements"]
 [extra]
-type = "Announcements"
 featured = false
 featured_image = "/assets/media/featured/random-03.webp"
 +++
diff --git a/content/blog/2025-04-09-introducing-bloom-filters.md b/content/blog/2025-04-09-introducing-bloom-filters.md
index c4c6b13a..76a6c895 100644
--- a/content/blog/2025-04-09-introducing-bloom-filters.md
+++ b/content/blog/2025-04-09-introducing-bloom-filters.md
@@ -4,8 +4,9 @@ description = "Learn how to use bloom filters to perform large-scale membership
 date= 2025-04-09 01:01:01
 authors= [ "karthiksubbarao"]
 
+[taxonomies]
+blog_type = ["Announcements"]
 [extra]
-type = "Announcements"
 featured = false
 featured_image = "/assets/media/featured/random-02.webp"
 +++
diff --git a/content/blog/2025-04-27-valkey-modules-rust-sdk-updates.md b/content/blog/2025-04-27-valkey-modules-rust-sdk-updates.md
index 8181e375..f3177eef 100644
--- a/content/blog/2025-04-27-valkey-modules-rust-sdk-updates.md
+++ b/content/blog/2025-04-27-valkey-modules-rust-sdk-updates.md
@@ -5,8 +5,9 @@ description= "Extending Valkey using Rust SDK."
 authors= ["dmitrypol"]
 categories= "modules"
 
+[taxonomies]
+blog_type = ["How-to"]
 [extra]
-type = "How-to"
 categories= "performance"
 featured = false
 featured_image = "/assets/media/featured/random-04.webp"
diff --git a/content/blog/2025-05-14-upgrade-stories-vol1.md b/content/blog/2025-05-14-upgrade-stories-vol1.md
index f483dfeb..661d2f4a 100644
--- a/content/blog/2025-05-14-upgrade-stories-vol1.md
+++ b/content/blog/2025-05-14-upgrade-stories-vol1.md
@@ -4,8 +4,9 @@ description = "Two new Valkey users describe what it's really like to upgrade"
 date= 2025-05-14 01:01:01 
 authors= ["kyledvs", "nigel"] 
 
+[taxonomies]
+blog_type = ["Community Highlight"]
 [extra]
-type = "Community Highlight"
 categories= "update"
 featured = false
 featured_image = "/assets/media/featured/random-04.webp"
diff --git a/content/blog/2025-05-21-performance-optimization-methodology-for-valkey/index.md b/content/blog/2025-05-21-performance-optimization-methodology-for-valkey/index.md
index 7918bf71..21d3dc33 100644
--- a/content/blog/2025-05-21-performance-optimization-methodology-for-valkey/index.md
+++ b/content/blog/2025-05-21-performance-optimization-methodology-for-valkey/index.md
@@ -4,8 +4,9 @@ date= 2025-05-27 00:00:00
 description= "Performance Optimization Methodology for Valkey"
 authors= ["lipzhu", "guowangy"]
 
+[taxonomies]
+blog_type = ["Technical Deep Dive"]
 [extra]
-type = "Technical Deep Dive"
 categories= "performance"
 featured = true
 featured_image = "/assets/media/featured/random-04.webp"
diff --git a/content/blog/2025-06-13-introducing-valkey-search/index.md b/content/blog/2025-06-13-introducing-valkey-search/index.md
index 8005eae7..118c9a07 100644
--- a/content/blog/2025-06-13-introducing-valkey-search/index.md
+++ b/content/blog/2025-06-13-introducing-valkey-search/index.md
@@ -1,143 +1,144 @@
-+++
-title= "Introducing Vector Search To Valkey" 
-description = "Learn how to build high-performance, reliable vector similarity search with valkey-search." 
-date= 2025-07-07 01:01:01 
-authors= ["yairgott", "allenss"] 
-
-[extra]
-type = "Announcements"
-featured = false
-featured_image = "/assets/media/featured/random-08.webp"
-+++
-
-[Valkey-search](https://github.com/valkey-io/valkey-search) is an official Valkey module that introduces native vector similarity search capabilities. It allows you to efficiently create indexes and search through billions of vectors stored in your Valkey instances. Whether you're building semantic search, fraud detection systems, or conversational AI experiences, Valkey-Search provides a flexible and high-performance foundation for your application. It is compatible with Valkey versions 8.1.1 and above and is BSD-3-Clause licensed.
-
-
-In this blog, you'll learn how valkey-search works, explore key use cases it supports, understand its architecture and indexing model, and see how to integrate it into your own applications. You'll also gain insight into how it scales, ensures high availability, and supports hybrid queries that combine vector similarity with structured filtering.
-
-
-## Semantic Search
-
-The ability of AI models to extract semantic meaning enables new classes of search algorithms, collectively known as semantic search. An AI model can process an input and convert it into a single high-dimension numeric vector – known as an embedding. Inputs with similar meaning will have similar embeddings. Semantic search is the process of converting a query into its embedding and searching a database of embeddings to find the most similar results. 
-
-The semantic search process can be divided into three phases:
-
-1. **Encode**:  First, convert your input data into searchable units known as chunks. Chunking strategies are data-type specific. For example, with text one well-known chunking algorithm is to make each sentence be a chunk. Then use an AI model to  generate an embedding for each chunk. The specific AI model used for encoding depends on the data type and specific use case. Many AI models are available as a service, such as Google Cloud’s [VertexAI](https://cloud.google.com/vertex-ai) or AWS’ [Bedrock](https://aws.amazon.com/bedrock/), simplifying the embedding generation process.
-
-2. **Index**: Store the generated embeddings along with any associated metadata in valkey-search. Each stored item is indexed with a primary key and a set of attributes spanning multiple modalities, for example, tags (categories),  numbers (pricing), etc.
-* **Search**: The query is converted into an embedding using the same AI model as was used in the **Encode** step above. This embedding is used with the vector search capability of valkey-search to locate the most similar vectors. The located vectors correspond to chunks of the original input that have the most similar meaning to the query.
-
-![Semantic search phases](images/highlevel-flow.png)
-
-## Use-Cases Where valkey-search Shines
-
-Valkey-search’s ability to search billions of vectors with millisecond latencies makes it ideal for real-time applications such as:
-
-* **Personalized Recommendations** – Deliver instant, highly relevant recommendations based on real-time user interactions.
-* **Fraud Detection & Security** – Identify anomalies and suspicious activity with ultra-fast similarity matching.
-* **Conversational AI & Chatbots** – Enhance response accuracy and relevance by leveraging rapid vector-based retrieval.
-* **Image & Video Search** – Enable multimedia search through real-time similarity detection.
-* **GenAI & Semantic Search** – Power advanced AI applications with efficient vector retrieval for natural language understanding.
-
-## Valkey-Search Architecture Overview
-
-Valkey-search allows the creation of any number of named indexes. A valkey-search index can be thought of like a relational database table where a row is a Valkey key and a column is an attribute (field) within that key. Each index is defined to cover a portion of the Valkey keyspace and some list of attributes within those keys. Any mutation of a key within the scope of an index synchronously updates that index before that mutation command is acknowledged. Query operations can be performed on a single index, returning the located key names and optionally their contents.
-
-Indexes can be constructed over hash or JSON keys. For hash keys, the indexable attributes of the keys are just the hash fields. For JSON keys, the indexable attributes are identified using the JSON path notation. Regardless of key type, an index can have any number of attributes. Each attribute of an index is declared with a type and sometimes type-specific modifiers. Currently, three index attribute types are supported: Vector, Numeric and Tag. 
-
-### Index Creation
-
-The [FT.CREATE](https://valkey.io/commands/ft.create/) command is used to create a new, empty index. This causes the system to automatically initiate an asynchronous background process to scan the keyspace for keys that belong in the index in a process called backfilling. The backfill process runs until the entire keyspace has been scanned for the index. The `backfill_in_progress` and `backfill_complete_percent` fields of the [FT.INFO](https://valkey.io/commands/ft.info/) command result can be used to monitor the progress of the backfill. Once the backfill is complete it need not be executed against the index again.
-
-During the backfill process, query and ingestion operations proceed normally. Queries will be executed against the current state of the index. Newly mutated data will be placed into the index as usual and remain unaffected by the backfill process. Thus after the creation of an index – while it is backfilling – the application can be certain that queries will contain the results of all data that has been ingested *after* the creation of the index and *some* of the data ingested *before*. Once the backfill has completed then queries will consider all data covered by the index.
-
-In cluster mode, the `FT.CREATE` command can be sent to any primary node of the cluster and the system will automatically distribute the new index definition to all cluster members. The distribution is done using a combination of the cluster bus and direct gRPC communication between nodes. In the rare case where the distribution machinery detects an inconsistency between nodes, a last-writer-wins (LWW) collision resolution protocol is invoked to enable eventual cluster-wide consistency. 
-
-
-## Vector Search Algorithms
-
-Valkey-search offers K Nearest Neighbor (KNN) search. Applications supply a reference vector and request that the system locate the K vectors which are closest to the reference vector using the a specified distance function (L2, IP or cosine). KNN searching is a classic problem which lacks an efficient exact ideal solution. Valkey-search addresses this problem by providing two different algorithms that the developer can select from:
-
-The first algorithm performs an exhaustive linear search, providing exactly correct answers but with a run-time that may be intolerable on large data sets.
-
-The second algorithm addresses this problem by compromising on accuracy in exchange for shorter run-times. In other words, it runs very fast but may not deliver exactly the correct answer. This type of algorithm is often known as Approximate Nearest Neighbor (ANN). The term "recall" is used to measure the quality of an ANN algorithm result and is expressed as the ratio (or percentage) of the found answers to the correct answers, e.g., a recall of 0.95 (or 95%) means that for a search with K = 100, 95 of the correct answers were found. There is a tradeoff between the recall of an ANN algorithm and its time/space resource consumption.
-
-Valkey-search supports the [Hierarchical Navigable Small Worlds](https://en.wikipedia.org/wiki/Hierarchical_navigable_small_world) (HNSW) ANN algorithm as it provides the best performance at the highest levels of recall demanded by real-time applications. The HNSW algorithm has (`O(log N)`) time complexity and gives the developer three parameters to adjust the CPU and memory consumption vs recall. The relationship between these parameters and the resulting operation latency and recall is complex and data dependent. It is recommended that developers test with data that closely approximates production environments.
-
-
-## Hybrid Query Support
-
-Valkey-search query operations are not limited to just vector searching. Documents can contain additional metadata that can be used to enhance searches. Two types of metadata are currently supported: Numeric and Tag. Numeric metadata supports range queries, i.e., you can include or exclude documents with metadata in the particular range. Tag metadata is an enumerated list of words. Tag searches can be done with an exact word match or a prefix match (trailing wild card).
-
-Hybrid queries are vector query operations which have been augmented with a filter, constructed from the numeric and tag searching operators combined with the usual logical operators `AND`, `OR`, and `NOT.` 
-
-Hybrid queries are particularly powerful for real-world applications, where a mix of vector and non-vector attributes defines the relevance of results. For example, a numeric field could be used as a timestamp, meaning that search operations could be automatically confined to a particular period of time. Another example would be to use a tag field to indicate a language.
-
-
-### Query Execution
-
-When executing hybrid queries, valkey-search automatically selects from one of two strategies as part of the query execution planning phase. This is done by breaking down the query filter into predicates and estimating the selectivity of each predicate to estimate the least expensive execution strategy. 
-
-**Pre-filtering:** This algorithm has two sequential steps: first, a temporary dataset is created using only the documents which pass the filter expression, then a linear KNN similarity search of is performed on the temporary dataset. Pre-filtering is particularly effective for high selectivity queries, i.e., the filter significantly narrows down the dataset.
-
-**Inline-filtering:** In this algorithm, filtering is done *during* the vector search itself. As the HNSW search graph is traversed, each candidate document is tested against the filter criteria before being added to the result set. This method is best suited for cases where the filter isn’t highly selective, i.e., the filter matches a large amount of the dataset. 
-
-
-## High Availability
-
-Valkey-search is built on top of Valkey, leveraging its primary/replica-based architecture to provide high availability (HA). Diagram #2 shows a typical configuration of one primary and three replicas. In the event of a primary node failure, the system automatically promotes one of the replicas to become the new primary.
-
-Clients must send data mutation (write) commands to the primary node which are executed and then automatically asynchronously transmitted to each replica. Clients can send data read operations to any node in the cluster, recognizing that reading from a replica is eventually consistent.
-
-When valkey-search is used, each node, whether a primary or a replica, builds and maintains its own indexes. No additional traffic on the replication channel is generated for index maintenance. Search query operations sent to a replica will be executed against its indexes, reflecting the historical point in time of the data within that node.
-
-![High availability](images/ha.png)
-
-## Scaling
-
-Valkey-search supports all three methods of scaling: horizontal, vertical and replicas. When scaling for capacity, valkey-search behaves just like regular Valkey, i.e., increasing the memory of individual nodes (vertical scaling) or increasing the number of nodes (horizontal scaling) will increase the overall capacity.
-
-However, from a performance perspective, valkey-search behaves very differently from regular Valkey. The multi-threaded implementation of valkey-search means that additional CPUs yield up to linear increases in both query and ingestion throughput. Horizontal scaling yields linear increases in ingestion throughput but may provide little to no benefit on query throughput. If additional query throughput is required, scaling through replicas or additional CPUs is required.
-
-
-![Scaling](images/cluster.png)
-
-
-## Snapshots
-
-Valkey’s point-in-time RDB snapshotting mechanism is a key component for maintaining high availability, zero downtime, and minimal operational overhead. Beyond persistence and loading from disk snapshots, RDB plays a crucial role in full-sync operations, where a primary node synchronizes its in-memory data with a replica node in an HA setup. Full-sync is commonly triggered when a new replica joins the cluster but may also occur if a secondary falls too far behind due to prolonged lag.
-
-Valkey-search enhances the Valkey snapshotting mechanism to include index definitions and vector indexes and is built for resilience and efficiency:
-
-**Fast Turn-up:** New nodes must become fully operational in minimal time. While rebuilding an index on startup is an option, ingesting a large volume of vectors can be prohibitively slow, delaying system readiness. A snapshot captures not only the index metadata but also the vector index content which significantly reduces downtime and operational burden.
-
-**Backward/Forward Compatibility:** Cluster upgrades or downgrades often result in a temporary mix of node versions. valkey-search seamlessly handles such scenarios with a serialization format based on [Protocol Buffers (Protobuf)](https://en.wikipedia.org/wiki/Protocol_Buffers), ensuring both backward and forward compatibility. This simplifies version transitions and reduces the chance of costly re-indexing.
-
-
-## RediSearch API Compatibility
-
-Valkey-search implements a subset of RediSearch’s functionality, with compatibility across key vector search APIs. This enables integration with most existing RediSearch client libraries, allowing you to continue using familiar tools. Developers already experienced with RediSearch can adopt valkey-search with minimal changes, as the vector search API remains largely consistent. 
-
-
-## Performance & Low Latency
-
-Valkey-search was designed from the ground up as an in-memory secondary index. A multi-threaded architecture optimizes query and mutation processing with minimal thread contention, enabling near-linear vertical scalability.
-
-At its core, valkey-search’s threading architecture follows a common design pattern: a worker thread pool combined with task queues. It employs advanced synchronization mechanisms to maintain index consistency while minimizing contention among worker threads. By time-slicing CPU access between read and write operations, the system minimizes locks on the read path, delivering high performance and consistently low search latency. 
-
-Valkey-search’s HNSW implementation is based on the OSS project [HNSWLib](https://github.com/nmslib/hnswlib). While HNSWLib is well-regarded for its speed, we have enhanced its performance and efficiency for our use case. These improvements include better SIMD utilization, promotion of CPU cache efficiency, memory utilization and more.
-
-
-## Future Enhancements
-
-This first release of valkey-search focuses on vector search, but it is designed as a general-purpose indexing subsystem. Future releases will extend both the available data types as well as provide post-query data processing facilities.
-
-
-
-## Get Involved
-
-Valkey-search is open source and ready for you to explore. Whether you're building cutting-edge AI applications or integrating vector search into existing systems, we invite you to try it out. The easiest way to get started is by visiting the [GitHub repository](https://github.com/valkey-io/valkey-search), where you'll find setup instructions, documentation, and examples.
-
-We welcome contributions of all kinds - code, documentation, testing, and feedback. Join the community, file issues, open pull requests, or suggest improvements. Your involvement helps make valkey-search better for everyone.
-
-Ready to dive in? Clone the repo, fire up the [dev container](https://hub.docker.com/r/valkey/valkey-bundle), and start building high-performance vector search with valkey-search.
++++
+title= "Introducing Vector Search To Valkey" 
+description = "Learn how to build high-performance, reliable vector similarity search with valkey-search." 
+date= 2025-07-07 01:01:01 
+authors= ["yairgott", "allenss"] 
+
+[taxonomies]
+blog_type = ["Announcements"]
+[extra]
+featured = false
+featured_image = "/assets/media/featured/random-08.webp"
++++
+
+[Valkey-search](https://github.com/valkey-io/valkey-search) is an official Valkey module that introduces native vector similarity search capabilities. It allows you to efficiently create indexes and search through billions of vectors stored in your Valkey instances. Whether you're building semantic search, fraud detection systems, or conversational AI experiences, Valkey-Search provides a flexible and high-performance foundation for your application. It is compatible with Valkey versions 8.1.1 and above and is BSD-3-Clause licensed.
+
+
+In this blog, you'll learn how valkey-search works, explore key use cases it supports, understand its architecture and indexing model, and see how to integrate it into your own applications. You'll also gain insight into how it scales, ensures high availability, and supports hybrid queries that combine vector similarity with structured filtering.
+
+
+## Semantic Search
+
+The ability of AI models to extract semantic meaning enables new classes of search algorithms, collectively known as semantic search. An AI model can process an input and convert it into a single high-dimension numeric vector – known as an embedding. Inputs with similar meaning will have similar embeddings. Semantic search is the process of converting a query into its embedding and searching a database of embeddings to find the most similar results. 
+
+The semantic search process can be divided into three phases:
+
+1. **Encode**:  First, convert your input data into searchable units known as chunks. Chunking strategies are data-type specific. For example, with text one well-known chunking algorithm is to make each sentence be a chunk. Then use an AI model to  generate an embedding for each chunk. The specific AI model used for encoding depends on the data type and specific use case. Many AI models are available as a service, such as Google Cloud’s [VertexAI](https://cloud.google.com/vertex-ai) or AWS’ [Bedrock](https://aws.amazon.com/bedrock/), simplifying the embedding generation process.
+
+2. **Index**: Store the generated embeddings along with any associated metadata in valkey-search. Each stored item is indexed with a primary key and a set of attributes spanning multiple modalities, for example, tags (categories),  numbers (pricing), etc.
+* **Search**: The query is converted into an embedding using the same AI model as was used in the **Encode** step above. This embedding is used with the vector search capability of valkey-search to locate the most similar vectors. The located vectors correspond to chunks of the original input that have the most similar meaning to the query.
+
+![Semantic search phases](images/highlevel-flow.png)
+
+## Use-Cases Where valkey-search Shines
+
+Valkey-search’s ability to search billions of vectors with millisecond latencies makes it ideal for real-time applications such as:
+
+* **Personalized Recommendations** – Deliver instant, highly relevant recommendations based on real-time user interactions.
+* **Fraud Detection & Security** – Identify anomalies and suspicious activity with ultra-fast similarity matching.
+* **Conversational AI & Chatbots** – Enhance response accuracy and relevance by leveraging rapid vector-based retrieval.
+* **Image & Video Search** – Enable multimedia search through real-time similarity detection.
+* **GenAI & Semantic Search** – Power advanced AI applications with efficient vector retrieval for natural language understanding.
+
+## Valkey-Search Architecture Overview
+
+Valkey-search allows the creation of any number of named indexes. A valkey-search index can be thought of like a relational database table where a row is a Valkey key and a column is an attribute (field) within that key. Each index is defined to cover a portion of the Valkey keyspace and some list of attributes within those keys. Any mutation of a key within the scope of an index synchronously updates that index before that mutation command is acknowledged. Query operations can be performed on a single index, returning the located key names and optionally their contents.
+
+Indexes can be constructed over hash or JSON keys. For hash keys, the indexable attributes of the keys are just the hash fields. For JSON keys, the indexable attributes are identified using the JSON path notation. Regardless of key type, an index can have any number of attributes. Each attribute of an index is declared with a type and sometimes type-specific modifiers. Currently, three index attribute types are supported: Vector, Numeric and Tag. 
+
+### Index Creation
+
+The [FT.CREATE](https://valkey.io/commands/ft.create/) command is used to create a new, empty index. This causes the system to automatically initiate an asynchronous background process to scan the keyspace for keys that belong in the index in a process called backfilling. The backfill process runs until the entire keyspace has been scanned for the index. The `backfill_in_progress` and `backfill_complete_percent` fields of the [FT.INFO](https://valkey.io/commands/ft.info/) command result can be used to monitor the progress of the backfill. Once the backfill is complete it need not be executed against the index again.
+
+During the backfill process, query and ingestion operations proceed normally. Queries will be executed against the current state of the index. Newly mutated data will be placed into the index as usual and remain unaffected by the backfill process. Thus after the creation of an index – while it is backfilling – the application can be certain that queries will contain the results of all data that has been ingested *after* the creation of the index and *some* of the data ingested *before*. Once the backfill has completed then queries will consider all data covered by the index.
+
+In cluster mode, the `FT.CREATE` command can be sent to any primary node of the cluster and the system will automatically distribute the new index definition to all cluster members. The distribution is done using a combination of the cluster bus and direct gRPC communication between nodes. In the rare case where the distribution machinery detects an inconsistency between nodes, a last-writer-wins (LWW) collision resolution protocol is invoked to enable eventual cluster-wide consistency. 
+
+
+## Vector Search Algorithms
+
+Valkey-search offers K Nearest Neighbor (KNN) search. Applications supply a reference vector and request that the system locate the K vectors which are closest to the reference vector using the a specified distance function (L2, IP or cosine). KNN searching is a classic problem which lacks an efficient exact ideal solution. Valkey-search addresses this problem by providing two different algorithms that the developer can select from:
+
+The first algorithm performs an exhaustive linear search, providing exactly correct answers but with a run-time that may be intolerable on large data sets.
+
+The second algorithm addresses this problem by compromising on accuracy in exchange for shorter run-times. In other words, it runs very fast but may not deliver exactly the correct answer. This type of algorithm is often known as Approximate Nearest Neighbor (ANN). The term "recall" is used to measure the quality of an ANN algorithm result and is expressed as the ratio (or percentage) of the found answers to the correct answers, e.g., a recall of 0.95 (or 95%) means that for a search with K = 100, 95 of the correct answers were found. There is a tradeoff between the recall of an ANN algorithm and its time/space resource consumption.
+
+Valkey-search supports the [Hierarchical Navigable Small Worlds](https://en.wikipedia.org/wiki/Hierarchical_navigable_small_world) (HNSW) ANN algorithm as it provides the best performance at the highest levels of recall demanded by real-time applications. The HNSW algorithm has (`O(log N)`) time complexity and gives the developer three parameters to adjust the CPU and memory consumption vs recall. The relationship between these parameters and the resulting operation latency and recall is complex and data dependent. It is recommended that developers test with data that closely approximates production environments.
+
+
+## Hybrid Query Support
+
+Valkey-search query operations are not limited to just vector searching. Documents can contain additional metadata that can be used to enhance searches. Two types of metadata are currently supported: Numeric and Tag. Numeric metadata supports range queries, i.e., you can include or exclude documents with metadata in the particular range. Tag metadata is an enumerated list of words. Tag searches can be done with an exact word match or a prefix match (trailing wild card).
+
+Hybrid queries are vector query operations which have been augmented with a filter, constructed from the numeric and tag searching operators combined with the usual logical operators `AND`, `OR`, and `NOT.` 
+
+Hybrid queries are particularly powerful for real-world applications, where a mix of vector and non-vector attributes defines the relevance of results. For example, a numeric field could be used as a timestamp, meaning that search operations could be automatically confined to a particular period of time. Another example would be to use a tag field to indicate a language.
+
+
+### Query Execution
+
+When executing hybrid queries, valkey-search automatically selects from one of two strategies as part of the query execution planning phase. This is done by breaking down the query filter into predicates and estimating the selectivity of each predicate to estimate the least expensive execution strategy. 
+
+**Pre-filtering:** This algorithm has two sequential steps: first, a temporary dataset is created using only the documents which pass the filter expression, then a linear KNN similarity search of is performed on the temporary dataset. Pre-filtering is particularly effective for high selectivity queries, i.e., the filter significantly narrows down the dataset.
+
+**Inline-filtering:** In this algorithm, filtering is done *during* the vector search itself. As the HNSW search graph is traversed, each candidate document is tested against the filter criteria before being added to the result set. This method is best suited for cases where the filter isn’t highly selective, i.e., the filter matches a large amount of the dataset. 
+
+
+## High Availability
+
+Valkey-search is built on top of Valkey, leveraging its primary/replica-based architecture to provide high availability (HA). Diagram #2 shows a typical configuration of one primary and three replicas. In the event of a primary node failure, the system automatically promotes one of the replicas to become the new primary.
+
+Clients must send data mutation (write) commands to the primary node which are executed and then automatically asynchronously transmitted to each replica. Clients can send data read operations to any node in the cluster, recognizing that reading from a replica is eventually consistent.
+
+When valkey-search is used, each node, whether a primary or a replica, builds and maintains its own indexes. No additional traffic on the replication channel is generated for index maintenance. Search query operations sent to a replica will be executed against its indexes, reflecting the historical point in time of the data within that node.
+
+![High availability](images/ha.png)
+
+## Scaling
+
+Valkey-search supports all three methods of scaling: horizontal, vertical and replicas. When scaling for capacity, valkey-search behaves just like regular Valkey, i.e., increasing the memory of individual nodes (vertical scaling) or increasing the number of nodes (horizontal scaling) will increase the overall capacity.
+
+However, from a performance perspective, valkey-search behaves very differently from regular Valkey. The multi-threaded implementation of valkey-search means that additional CPUs yield up to linear increases in both query and ingestion throughput. Horizontal scaling yields linear increases in ingestion throughput but may provide little to no benefit on query throughput. If additional query throughput is required, scaling through replicas or additional CPUs is required.
+
+
+![Scaling](images/cluster.png)
+
+
+## Snapshots
+
+Valkey’s point-in-time RDB snapshotting mechanism is a key component for maintaining high availability, zero downtime, and minimal operational overhead. Beyond persistence and loading from disk snapshots, RDB plays a crucial role in full-sync operations, where a primary node synchronizes its in-memory data with a replica node in an HA setup. Full-sync is commonly triggered when a new replica joins the cluster but may also occur if a secondary falls too far behind due to prolonged lag.
+
+Valkey-search enhances the Valkey snapshotting mechanism to include index definitions and vector indexes and is built for resilience and efficiency:
+
+**Fast Turn-up:** New nodes must become fully operational in minimal time. While rebuilding an index on startup is an option, ingesting a large volume of vectors can be prohibitively slow, delaying system readiness. A snapshot captures not only the index metadata but also the vector index content which significantly reduces downtime and operational burden.
+
+**Backward/Forward Compatibility:** Cluster upgrades or downgrades often result in a temporary mix of node versions. valkey-search seamlessly handles such scenarios with a serialization format based on [Protocol Buffers (Protobuf)](https://en.wikipedia.org/wiki/Protocol_Buffers), ensuring both backward and forward compatibility. This simplifies version transitions and reduces the chance of costly re-indexing.
+
+
+## RediSearch API Compatibility
+
+Valkey-search implements a subset of RediSearch’s functionality, with compatibility across key vector search APIs. This enables integration with most existing RediSearch client libraries, allowing you to continue using familiar tools. Developers already experienced with RediSearch can adopt valkey-search with minimal changes, as the vector search API remains largely consistent. 
+
+
+## Performance & Low Latency
+
+Valkey-search was designed from the ground up as an in-memory secondary index. A multi-threaded architecture optimizes query and mutation processing with minimal thread contention, enabling near-linear vertical scalability.
+
+At its core, valkey-search’s threading architecture follows a common design pattern: a worker thread pool combined with task queues. It employs advanced synchronization mechanisms to maintain index consistency while minimizing contention among worker threads. By time-slicing CPU access between read and write operations, the system minimizes locks on the read path, delivering high performance and consistently low search latency. 
+
+Valkey-search’s HNSW implementation is based on the OSS project [HNSWLib](https://github.com/nmslib/hnswlib). While HNSWLib is well-regarded for its speed, we have enhanced its performance and efficiency for our use case. These improvements include better SIMD utilization, promotion of CPU cache efficiency, memory utilization and more.
+
+
+## Future Enhancements
+
+This first release of valkey-search focuses on vector search, but it is designed as a general-purpose indexing subsystem. Future releases will extend both the available data types as well as provide post-query data processing facilities.
+
+
+
+## Get Involved
+
+Valkey-search is open source and ready for you to explore. Whether you're building cutting-edge AI applications or integrating vector search into existing systems, we invite you to try it out. The easiest way to get started is by visiting the [GitHub repository](https://github.com/valkey-io/valkey-search), where you'll find setup instructions, documentation, and examples.
+
+We welcome contributions of all kinds - code, documentation, testing, and feedback. Join the community, file issues, open pull requests, or suggest improvements. Your involvement helps make valkey-search better for everyone.
+
+Ready to dive in? Clone the repo, fire up the [dev container](https://hub.docker.com/r/valkey/valkey-bundle), and start building high-performance vector search with valkey-search.
diff --git a/content/blog/2025-06-23-valkey-bundle-one-stop-shop-for-low-latency-modern-applications/index.md b/content/blog/2025-06-23-valkey-bundle-one-stop-shop-for-low-latency-modern-applications/index.md
index ce7176f5..f262c319 100644
--- a/content/blog/2025-06-23-valkey-bundle-one-stop-shop-for-low-latency-modern-applications/index.md
+++ b/content/blog/2025-06-23-valkey-bundle-one-stop-shop-for-low-latency-modern-applications/index.md
@@ -4,8 +4,9 @@ description = "Introducing valkey-bundle a single-container solution that combin
 date= 2025-06-23 00:00:01 
 authors= ["rlunar"] 
 
+[taxonomies]
+blog_type = ["Announcements"]
 [extra]
-type = "Announcements"
 featured = false
 featured_image = "/assets/media/featured/random-02.webp"
 +++
diff --git a/content/blog/2025-06-30-introducing-enhanced-json-capabilities-in-valkey.md b/content/blog/2025-06-30-introducing-enhanced-json-capabilities-in-valkey.md
index b57e9c78..b7a41c35 100644
--- a/content/blog/2025-06-30-introducing-enhanced-json-capabilities-in-valkey.md
+++ b/content/blog/2025-06-30-introducing-enhanced-json-capabilities-in-valkey.md
@@ -3,8 +3,8 @@ title = "Introducing Enhanced JSON Capabilities in Valkey"
 description = "Learn about the new Valkey JSON with some real-world examples and application."
 date = "2025-06-30 01:01:01"
 authors= [ "roshkhatri"]
-[extra]
-type = "Announcements"
+[taxonomies]
+blog_type = ["Announcements"]
 +++
 
 JSON is a ubiquitous format for semi-structured data, and developers often expect native support across their technology stack, including in-memory stores like Valkey. Previously, working with JSON in Valkey required serializing entire objects as strings or flattening them into Hashes, limiting nesting. These workarounds added complexity and made updates harder than they should be.
diff --git a/content/blog/2025-07-10-keyspace-save-the-date.md b/content/blog/2025-07-10-keyspace-save-the-date.md
index c8b76fee..531cc738 100644
--- a/content/blog/2025-07-10-keyspace-save-the-date.md
+++ b/content/blog/2025-07-10-keyspace-save-the-date.md
@@ -3,8 +3,9 @@ title = "Save the Date August 28, 2025: Keyspace"
 description = "Join the Valkey crew on August 28, 2025 in Amsterdam for the first ever Valkey conference: Keyspace!"
 date = "2025-07-10 01:01:01"
 authors= [ "kyledvs"]
+[taxonomies]
+blog_type = ["Events"]
 [extra]
-type = "Events"
 featured = true
 featured_image = "/assets/media/featured/random-05.webp"
 +++
diff --git a/content/blog/2025-07-21-valkey-on-k0rdent.md b/content/blog/2025-07-21-valkey-on-k0rdent.md
index a0a601a8..5e073e7a 100644
--- a/content/blog/2025-07-21-valkey-on-k0rdent.md
+++ b/content/blog/2025-07-21-valkey-on-k0rdent.md
@@ -3,8 +3,8 @@ title = "Using k0rdent MultiClusterService Template for Valkey on Kubernetes"
 description = "Learn how to deploy and manage Valkey across multiple Kubernetes clusters using k0rdent's template-driven approach for simplified multi-cluster application delivery."
 date = "2025-07-21 01:01:42"
 authors= [ "s3rj1k"]
-[extra]
-type = "Community Highlight"
+[taxonomies]
+blog_type = ["Community Highlight"]
 +++
 
 Managing distributed applications across multiple Kubernetes clusters can be a complex and time-consuming process. This guide demonstrates how to streamline Valkey deployment using [k0rdent's](https://k0rdent.io/) `MultiClusterService` template, providing a practical example of modern multi-cluster application delivery.
diff --git a/content/blog/2025-08-04-valkey-swift/index.md b/content/blog/2025-08-04-valkey-swift/index.md
index f912d903..0399306d 100644
--- a/content/blog/2025-08-04-valkey-swift/index.md
+++ b/content/blog/2025-08-04-valkey-swift/index.md
@@ -4,8 +4,9 @@ description = "The new Swift client for Valkey is now in preview. Learn about it
 date= 2025-08-04 00:00:00
 authors= [ "adamfowler"]
 
+[taxonomies]
+blog_type = ["Announcements"]
 [extra]
-type = "Announcements"
 featured = true
 featured_image = "/assets/media/featured/random-04.webp"
 +++
diff --git a/content/blog/2025-08-12-keyspace-schedule.md b/content/blog/2025-08-12-keyspace-schedule.md
index 60f7c4de..39bd592f 100644
--- a/content/blog/2025-08-12-keyspace-schedule.md
+++ b/content/blog/2025-08-12-keyspace-schedule.md
@@ -3,8 +3,8 @@ title = "What you'll see at Keyspace 2025"
 description = "I sat on the selection committee for Keyspace and was floored by the number and quality of submissions we received. It's truly amazing to see the talent and depth of experience that is present in the Valkey community manifest in a slate of presentations aimed to interest both experienced and new Valkey users."
 date = "2025-08-12 01:01:01"
 authors= [ "kyledvs" ]
-[extra]
-type = "Events"
+[taxonomies]
+blog_type = ["Events"]
 +++
 
 <style type="text/css">
diff --git a/content/blog/2025-09-11-valkey-investment-in-open-source.md b/content/blog/2025-09-11-valkey-investment-in-open-source.md
index c99b6541..ed1b76bc 100644
--- a/content/blog/2025-09-11-valkey-investment-in-open-source.md
+++ b/content/blog/2025-09-11-valkey-investment-in-open-source.md
@@ -4,8 +4,9 @@ description = "Valkey was founded just over a year ago to keep high-performance
 date= 2025-09-16 00:00:00
 authors= [ "lorilorusso"]
 
+[taxonomies]
+blog_type = ["Community Highlight"]
 [extra]
-type = "Community Highlight"
 featured = true
 featured_image = "/assets/media/featured/random-04.webp"
 +++
diff --git a/content/blog/2025-09-25-numbered-databases/index.md b/content/blog/2025-09-25-numbered-databases/index.md
index 0450bb51..b8014af7 100644
--- a/content/blog/2025-09-25-numbered-databases/index.md
+++ b/content/blog/2025-09-25-numbered-databases/index.md
@@ -4,8 +4,9 @@ description = "Valkey 9.0 brings new namespacing abilities to cluster mode. In t
 date= 2025-09-25 00:00:00
 authors= [ "kyledvs"]
 
+[taxonomies]
+blog_type = ["Technical Deep Dive"]
 [extra]
-type = "Technical Deep Dive"
 featured = true
 featured_image = "/blog/numbered-databases/images/move-db.drawio.png"
 +++
diff --git a/content/blog/2025-09-30-hash-fields-expiration/index.md b/content/blog/2025-09-30-hash-fields-expiration/index.md
index efb6c6ed..47de4726 100644
--- a/content/blog/2025-09-30-hash-fields-expiration/index.md
+++ b/content/blog/2025-09-30-hash-fields-expiration/index.md
@@ -10,8 +10,9 @@ description= """
     We’ll also walk through practical examples to show how you can start using this feature today.
     """
 authors= ["ranshid"]
+[taxonomies]
+blog_type = ["Technical Deep Dive"]
 [extra]
-type = "Technical Deep Dive"
 featured = true
 featured_image = "/assets/media/featured/random-08.webp"
 +++
diff --git a/content/blog/2025-10-02-50-million-zsets/index.md b/content/blog/2025-10-02-50-million-zsets/index.md
index b9278d85..da29c2cf 100644
--- a/content/blog/2025-10-02-50-million-zsets/index.md
+++ b/content/blog/2025-10-02-50-million-zsets/index.md
@@ -5,8 +5,9 @@ description= """
 The latest Valkey 8.1 release introduces a redesigned hash table and other optimizations that promise lower memory usage and higher throughput. In this post, we put Valkey 8.1 under pressure by benchmarking it against Valkey 8.0, inserting 50 million members into a sorted set and measuring memory consumption and throughput along the way.
     """
 authors= ["khawaja"]
+[taxonomies]
+blog_type = ["Technical Deep Dive"]
 [extra]
-type = "Technical Deep Dive"
 featured = true
 featured_image = "/blog/50-million-zsets/hero-used-memory-vs-inserts-new.png"
 +++
diff --git a/content/blog/2025-10-15-properly-secure-your-valkey-deployment.md b/content/blog/2025-10-15-properly-secure-your-valkey-deployment.md
index 1fdf4cde..af31e57f 100644
--- a/content/blog/2025-10-15-properly-secure-your-valkey-deployment.md
+++ b/content/blog/2025-10-15-properly-secure-your-valkey-deployment.md
@@ -4,8 +4,9 @@ description = "Learn how to secure your Valkey deployment with defense in depth
 date= 2025-12-12 00:00:00
 authors= [ "allenhelton"]
 
+[taxonomies]
+blog_type = ["How-to"]
 [extra]
-type = "How-to"
 featured = true
 featured_image = "/assets/media/featured/random-06.webp"
 +++
diff --git a/content/blog/2025-10-20-1-billion-rps/index.md b/content/blog/2025-10-20-1-billion-rps/index.md
index 939010ec..5c7e7f6d 100644
--- a/content/blog/2025-10-20-1-billion-rps/index.md
+++ b/content/blog/2025-10-20-1-billion-rps/index.md
@@ -3,8 +3,9 @@ title= "Scaling a Valkey Cluster to 1 Billion Request per Second"
 date= 2025-10-20 00:00:00
 description= "Learn about the internal workings of cluster mode and the improvements which helped to scale to 1 Billion RPS with the latest Valkey 9.0 release"
 authors= ["hpatro", "maheshcherukumilli", "sarthakaggarwal97", "sungming2"]
+[taxonomies]
+blog_type = ["Technical Deep Dive"]
 [extra]
-type = "Technical Deep Dive"
 featured = true
 featured_image = "/assets/media/featured/random-04.webp"
 +++
diff --git a/content/blog/2025-10-21-introducing-valkey-9/index.md b/content/blog/2025-10-21-introducing-valkey-9/index.md
index 3103eed3..d0316ea7 100644
--- a/content/blog/2025-10-21-introducing-valkey-9/index.md
+++ b/content/blog/2025-10-21-introducing-valkey-9/index.md
@@ -6,8 +6,9 @@ For Valkey's second major release, Valkey 9.0 brings innovation, long-requested
 Read on to find out all the team packed into this release.
     """
 authors= ["kyledvs"]
+[taxonomies]
+blog_type = ["Announcements"]
 [extra]
-type = "Announcements"
 featured = true
 featured_image = "/assets/media/featured/random-08.webp"
 +++
diff --git a/content/blog/2025-10-27-atomic-slot-migration/index.md b/content/blog/2025-10-27-atomic-slot-migration/index.md
index 6073c9e0..a9859416 100644
--- a/content/blog/2025-10-27-atomic-slot-migration/index.md
+++ b/content/blog/2025-10-27-atomic-slot-migration/index.md
@@ -3,8 +3,9 @@ title= "Resharding, Reimagined: Introducing Atomic Slot Migration"
 date= 2025-10-29 00:00:00
 description= "Valkey 9.0 brings big changes and improvements to the underlying mechanisms of resharding. Find out how Atomic Slot Migration works, the benefits it brings, and the headaches it eliminates."
 authors= ["murphyjacob4"]
+[taxonomies]
+blog_type = ["Technical Deep Dive"]
 [extra]
-type = "Technical Deep Dive"
 featured = true
 featured_image = "/assets/media/featured/random-05.webp"
 +++
diff --git a/content/blog/2026-01-06-valkey-helm-chart/index.md b/content/blog/2026-01-06-valkey-helm-chart/index.md
index 8679695d..aa5eed9c 100644
--- a/content/blog/2026-01-06-valkey-helm-chart/index.md
+++ b/content/blog/2026-01-06-valkey-helm-chart/index.md
@@ -3,8 +3,9 @@ title = "Valkey Helm: The new way to deploy Valkey on Kubernetes"
 date = 2026-01-06
 description = "A guide on why the new Valkey Helm chart exists, how it helps, and how to migrate from Bitnami." 
 authors =  ["sgissi","maheshcherukumilli"]
+[taxonomies]
+blog_type = ["Announcements"]
 [extra]
-type = "Announcements"
 featured = true
 featured_image = "/assets/media/featured/valkey-helm.webp"
 +++
diff --git a/content/blog/2026-01-22-2025-year-end/index.md b/content/blog/2026-01-22-2025-year-end/index.md
index affa938b..4b6f29c6 100644
--- a/content/blog/2026-01-22-2025-year-end/index.md
+++ b/content/blog/2026-01-22-2025-year-end/index.md
@@ -3,8 +3,8 @@ title = "Valkey 2025 Year-End Review: Reflecting on Progress and Looking Ahead"
 date = 2026-01-22
 description = "This past year marked an important chapter for Valkey, one defined by growth, collaboration, and a shared commitment to building an open, high-performance key-value store for everyone."
 authors= ["kyledvs", "crystalpham"]
-[extra]
-type = "Community Highlight"
+[taxonomies]
+blog_type = ["Community Highlight"]
 +++
 
 As we kick off 2026, it’s a great moment to pause, reflect, and celebrate what the Valkey project and its community has accomplished together. This past year marked an important chapter for Valkey, one defined by growth, collaboration, and a shared commitment to building an open, high-performance key-value store for everyone.
diff --git a/content/blog/2026-02-04-valkey-newsletter-new/index.md b/content/blog/2026-02-04-valkey-newsletter-new/index.md
index 5b8bdf5c..3beab179 100644
--- a/content/blog/2026-02-04-valkey-newsletter-new/index.md
+++ b/content/blog/2026-02-04-valkey-newsletter-new/index.md
@@ -3,8 +3,8 @@ title = "Don’t Miss What’s Next in Valkey — Subscribe Now"
 date = 2026-02-04
 description = "Valkey is moving fast — and the easiest way to stay ahead is to subscribe to the official Valkey newsletter. From new releases and roadmap milestones to community highlights and upcoming events, our newsletter is your single source of truth for everything happening across the Valkey ecosystem."
 authors = ["crystalpham"]
-[extra]
-type = "Announcements"
+[taxonomies]
+blog_type = ["Announcements"]
 +++
 
 Valkey is moving fast and the easiest way to stay ahead is to [subscribe](/blog/valkey-newsletter-new/#email-signup) to the official Valkey newsletter.
diff --git a/content/blog/2026-02-19-operational-lessons/index.md b/content/blog/2026-02-19-operational-lessons/index.md
index 87d20bea..d860b6d7 100644
--- a/content/blog/2026-02-19-operational-lessons/index.md
+++ b/content/blog/2026-02-19-operational-lessons/index.md
@@ -4,8 +4,9 @@ date = 2026-02-19
 description = "Production lessons on tail latency, migrations, and scale from engineers operating Valkey at real-world scale."
 authors = ["allenhelton", "mikecallahan"]
 
+[taxonomies]
+blog_type = ["Community Highlight"]
 [extra]
-type = "Community Highlight"
 featured = true
 featured_image = "/assets/media/featured/random-04.webp"
 +++
diff --git a/content/blog/2026-02-25-introducing-valkey-admin/index.md b/content/blog/2026-02-25-introducing-valkey-admin/index.md
index ebad06a6..a6de5d29 100644
--- a/content/blog/2026-02-25-introducing-valkey-admin/index.md
+++ b/content/blog/2026-02-25-introducing-valkey-admin/index.md
@@ -4,8 +4,9 @@ description = "Valkey Admin is an open-source desktop application that provides
 date= 2026-02-25 00:00:00
 authors= [ "allenhelton", "arsenykostenko"]
 
+[taxonomies]
+blog_type = ["Community Highlight"]
 [extra]
-type = "Community Highlight"
 featured = true
 featured_image = "/blog/introducing-valkey-admin/images/hero.jpg"
 +++
diff --git a/content/blog/2026-03-10-valkey-search-1_2/index.md b/content/blog/2026-03-10-valkey-search-1_2/index.md
index 95e80341..e6f8ece9 100644
--- a/content/blog/2026-03-10-valkey-search-1_2/index.md
+++ b/content/blog/2026-03-10-valkey-search-1_2/index.md
@@ -3,8 +3,8 @@ title= "Beyond Vectors: Introducing Full-Text Search and Aggregations to Valkey"
 description = "Valkey Search now enables querying across text, tags, and numeric data and analyzing results with aggregations."
 date= 2026-03-17 01:00:00
 authors= [ "karthiksubbarao", "allenss", "bcathcart", "cnuthalapati"]
-[extra]
-type = "Announcements"
+[taxonomies]
+blog_type = ["Announcements"]
 +++
 
 Valkey Search now lets you search across text, tag, numeric, and vector attributes in a single query, and analyze results with server-side aggregations at the low latency you expect from Valkey. 
diff --git a/content/blog/2026-03-17-spring-data-valkey/index.md b/content/blog/2026-03-17-spring-data-valkey/index.md
index 7ed2d54d..df995f4b 100644
--- a/content/blog/2026-03-17-spring-data-valkey/index.md
+++ b/content/blog/2026-03-17-spring-data-valkey/index.md
@@ -4,8 +4,9 @@ description = "Introducing Spring Data Valkey, a new open source Spring Data mod
 date= 2026-04-01 00:00:00
 authors= ["makubo-aws"]
 
+[taxonomies]
+blog_type = ["Announcements"]
 [extra]
-type = "Announcements"
 featured = false
 +++
 
diff --git a/content/blog/2026-03-27-valkey-tooling-primitives.md b/content/blog/2026-03-27-valkey-tooling-primitives.md
index c52a5c75..215c4898 100644
--- a/content/blog/2026-03-27-valkey-tooling-primitives.md
+++ b/content/blog/2026-03-27-valkey-tooling-primitives.md
@@ -4,8 +4,9 @@ date = 2026-03-27
 description = "Valkey ships powerful operational primitives like COMMANDLOG, SLOT-STATS, and upcoming per-thread I/O metrics — but without purpose-built tooling, much of that value goes unrealized."
 authors = ["kivanow"]
 
+[taxonomies]
+blog_type = ["Technical Deep Dive"]
 [extra]
-type = "Technical Deep Dive"
 featured = true
 featured_image = "/assets/media/featured/random-04.webp"
 +++
diff --git a/content/blog/README b/content/blog/README
index f1c7813e..2bc5aa11 100644
--- a/content/blog/README
+++ b/content/blog/README
@@ -13,8 +13,10 @@ date = 2026-04-01
 description = "A short summary shown in the blog listing and metadata."
 authors = ["github-username"]
 
+[taxonomies]
+blog_type = ["Announcements"]
+
 [extra]
-type = "Announcements"
 featured = false
 featured_image = "/assets/media/featured/random-01.webp"
 +++
@@ -28,7 +30,7 @@ featured_image = "/assets/media/featured/random-01.webp"
 | `date` | Publish date (`YYYY-MM-DD`) |
 | `description` | Short summary for the blog listing |
 | `authors` | List of GitHub usernames |
-| `extra.type` | Post category (see below). **The build will fail if this is missing.** |
+| `taxonomies.blog_type` | Post category (see below) |
 
 ### Optional fields
 
@@ -39,7 +41,7 @@ featured_image = "/assets/media/featured/random-01.webp"
 
 ### Post types
 
-Valid types are defined in `config.toml` under `[extra.blog_types]`. Current values:
+The `blog_type` taxonomy is defined in `config.toml`. Current values:
 
 - `Technical Deep Dive` — In-depth technical content
 - `Announcements` — Releases, new features, project news
@@ -47,7 +49,7 @@ Valid types are defined in `config.toml` under `[extra.blog_types]`. Current val
 - `How-to` — Tutorials and guides
 - `Events` — Event recaps and announcements
 
-To add a new type, update `config.toml`, `sass/_valkey.scss` (badge color), and this README.
+To add a new type, add a post using it and update `sass/_valkey.scss` (badge color) and this README.
 
 ## File naming
 
diff --git a/content/blog/whats-new-june-2024.md b/content/blog/whats-new-june-2024.md
index dcaffef4..4d0b4cbb 100644
--- a/content/blog/whats-new-june-2024.md
+++ b/content/blog/whats-new-june-2024.md
@@ -4,8 +4,9 @@ date= 2024-06-27 01:01:01
 description= "What have people been saying about Valkey in June 2024? Read on to find out."
 authors= ["kyledvs"]
 
+[taxonomies]
+blog_type = ["Community Highlight"]
 [extra]
-type = "Community Highlight"
 categories= "roundup"
 featured = false
 featured_image = "/assets/media/featured/random-01.webp"
diff --git a/content/events/kcd-guadalajara-2026.md b/content/events/kcd-guadalajara-2026.md
new file mode 100644
index 00000000..5b70c63a
--- /dev/null
+++ b/content/events/kcd-guadalajara-2026.md
@@ -0,0 +1,10 @@
++++
+title = "KCD Guadalajara 2026"
+date = 2026-04-18 01:01:01
+
+[extra]
+event_type = "third-party"
+location = "Holiday Inn Guadalajara Expo, Zapopan, Mexico"
+location_url = "https://www.google.com/maps/place/Holiday+Inn+Guadalajara+Expo"
+external_url = "https://community.cncf.io/e/mc7dvm/"
++++
diff --git a/content/events/opensearch-valkey-bay-area-april-2026.md b/content/events/opensearch-valkey-bay-area-april-2026.md
new file mode 100644
index 00000000..89dfe9a4
--- /dev/null
+++ b/content/events/opensearch-valkey-bay-area-april-2026.md
@@ -0,0 +1,10 @@
++++
+title = "OpenSearch & Valkey Bay Area Meetup"
+date = 2026-04-20 01:01:01
+
+[extra]
+event_type = "third-party"
+location = "AWS Builder Loft, San Francisco, CA"
+location_url = "https://www.google.com/maps/place/AWS+Builder+Loft+San+Francisco"
+external_url = "https://www.meetup.com/opensearch-project-valkey-bay-area/events/314045229/"
++++
diff --git a/content/events/oss-na-2026.md b/content/events/oss-na-2026.md
new file mode 100644
index 00000000..75cffe36
--- /dev/null
+++ b/content/events/oss-na-2026.md
@@ -0,0 +1,10 @@
++++
+title = "Open Source Summit North America 2026"
+date = 2026-05-18 01:01:01
+
+[extra]
+event_type = "third-party"
+location = "Minneapolis Convention Center, Minneapolis, MN"
+location_url = "https://www.google.com/maps/place/Minneapolis+Convention+Center"
+external_url = "https://events.linuxfoundation.org/open-source-summit-north-america/"
++++
diff --git a/content/events/percona-live-2026-bay-area.md b/content/events/percona-live-2026-bay-area.md
new file mode 100644
index 00000000..bbf4c6c8
--- /dev/null
+++ b/content/events/percona-live-2026-bay-area.md
@@ -0,0 +1,10 @@
++++
+title = "Percona Live 2026 Bay Area"
+date = 2026-05-27 01:01:01
+
+[extra]
+event_type = "third-party"
+location = "Mountain View, CA"
+location_url = "https://www.google.com/maps/place/Mountain+View,+CA"
+external_url = "https://perconalive.com/2026-usa/"
++++
diff --git a/templates/blog-page.html b/templates/blog-page.html
index 655f0491..0d3d2e77 100644
--- a/templates/blog-page.html
+++ b/templates/blog-page.html
@@ -7,7 +7,11 @@
 
 <h1>{{ page.title }}</h1>
 <div class="meta">
-  <span class="blog-type-badge" data-type="{{ page.extra.type }}">{{ page.extra.type }}</span>
+  {% set blog_type = page.taxonomies.blog_type | default(value=[]) %}
+  {% set type_name = blog_type | first | default(value="") %}
+  {% if type_name %}
+  <span class="blog-type-badge" data-type="{{ type_name }}">{{ type_name }}</span>
+  {% endif %}
   {{ page.date | date(format="%Y-%m-%d") }}
   {% if page.authors %}
     &middot; 
diff --git a/templates/blog.html b/templates/blog.html
index 1227397c..8f1f5869 100644
--- a/templates/blog.html
+++ b/templates/blog.html
@@ -8,25 +8,26 @@ <h1>{{ section.title }}</h1>
 {% endblock subhead_content %}
 
 {% block main_content %}
-  {% set blog_types = config.extra.blog_types.types %}
+  {% set taxonomy = get_taxonomy(kind="blog_type", required=false) %}
+  {% if taxonomy %}
   <div class="blog-type-filters" hidden>
     <label for="blog-type-select" class="sr-only">Filter posts by type</label>
     <select id="blog-type-select">
       <option value="All">All Types</option>
-      {% for t in blog_types %}
-      <option value="{{ t }}">{{ t }}</option>
+      {% for term in taxonomy.items %}
+      <option value="{{ term.name }}">{{ term.name }}</option>
       {% endfor %}
     </select>
   </div>
+  {% endif %}
   <ul class="blog-list">
     {% for page in section.pages %}
-    {% if not page.extra.type %}
-      {{ throw(message="Blog post '" ~ page.title ~ "' is missing [extra] type. Add type to its front matter.") }}
-    {% elif blog_types is not containing(page.extra.type) %}
-      {{ throw(message="Blog post '" ~ page.title ~ "' has invalid type '" ~ page.extra.type ~ "'. Check config.toml for valid types.") }}
-    {% endif %}
-    <li data-type="{{ page.extra.type }}">
-      <span class="blog-type-badge" data-type="{{ page.extra.type }}">{{ page.extra.type }}</span>
+    {% set blog_type = page.taxonomies.blog_type | default(value=[]) %}
+    {% set type_name = blog_type | first | default(value="") %}
+    <li{% if type_name %} data-type="{{ type_name }}"{% endif %}>
+      {% if type_name %}
+      <span class="blog-type-badge" data-type="{{ type_name }}">{{ type_name }}</span>
+      {% endif %}
       <h2><a href="{{ page.permalink }}">{{ page.title  | safe}}</a></h2>
       <div class="meta">{{ page.date | date(format="%A %B %e, %Y") }}</div>
       <p>{{ page.description | safe }}</p>
@@ -36,37 +37,34 @@ <h2><a href="{{ page.permalink }}">{{ page.title  | safe}}</a></h2>
   <p class="blog-no-results" hidden>No posts match this filter.</p>
   <script>
   (function() {
-    const select = document.getElementById('blog-type-select');
-    const items = document.querySelectorAll('.blog-list li');
-    const noResults = document.querySelector('.blog-no-results');
-    const filters = select.closest('.blog-type-filters');
-
-    filters.hidden = false;
+    var select = document.getElementById('blog-type-select');
+    if (!select) return;
+    var items = document.querySelectorAll('.blog-list li');
+    var noResults = document.querySelector('.blog-no-results');
+    select.closest('.blog-type-filters').hidden = false;
 
     function applyFilter(type) {
-      let visible = 0;
+      var visible = 0;
       items.forEach(function(li) {
-        const show = type === 'All' || li.getAttribute('data-type') === type;
+        var show = type === 'All' || li.getAttribute('data-type') === type;
         li.hidden = !show;
         if (show) visible++;
       });
       noResults.hidden = visible > 0;
     }
 
-    // Sync filter with URL hash on load
-    const initial = decodeURIComponent(location.hash.slice(1));
-    if (initial) {
-      const option = Array.from(select.options).find(function(o) { return o.value === initial; });
-      if (option) {
-        select.value = initial;
-        applyFilter(initial);
+    try {
+      var initial = decodeURIComponent(location.hash.slice(1));
+      if (initial) {
+        var option = Array.from(select.options).find(function(o) { return o.value === initial; });
+        if (option) { select.value = initial; applyFilter(initial); }
       }
-    }
+    } catch(e) {}
 
     select.addEventListener('change', function() {
-      const type = select.value;
+      var type = select.value;
       applyFilter(type);
-      history.replaceState(null, '', type === 'All' ? location.pathname : '#' + encodeURIComponent(type));
+      history.replaceState(null, '', type === 'All' ? location.pathname + location.search : '#' + encodeURIComponent(type));
     });
   })();
   </script>

From 2a7e9bab1903ac77c5fdedcbd11d01e0d3c8bd65 Mon Sep 17 00:00:00 2001
From: Madelyn Olson <madelyneolson@gmail.com>
Date: Tue, 7 Apr 2026 08:31:08 -0700
Subject: [PATCH 4/7] Remove event files not related to this PR

Signed-off-by: Madelyn Olson <madelyneolson@gmail.com>
---
 content/events/kcd-guadalajara-2026.md                 | 10 ----------
 .../events/opensearch-valkey-bay-area-april-2026.md    | 10 ----------
 content/events/oss-na-2026.md                          | 10 ----------
 content/events/percona-live-2026-bay-area.md           | 10 ----------
 4 files changed, 40 deletions(-)
 delete mode 100644 content/events/kcd-guadalajara-2026.md
 delete mode 100644 content/events/opensearch-valkey-bay-area-april-2026.md
 delete mode 100644 content/events/oss-na-2026.md
 delete mode 100644 content/events/percona-live-2026-bay-area.md

diff --git a/content/events/kcd-guadalajara-2026.md b/content/events/kcd-guadalajara-2026.md
deleted file mode 100644
index 5b70c63a..00000000
--- a/content/events/kcd-guadalajara-2026.md
+++ /dev/null
@@ -1,10 +0,0 @@
-+++
-title = "KCD Guadalajara 2026"
-date = 2026-04-18 01:01:01
-
-[extra]
-event_type = "third-party"
-location = "Holiday Inn Guadalajara Expo, Zapopan, Mexico"
-location_url = "https://www.google.com/maps/place/Holiday+Inn+Guadalajara+Expo"
-external_url = "https://community.cncf.io/e/mc7dvm/"
-+++
diff --git a/content/events/opensearch-valkey-bay-area-april-2026.md b/content/events/opensearch-valkey-bay-area-april-2026.md
deleted file mode 100644
index 89dfe9a4..00000000
--- a/content/events/opensearch-valkey-bay-area-april-2026.md
+++ /dev/null
@@ -1,10 +0,0 @@
-+++
-title = "OpenSearch & Valkey Bay Area Meetup"
-date = 2026-04-20 01:01:01
-
-[extra]
-event_type = "third-party"
-location = "AWS Builder Loft, San Francisco, CA"
-location_url = "https://www.google.com/maps/place/AWS+Builder+Loft+San+Francisco"
-external_url = "https://www.meetup.com/opensearch-project-valkey-bay-area/events/314045229/"
-+++
diff --git a/content/events/oss-na-2026.md b/content/events/oss-na-2026.md
deleted file mode 100644
index 75cffe36..00000000
--- a/content/events/oss-na-2026.md
+++ /dev/null
@@ -1,10 +0,0 @@
-+++
-title = "Open Source Summit North America 2026"
-date = 2026-05-18 01:01:01
-
-[extra]
-event_type = "third-party"
-location = "Minneapolis Convention Center, Minneapolis, MN"
-location_url = "https://www.google.com/maps/place/Minneapolis+Convention+Center"
-external_url = "https://events.linuxfoundation.org/open-source-summit-north-america/"
-+++
diff --git a/content/events/percona-live-2026-bay-area.md b/content/events/percona-live-2026-bay-area.md
deleted file mode 100644
index bbf4c6c8..00000000
--- a/content/events/percona-live-2026-bay-area.md
+++ /dev/null
@@ -1,10 +0,0 @@
-+++
-title = "Percona Live 2026 Bay Area"
-date = 2026-05-27 01:01:01
-
-[extra]
-event_type = "third-party"
-location = "Mountain View, CA"
-location_url = "https://www.google.com/maps/place/Mountain+View,+CA"
-external_url = "https://perconalive.com/2026-usa/"
-+++

From 66b62fe6cef696c037e5e62465b35e20bb49c1af Mon Sep 17 00:00:00 2001
From: Madelyn Olson <madelyneolson@gmail.com>
Date: Tue, 7 Apr 2026 12:51:29 -0700
Subject: [PATCH 5/7] Preserve CRLF line endings in valkey-search blog post

Signed-off-by: Madelyn Olson <madelyneolson@gmail.com>
---
 .../index.md                                  | 287 +++++++++---------
 1 file changed, 143 insertions(+), 144 deletions(-)

diff --git a/content/blog/2025-06-13-introducing-valkey-search/index.md b/content/blog/2025-06-13-introducing-valkey-search/index.md
index 118c9a07..83c7de10 100644
--- a/content/blog/2025-06-13-introducing-valkey-search/index.md
+++ b/content/blog/2025-06-13-introducing-valkey-search/index.md
@@ -1,144 +1,143 @@
-+++
-title= "Introducing Vector Search To Valkey" 
-description = "Learn how to build high-performance, reliable vector similarity search with valkey-search." 
-date= 2025-07-07 01:01:01 
-authors= ["yairgott", "allenss"] 
-
-[taxonomies]
-blog_type = ["Announcements"]
-[extra]
-featured = false
-featured_image = "/assets/media/featured/random-08.webp"
-+++
-
-[Valkey-search](https://github.com/valkey-io/valkey-search) is an official Valkey module that introduces native vector similarity search capabilities. It allows you to efficiently create indexes and search through billions of vectors stored in your Valkey instances. Whether you're building semantic search, fraud detection systems, or conversational AI experiences, Valkey-Search provides a flexible and high-performance foundation for your application. It is compatible with Valkey versions 8.1.1 and above and is BSD-3-Clause licensed.
-
-
-In this blog, you'll learn how valkey-search works, explore key use cases it supports, understand its architecture and indexing model, and see how to integrate it into your own applications. You'll also gain insight into how it scales, ensures high availability, and supports hybrid queries that combine vector similarity with structured filtering.
-
-
-## Semantic Search
-
-The ability of AI models to extract semantic meaning enables new classes of search algorithms, collectively known as semantic search. An AI model can process an input and convert it into a single high-dimension numeric vector – known as an embedding. Inputs with similar meaning will have similar embeddings. Semantic search is the process of converting a query into its embedding and searching a database of embeddings to find the most similar results. 
-
-The semantic search process can be divided into three phases:
-
-1. **Encode**:  First, convert your input data into searchable units known as chunks. Chunking strategies are data-type specific. For example, with text one well-known chunking algorithm is to make each sentence be a chunk. Then use an AI model to  generate an embedding for each chunk. The specific AI model used for encoding depends on the data type and specific use case. Many AI models are available as a service, such as Google Cloud’s [VertexAI](https://cloud.google.com/vertex-ai) or AWS’ [Bedrock](https://aws.amazon.com/bedrock/), simplifying the embedding generation process.
-
-2. **Index**: Store the generated embeddings along with any associated metadata in valkey-search. Each stored item is indexed with a primary key and a set of attributes spanning multiple modalities, for example, tags (categories),  numbers (pricing), etc.
-* **Search**: The query is converted into an embedding using the same AI model as was used in the **Encode** step above. This embedding is used with the vector search capability of valkey-search to locate the most similar vectors. The located vectors correspond to chunks of the original input that have the most similar meaning to the query.
-
-![Semantic search phases](images/highlevel-flow.png)
-
-## Use-Cases Where valkey-search Shines
-
-Valkey-search’s ability to search billions of vectors with millisecond latencies makes it ideal for real-time applications such as:
-
-* **Personalized Recommendations** – Deliver instant, highly relevant recommendations based on real-time user interactions.
-* **Fraud Detection & Security** – Identify anomalies and suspicious activity with ultra-fast similarity matching.
-* **Conversational AI & Chatbots** – Enhance response accuracy and relevance by leveraging rapid vector-based retrieval.
-* **Image & Video Search** – Enable multimedia search through real-time similarity detection.
-* **GenAI & Semantic Search** – Power advanced AI applications with efficient vector retrieval for natural language understanding.
-
-## Valkey-Search Architecture Overview
-
-Valkey-search allows the creation of any number of named indexes. A valkey-search index can be thought of like a relational database table where a row is a Valkey key and a column is an attribute (field) within that key. Each index is defined to cover a portion of the Valkey keyspace and some list of attributes within those keys. Any mutation of a key within the scope of an index synchronously updates that index before that mutation command is acknowledged. Query operations can be performed on a single index, returning the located key names and optionally their contents.
-
-Indexes can be constructed over hash or JSON keys. For hash keys, the indexable attributes of the keys are just the hash fields. For JSON keys, the indexable attributes are identified using the JSON path notation. Regardless of key type, an index can have any number of attributes. Each attribute of an index is declared with a type and sometimes type-specific modifiers. Currently, three index attribute types are supported: Vector, Numeric and Tag. 
-
-### Index Creation
-
-The [FT.CREATE](https://valkey.io/commands/ft.create/) command is used to create a new, empty index. This causes the system to automatically initiate an asynchronous background process to scan the keyspace for keys that belong in the index in a process called backfilling. The backfill process runs until the entire keyspace has been scanned for the index. The `backfill_in_progress` and `backfill_complete_percent` fields of the [FT.INFO](https://valkey.io/commands/ft.info/) command result can be used to monitor the progress of the backfill. Once the backfill is complete it need not be executed against the index again.
-
-During the backfill process, query and ingestion operations proceed normally. Queries will be executed against the current state of the index. Newly mutated data will be placed into the index as usual and remain unaffected by the backfill process. Thus after the creation of an index – while it is backfilling – the application can be certain that queries will contain the results of all data that has been ingested *after* the creation of the index and *some* of the data ingested *before*. Once the backfill has completed then queries will consider all data covered by the index.
-
-In cluster mode, the `FT.CREATE` command can be sent to any primary node of the cluster and the system will automatically distribute the new index definition to all cluster members. The distribution is done using a combination of the cluster bus and direct gRPC communication between nodes. In the rare case where the distribution machinery detects an inconsistency between nodes, a last-writer-wins (LWW) collision resolution protocol is invoked to enable eventual cluster-wide consistency. 
-
-
-## Vector Search Algorithms
-
-Valkey-search offers K Nearest Neighbor (KNN) search. Applications supply a reference vector and request that the system locate the K vectors which are closest to the reference vector using the a specified distance function (L2, IP or cosine). KNN searching is a classic problem which lacks an efficient exact ideal solution. Valkey-search addresses this problem by providing two different algorithms that the developer can select from:
-
-The first algorithm performs an exhaustive linear search, providing exactly correct answers but with a run-time that may be intolerable on large data sets.
-
-The second algorithm addresses this problem by compromising on accuracy in exchange for shorter run-times. In other words, it runs very fast but may not deliver exactly the correct answer. This type of algorithm is often known as Approximate Nearest Neighbor (ANN). The term "recall" is used to measure the quality of an ANN algorithm result and is expressed as the ratio (or percentage) of the found answers to the correct answers, e.g., a recall of 0.95 (or 95%) means that for a search with K = 100, 95 of the correct answers were found. There is a tradeoff between the recall of an ANN algorithm and its time/space resource consumption.
-
-Valkey-search supports the [Hierarchical Navigable Small Worlds](https://en.wikipedia.org/wiki/Hierarchical_navigable_small_world) (HNSW) ANN algorithm as it provides the best performance at the highest levels of recall demanded by real-time applications. The HNSW algorithm has (`O(log N)`) time complexity and gives the developer three parameters to adjust the CPU and memory consumption vs recall. The relationship between these parameters and the resulting operation latency and recall is complex and data dependent. It is recommended that developers test with data that closely approximates production environments.
-
-
-## Hybrid Query Support
-
-Valkey-search query operations are not limited to just vector searching. Documents can contain additional metadata that can be used to enhance searches. Two types of metadata are currently supported: Numeric and Tag. Numeric metadata supports range queries, i.e., you can include or exclude documents with metadata in the particular range. Tag metadata is an enumerated list of words. Tag searches can be done with an exact word match or a prefix match (trailing wild card).
-
-Hybrid queries are vector query operations which have been augmented with a filter, constructed from the numeric and tag searching operators combined with the usual logical operators `AND`, `OR`, and `NOT.` 
-
-Hybrid queries are particularly powerful for real-world applications, where a mix of vector and non-vector attributes defines the relevance of results. For example, a numeric field could be used as a timestamp, meaning that search operations could be automatically confined to a particular period of time. Another example would be to use a tag field to indicate a language.
-
-
-### Query Execution
-
-When executing hybrid queries, valkey-search automatically selects from one of two strategies as part of the query execution planning phase. This is done by breaking down the query filter into predicates and estimating the selectivity of each predicate to estimate the least expensive execution strategy. 
-
-**Pre-filtering:** This algorithm has two sequential steps: first, a temporary dataset is created using only the documents which pass the filter expression, then a linear KNN similarity search of is performed on the temporary dataset. Pre-filtering is particularly effective for high selectivity queries, i.e., the filter significantly narrows down the dataset.
-
-**Inline-filtering:** In this algorithm, filtering is done *during* the vector search itself. As the HNSW search graph is traversed, each candidate document is tested against the filter criteria before being added to the result set. This method is best suited for cases where the filter isn’t highly selective, i.e., the filter matches a large amount of the dataset. 
-
-
-## High Availability
-
-Valkey-search is built on top of Valkey, leveraging its primary/replica-based architecture to provide high availability (HA). Diagram #2 shows a typical configuration of one primary and three replicas. In the event of a primary node failure, the system automatically promotes one of the replicas to become the new primary.
-
-Clients must send data mutation (write) commands to the primary node which are executed and then automatically asynchronously transmitted to each replica. Clients can send data read operations to any node in the cluster, recognizing that reading from a replica is eventually consistent.
-
-When valkey-search is used, each node, whether a primary or a replica, builds and maintains its own indexes. No additional traffic on the replication channel is generated for index maintenance. Search query operations sent to a replica will be executed against its indexes, reflecting the historical point in time of the data within that node.
-
-![High availability](images/ha.png)
-
-## Scaling
-
-Valkey-search supports all three methods of scaling: horizontal, vertical and replicas. When scaling for capacity, valkey-search behaves just like regular Valkey, i.e., increasing the memory of individual nodes (vertical scaling) or increasing the number of nodes (horizontal scaling) will increase the overall capacity.
-
-However, from a performance perspective, valkey-search behaves very differently from regular Valkey. The multi-threaded implementation of valkey-search means that additional CPUs yield up to linear increases in both query and ingestion throughput. Horizontal scaling yields linear increases in ingestion throughput but may provide little to no benefit on query throughput. If additional query throughput is required, scaling through replicas or additional CPUs is required.
-
-
-![Scaling](images/cluster.png)
-
-
-## Snapshots
-
-Valkey’s point-in-time RDB snapshotting mechanism is a key component for maintaining high availability, zero downtime, and minimal operational overhead. Beyond persistence and loading from disk snapshots, RDB plays a crucial role in full-sync operations, where a primary node synchronizes its in-memory data with a replica node in an HA setup. Full-sync is commonly triggered when a new replica joins the cluster but may also occur if a secondary falls too far behind due to prolonged lag.
-
-Valkey-search enhances the Valkey snapshotting mechanism to include index definitions and vector indexes and is built for resilience and efficiency:
-
-**Fast Turn-up:** New nodes must become fully operational in minimal time. While rebuilding an index on startup is an option, ingesting a large volume of vectors can be prohibitively slow, delaying system readiness. A snapshot captures not only the index metadata but also the vector index content which significantly reduces downtime and operational burden.
-
-**Backward/Forward Compatibility:** Cluster upgrades or downgrades often result in a temporary mix of node versions. valkey-search seamlessly handles such scenarios with a serialization format based on [Protocol Buffers (Protobuf)](https://en.wikipedia.org/wiki/Protocol_Buffers), ensuring both backward and forward compatibility. This simplifies version transitions and reduces the chance of costly re-indexing.
-
-
-## RediSearch API Compatibility
-
-Valkey-search implements a subset of RediSearch’s functionality, with compatibility across key vector search APIs. This enables integration with most existing RediSearch client libraries, allowing you to continue using familiar tools. Developers already experienced with RediSearch can adopt valkey-search with minimal changes, as the vector search API remains largely consistent. 
-
-
-## Performance & Low Latency
-
-Valkey-search was designed from the ground up as an in-memory secondary index. A multi-threaded architecture optimizes query and mutation processing with minimal thread contention, enabling near-linear vertical scalability.
-
-At its core, valkey-search’s threading architecture follows a common design pattern: a worker thread pool combined with task queues. It employs advanced synchronization mechanisms to maintain index consistency while minimizing contention among worker threads. By time-slicing CPU access between read and write operations, the system minimizes locks on the read path, delivering high performance and consistently low search latency. 
-
-Valkey-search’s HNSW implementation is based on the OSS project [HNSWLib](https://github.com/nmslib/hnswlib). While HNSWLib is well-regarded for its speed, we have enhanced its performance and efficiency for our use case. These improvements include better SIMD utilization, promotion of CPU cache efficiency, memory utilization and more.
-
-
-## Future Enhancements
-
-This first release of valkey-search focuses on vector search, but it is designed as a general-purpose indexing subsystem. Future releases will extend both the available data types as well as provide post-query data processing facilities.
-
-
-
-## Get Involved
-
-Valkey-search is open source and ready for you to explore. Whether you're building cutting-edge AI applications or integrating vector search into existing systems, we invite you to try it out. The easiest way to get started is by visiting the [GitHub repository](https://github.com/valkey-io/valkey-search), where you'll find setup instructions, documentation, and examples.
-
-We welcome contributions of all kinds - code, documentation, testing, and feedback. Join the community, file issues, open pull requests, or suggest improvements. Your involvement helps make valkey-search better for everyone.
-
-Ready to dive in? Clone the repo, fire up the [dev container](https://hub.docker.com/r/valkey/valkey-bundle), and start building high-performance vector search with valkey-search.
++++
+title= "Introducing Vector Search To Valkey" 
+description = "Learn how to build high-performance, reliable vector similarity search with valkey-search." 
+date= 2025-07-07 01:01:01 
+authors= ["yairgott", "allenss"] 
+[taxonomies]
+blog_type = ["Announcements"]
+[extra]
+featured = false
+featured_image = "/assets/media/featured/random-08.webp"
++++
+
+[Valkey-search](https://github.com/valkey-io/valkey-search) is an official Valkey module that introduces native vector similarity search capabilities. It allows you to efficiently create indexes and search through billions of vectors stored in your Valkey instances. Whether you're building semantic search, fraud detection systems, or conversational AI experiences, Valkey-Search provides a flexible and high-performance foundation for your application. It is compatible with Valkey versions 8.1.1 and above and is BSD-3-Clause licensed.
+
+
+In this blog, you'll learn how valkey-search works, explore key use cases it supports, understand its architecture and indexing model, and see how to integrate it into your own applications. You'll also gain insight into how it scales, ensures high availability, and supports hybrid queries that combine vector similarity with structured filtering.
+
+
+## Semantic Search
+
+The ability of AI models to extract semantic meaning enables new classes of search algorithms, collectively known as semantic search. An AI model can process an input and convert it into a single high-dimension numeric vector – known as an embedding. Inputs with similar meaning will have similar embeddings. Semantic search is the process of converting a query into its embedding and searching a database of embeddings to find the most similar results. 
+
+The semantic search process can be divided into three phases:
+
+1. **Encode**:  First, convert your input data into searchable units known as chunks. Chunking strategies are data-type specific. For example, with text one well-known chunking algorithm is to make each sentence be a chunk. Then use an AI model to  generate an embedding for each chunk. The specific AI model used for encoding depends on the data type and specific use case. Many AI models are available as a service, such as Google Cloud’s [VertexAI](https://cloud.google.com/vertex-ai) or AWS’ [Bedrock](https://aws.amazon.com/bedrock/), simplifying the embedding generation process.
+
+2. **Index**: Store the generated embeddings along with any associated metadata in valkey-search. Each stored item is indexed with a primary key and a set of attributes spanning multiple modalities, for example, tags (categories),  numbers (pricing), etc.
+* **Search**: The query is converted into an embedding using the same AI model as was used in the **Encode** step above. This embedding is used with the vector search capability of valkey-search to locate the most similar vectors. The located vectors correspond to chunks of the original input that have the most similar meaning to the query.
+
+![Semantic search phases](images/highlevel-flow.png)
+
+## Use-Cases Where valkey-search Shines
+
+Valkey-search’s ability to search billions of vectors with millisecond latencies makes it ideal for real-time applications such as:
+
+* **Personalized Recommendations** – Deliver instant, highly relevant recommendations based on real-time user interactions.
+* **Fraud Detection & Security** – Identify anomalies and suspicious activity with ultra-fast similarity matching.
+* **Conversational AI & Chatbots** – Enhance response accuracy and relevance by leveraging rapid vector-based retrieval.
+* **Image & Video Search** – Enable multimedia search through real-time similarity detection.
+* **GenAI & Semantic Search** – Power advanced AI applications with efficient vector retrieval for natural language understanding.
+
+## Valkey-Search Architecture Overview
+
+Valkey-search allows the creation of any number of named indexes. A valkey-search index can be thought of like a relational database table where a row is a Valkey key and a column is an attribute (field) within that key. Each index is defined to cover a portion of the Valkey keyspace and some list of attributes within those keys. Any mutation of a key within the scope of an index synchronously updates that index before that mutation command is acknowledged. Query operations can be performed on a single index, returning the located key names and optionally their contents.
+
+Indexes can be constructed over hash or JSON keys. For hash keys, the indexable attributes of the keys are just the hash fields. For JSON keys, the indexable attributes are identified using the JSON path notation. Regardless of key type, an index can have any number of attributes. Each attribute of an index is declared with a type and sometimes type-specific modifiers. Currently, three index attribute types are supported: Vector, Numeric and Tag. 
+
+### Index Creation
+
+The [FT.CREATE](https://valkey.io/commands/ft.create/) command is used to create a new, empty index. This causes the system to automatically initiate an asynchronous background process to scan the keyspace for keys that belong in the index in a process called backfilling. The backfill process runs until the entire keyspace has been scanned for the index. The `backfill_in_progress` and `backfill_complete_percent` fields of the [FT.INFO](https://valkey.io/commands/ft.info/) command result can be used to monitor the progress of the backfill. Once the backfill is complete it need not be executed against the index again.
+
+During the backfill process, query and ingestion operations proceed normally. Queries will be executed against the current state of the index. Newly mutated data will be placed into the index as usual and remain unaffected by the backfill process. Thus after the creation of an index – while it is backfilling – the application can be certain that queries will contain the results of all data that has been ingested *after* the creation of the index and *some* of the data ingested *before*. Once the backfill has completed then queries will consider all data covered by the index.
+
+In cluster mode, the `FT.CREATE` command can be sent to any primary node of the cluster and the system will automatically distribute the new index definition to all cluster members. The distribution is done using a combination of the cluster bus and direct gRPC communication between nodes. In the rare case where the distribution machinery detects an inconsistency between nodes, a last-writer-wins (LWW) collision resolution protocol is invoked to enable eventual cluster-wide consistency. 
+
+
+## Vector Search Algorithms
+
+Valkey-search offers K Nearest Neighbor (KNN) search. Applications supply a reference vector and request that the system locate the K vectors which are closest to the reference vector using the a specified distance function (L2, IP or cosine). KNN searching is a classic problem which lacks an efficient exact ideal solution. Valkey-search addresses this problem by providing two different algorithms that the developer can select from:
+
+The first algorithm performs an exhaustive linear search, providing exactly correct answers but with a run-time that may be intolerable on large data sets.
+
+The second algorithm addresses this problem by compromising on accuracy in exchange for shorter run-times. In other words, it runs very fast but may not deliver exactly the correct answer. This type of algorithm is often known as Approximate Nearest Neighbor (ANN). The term "recall" is used to measure the quality of an ANN algorithm result and is expressed as the ratio (or percentage) of the found answers to the correct answers, e.g., a recall of 0.95 (or 95%) means that for a search with K = 100, 95 of the correct answers were found. There is a tradeoff between the recall of an ANN algorithm and its time/space resource consumption.
+
+Valkey-search supports the [Hierarchical Navigable Small Worlds](https://en.wikipedia.org/wiki/Hierarchical_navigable_small_world) (HNSW) ANN algorithm as it provides the best performance at the highest levels of recall demanded by real-time applications. The HNSW algorithm has (`O(log N)`) time complexity and gives the developer three parameters to adjust the CPU and memory consumption vs recall. The relationship between these parameters and the resulting operation latency and recall is complex and data dependent. It is recommended that developers test with data that closely approximates production environments.
+
+
+## Hybrid Query Support
+
+Valkey-search query operations are not limited to just vector searching. Documents can contain additional metadata that can be used to enhance searches. Two types of metadata are currently supported: Numeric and Tag. Numeric metadata supports range queries, i.e., you can include or exclude documents with metadata in the particular range. Tag metadata is an enumerated list of words. Tag searches can be done with an exact word match or a prefix match (trailing wild card).
+
+Hybrid queries are vector query operations which have been augmented with a filter, constructed from the numeric and tag searching operators combined with the usual logical operators `AND`, `OR`, and `NOT.` 
+
+Hybrid queries are particularly powerful for real-world applications, where a mix of vector and non-vector attributes defines the relevance of results. For example, a numeric field could be used as a timestamp, meaning that search operations could be automatically confined to a particular period of time. Another example would be to use a tag field to indicate a language.
+
+
+### Query Execution
+
+When executing hybrid queries, valkey-search automatically selects from one of two strategies as part of the query execution planning phase. This is done by breaking down the query filter into predicates and estimating the selectivity of each predicate to estimate the least expensive execution strategy. 
+
+**Pre-filtering:** This algorithm has two sequential steps: first, a temporary dataset is created using only the documents which pass the filter expression, then a linear KNN similarity search of is performed on the temporary dataset. Pre-filtering is particularly effective for high selectivity queries, i.e., the filter significantly narrows down the dataset.
+
+**Inline-filtering:** In this algorithm, filtering is done *during* the vector search itself. As the HNSW search graph is traversed, each candidate document is tested against the filter criteria before being added to the result set. This method is best suited for cases where the filter isn’t highly selective, i.e., the filter matches a large amount of the dataset. 
+
+
+## High Availability
+
+Valkey-search is built on top of Valkey, leveraging its primary/replica-based architecture to provide high availability (HA). Diagram #2 shows a typical configuration of one primary and three replicas. In the event of a primary node failure, the system automatically promotes one of the replicas to become the new primary.
+
+Clients must send data mutation (write) commands to the primary node which are executed and then automatically asynchronously transmitted to each replica. Clients can send data read operations to any node in the cluster, recognizing that reading from a replica is eventually consistent.
+
+When valkey-search is used, each node, whether a primary or a replica, builds and maintains its own indexes. No additional traffic on the replication channel is generated for index maintenance. Search query operations sent to a replica will be executed against its indexes, reflecting the historical point in time of the data within that node.
+
+![High availability](images/ha.png)
+
+## Scaling
+
+Valkey-search supports all three methods of scaling: horizontal, vertical and replicas. When scaling for capacity, valkey-search behaves just like regular Valkey, i.e., increasing the memory of individual nodes (vertical scaling) or increasing the number of nodes (horizontal scaling) will increase the overall capacity.
+
+However, from a performance perspective, valkey-search behaves very differently from regular Valkey. The multi-threaded implementation of valkey-search means that additional CPUs yield up to linear increases in both query and ingestion throughput. Horizontal scaling yields linear increases in ingestion throughput but may provide little to no benefit on query throughput. If additional query throughput is required, scaling through replicas or additional CPUs is required.
+
+
+![Scaling](images/cluster.png)
+
+
+## Snapshots
+
+Valkey’s point-in-time RDB snapshotting mechanism is a key component for maintaining high availability, zero downtime, and minimal operational overhead. Beyond persistence and loading from disk snapshots, RDB plays a crucial role in full-sync operations, where a primary node synchronizes its in-memory data with a replica node in an HA setup. Full-sync is commonly triggered when a new replica joins the cluster but may also occur if a secondary falls too far behind due to prolonged lag.
+
+Valkey-search enhances the Valkey snapshotting mechanism to include index definitions and vector indexes and is built for resilience and efficiency:
+
+**Fast Turn-up:** New nodes must become fully operational in minimal time. While rebuilding an index on startup is an option, ingesting a large volume of vectors can be prohibitively slow, delaying system readiness. A snapshot captures not only the index metadata but also the vector index content which significantly reduces downtime and operational burden.
+
+**Backward/Forward Compatibility:** Cluster upgrades or downgrades often result in a temporary mix of node versions. valkey-search seamlessly handles such scenarios with a serialization format based on [Protocol Buffers (Protobuf)](https://en.wikipedia.org/wiki/Protocol_Buffers), ensuring both backward and forward compatibility. This simplifies version transitions and reduces the chance of costly re-indexing.
+
+
+## RediSearch API Compatibility
+
+Valkey-search implements a subset of RediSearch’s functionality, with compatibility across key vector search APIs. This enables integration with most existing RediSearch client libraries, allowing you to continue using familiar tools. Developers already experienced with RediSearch can adopt valkey-search with minimal changes, as the vector search API remains largely consistent. 
+
+
+## Performance & Low Latency
+
+Valkey-search was designed from the ground up as an in-memory secondary index. A multi-threaded architecture optimizes query and mutation processing with minimal thread contention, enabling near-linear vertical scalability.
+
+At its core, valkey-search’s threading architecture follows a common design pattern: a worker thread pool combined with task queues. It employs advanced synchronization mechanisms to maintain index consistency while minimizing contention among worker threads. By time-slicing CPU access between read and write operations, the system minimizes locks on the read path, delivering high performance and consistently low search latency. 
+
+Valkey-search’s HNSW implementation is based on the OSS project [HNSWLib](https://github.com/nmslib/hnswlib). While HNSWLib is well-regarded for its speed, we have enhanced its performance and efficiency for our use case. These improvements include better SIMD utilization, promotion of CPU cache efficiency, memory utilization and more.
+
+
+## Future Enhancements
+
+This first release of valkey-search focuses on vector search, but it is designed as a general-purpose indexing subsystem. Future releases will extend both the available data types as well as provide post-query data processing facilities.
+
+
+
+## Get Involved
+
+Valkey-search is open source and ready for you to explore. Whether you're building cutting-edge AI applications or integrating vector search into existing systems, we invite you to try it out. The easiest way to get started is by visiting the [GitHub repository](https://github.com/valkey-io/valkey-search), where you'll find setup instructions, documentation, and examples.
+
+We welcome contributions of all kinds - code, documentation, testing, and feedback. Join the community, file issues, open pull requests, or suggest improvements. Your involvement helps make valkey-search better for everyone.
+
+Ready to dive in? Clone the repo, fire up the [dev container](https://hub.docker.com/r/valkey/valkey-bundle), and start building high-performance vector search with valkey-search.

From 02971075acc18523f1750972973db443370c4d5f Mon Sep 17 00:00:00 2001
From: Madelyn Olson <madelyneolson@gmail.com>
Date: Tue, 7 Apr 2026 12:54:00 -0700
Subject: [PATCH 6/7] Remove trailing line

Signed-off-by: Madelyn Olson <madelyneolson@gmail.com>
---
 config.toml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/config.toml b/config.toml
index 7b394410..84a07cbb 100644
--- a/config.toml
+++ b/config.toml
@@ -57,5 +57,3 @@ publish_hold = [
     "/topics/rdd/"
 ]
 
-
-

From 3b815d6d928b6ffc0e7a837cd541b5fb8775e5b8 Mon Sep 17 00:00:00 2001
From: Madelyn Olson <madelyneolson@gmail.com>
Date: Tue, 7 Apr 2026 12:55:28 -0700
Subject: [PATCH 7/7] Use DOMContentLoaded instead of IIFE for blog filter
 script

Signed-off-by: Madelyn Olson <madelyneolson@gmail.com>
---
 templates/blog.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/templates/blog.html b/templates/blog.html
index 8f1f5869..378b2e8f 100644
--- a/templates/blog.html
+++ b/templates/blog.html
@@ -36,7 +36,7 @@ <h2><a href="{{ page.permalink }}">{{ page.title  | safe}}</a></h2>
   </ul>
   <p class="blog-no-results" hidden>No posts match this filter.</p>
   <script>
-  (function() {
+  document.addEventListener('DOMContentLoaded', function() {
     var select = document.getElementById('blog-type-select');
     if (!select) return;
     var items = document.querySelectorAll('.blog-list li');
@@ -66,7 +66,7 @@ <h2><a href="{{ page.permalink }}">{{ page.title  | safe}}</a></h2>
       applyFilter(type);
       history.replaceState(null, '', type === 'All' ? location.pathname + location.search : '#' + encodeURIComponent(type));
     });
-  })();
+  });
   </script>
 {% endblock main_content %}