---
title: 'Databend Monthly Report (August 2025)'
date: 2025-09-10
slug: category-product/monthly-August2025
cover_url: 'august2025-cover.png'
description: 'This month: We delivered 23x faster similarity search with HNSW acceleration, making AI workloads production-ready...'
tags: [databend, monthly]
category: Product
authors:
  - DatabendLabs
---

# Databend Monthly Report: August 2025

Hey Databend community! 🚀

August was all about **vector indexing**. We delivered **23x faster similarity search** with HNSW acceleration, making AI workloads production-ready. Combined with our existing structured and JSON capabilities, **Databend is now a complete multi-modal data warehouse**.

{/* truncate */}

## By the Numbers

**15+ new features**, **20+ bug fixes**, **15+ performance optimizations**. The highlight: vector indexing that makes AI applications blazing fast.

## Monthly Highlights

### 🔥 **Major Features**

- **Vector Indexing with HNSW** - 23x faster similarity search
- **Time slice functions** - Advanced temporal data analysis
- **Enhanced JSON5 parsing** - More flexible JSON processing

### ⚡ **Performance & Stability**

- **Stack overflow prevention** - Fixed CTE and PhysicalPlan recursion issues
- **Memory management** - Structured spill configuration for large operations
- **Meta service optimization** - 40% reduction in service pressure
- **Vector index reliability** - Fixed data loss during refresh operations

## Game-Changing: Vector Search

### 📊 **23x Performance Improvement**

**Before:**

```sql
SELECT title, cosine_distance(embedding, :query) as score
FROM documents ORDER BY score LIMIT 10;
-- 8.2 seconds, full table scan
```

**After (with HNSW index):**

```sql
-- Same query, same results
SELECT title, cosine_distance(embedding, :query) as score
FROM documents ORDER BY score LIMIT 10;
-- 0.35 seconds, index-accelerated
```

## Vector Search Made Simple

Getting started with production-ready semantic search:

### 1. Create Table with Vector Index

```sql
CREATE TABLE products (
    id INT,
    name VARCHAR,
    embedding VECTOR(1024),
    -- Automatic 23x speedup with HNSW index
    VECTOR INDEX idx(embedding) distance='cosine'
);
```

### 2. Insert Data

```sql
INSERT INTO products VALUES
(1, 'Wireless Headphones', [0.1, 0.2, ...]::VECTOR(1024)),
(2, 'Bluetooth Speaker', [0.3, 0.1, ...]::VECTOR(1024));
```

### 3. Search

```sql
-- Find similar products instantly
SELECT name FROM products
ORDER BY cosine_distance(embedding, :search_vector)
LIMIT 5;
```

**That's it. Sub-second similarity search for millions of vectors.**

### Supported Distance Functions

- **Cosine** - Best for text/semantic similarity
- **L2** - Best for image/visual similarity
- **L1** - Best for feature comparison

## What This Enables: Multi-Modal Data Warehouse

**Traditional approach:** Multiple separate systems

- Structured data → PostgreSQL
- JSON documents → Elasticsearch
- Vector embeddings → Pinecone
- Complex data pipelines to connect everything

**Databend approach:** One platform for everything

- **Structured data** - World-class columnar analytics
- **JSON documents** - 3x faster with automatic virtual columns (July)
- **Vector embeddings** - 23x faster with HNSW indexing (August)

**All in one SQL platform. No data movement. No system integration complexity.**

This means you can build complete AI applications - recommendation engines, semantic search, RAG systems - using just SQL queries that work across all your data types seamlessly.

---

**Databend: The multi-modal data warehouse** - built for the AI era.

Try it today: https://databend.com

_The Databend Team_