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Cosmosdb Best Practices

ClineOfficialCreated by cosmosdb-agent-kitDistributed by Cline

  • Skill
  • Guidelines
  • Software development
  • MIT
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From the author

Azure Cosmos DB performance optimization and best practices guidelines for NoSQL, partitioning, queries, SDK usage, and vector search. Use when writing, reviewing, or refactoring code that interacts with Azure Cosmos DB, designing data models, optimizing queries, or implementing high-performance database operations.
— the description by cosmosdb-agent-kit

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured set of 73+ guidelines for working with Azure Cosmos DB. It covers essential topics including data modeling, partition key design, query optimization, SDK usage, indexing, throughput scaling, global distribution, monitoring, design patterns, and vector search. The content is organized into modular rule files to assist developers in writing, reviewing, and refactoring code that interacts with the database.

What it does

  • Provides specific rules for designing efficient data models and choosing partition keys.
  • Offers guidance on optimizing queries and implementing SDK patterns.
  • Details strategies for indexing, throughput management, and global distribution.
  • Includes best practices for implementing vector search and RAG patterns.
  • Supplies diagnostic and monitoring recommendations for database operations.
  • Organizes content into categorized rule files for targeted retrieval.

When to use it

  • When designing data models or selecting partition keys for Cosmos DB.
  • When writing or refactoring application code that uses the Cosmos DB SDK.
  • When optimizing query performance or indexing strategies.
  • When configuring global distribution, throughput, or scaling settings.
  • When implementing vector search or advanced design patterns.

Advantages

  • Modular structure allows for loading only relevant rules for specific tasks.
  • Covers a wide range of operational and architectural concerns.
  • Provides clear examples of incorrect and correct implementation patterns.
  • Prioritizes rules by impact to guide development decisions.

Usage examples

“How should I choose a partition key for my Cosmos DB container?”
The agent will retrieve rules from the partition- prefix to explain high-cardinality, write distribution, and query alignment.
“What are the best practices for using the Cosmos DB SDK?”
The agent will load rules from the sdk- prefix to provide guidance on singleton client usage, async APIs, and retry logic.
“How do I optimize my data model for read-heavy workloads?”
The agent will reference model- prefixed rules to explain denormalization and embedding strategies.
“What should I consider when implementing vector search in Cosmos DB?”
The agent will access vector- prefixed rules to explain embedding policies, index types, and similarity search functions.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/cline/cosmosdb-best-practices/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip cosmosdb-best-practices.zip -d .agents/skills

Documentation by cosmosdb-agent-kit

Azure Cosmos DB Best Practices

Comprehensive performance optimization guide for Azure Cosmos DB applications, containing 73+ rules across 10 categories, prioritized by impact to guide automated refactoring and code generation.

73 individual rule files are in the rules/ directory, one file per rule, synced from the AzureCosmosDB/cosmosdb-agent-kit. Load only the relevant rule file(s) when answering a question — do NOT load all files at once.

When to Apply

Reference these guidelines when:

  • Designing data models for Cosmos DB

  • Choosing partition keys

  • Writing or optimizing queries

  • Implementing SDK patterns

  • Reviewing code for performance issues

  • Configuring throughput and scaling

  • Building globally distributed applications

  • Implementing vector search and RAG patterns

Rule Index

Rules are grouped by category prefix. For a given question, load files matching the relevant prefix (e.g., model-*.md for data modeling, sdk-*.md for SDK usage). See rules/_sections.md for category descriptions.

1. Data Modeling — CRITICAL (prefix: model-)
2. Partition Key Design — CRITICAL (prefix: partition-)
3. Query Optimization — HIGH (prefix: query-)
4. SDK Best Practices — HIGH (prefix: sdk-)
5. Indexing Strategies — MEDIUM-HIGH (prefix: index-)
6. Throughput & Scaling — MEDIUM (prefix: throughput-)
7. Global Distribution — MEDIUM (prefix: global-)
8. Monitoring & Diagnostics — LOW-MEDIUM (prefix: monitoring-)
9. Design Patterns — HIGH (prefix: pattern-)
10. Vector Search — HIGH (prefix: vector-)

How to Use

Each rule is a separate file under rules/. When answering a Cosmos DB question, read only the relevant rule file(s) based on the prefix matching the topic:

  • Data modeling question → read rules/model-*.md files

  • Partition key question → read rules/partition-*.md files

  • SDK/client question → read rules/sdk-*.md files

  • etc.

Each rule file contains:

  • Brief explanation of why it matters

  • Incorrect code example with explanation

  • Correct code example with explanation

  • Additional context and references

Source: AzureCosmosDB/cosmosdb-agent-kit

Files

rules/query-pagination.md

title: Use Continuation Tokens for Pagination impact: HIGH impactDescription: enables efficient large result sets tags: query, pagination, continuation-token, performance

Use Continuation Tokens for Pagination

Use continuation tokens to paginate through large result sets efficiently. Never use OFFSET/LIMIT for deep pagination — it is a common anti-pattern with severe performance implications.

⚠️ OFFSET/LIMIT Anti-Pattern

OFFSET/LIMIT is one of the most common and costly Cosmos DB anti-patterns. The RU cost of OFFSET scales linearly with the offset value because Cosmos DB must read and discard all skipped documents:

PageOFFSETDocuments ScannedDocuments ReturnedRelative RU Cost
101001001x
109001,00010010x
1009,90010,000100100x
1,00099,900100,0001001,000x

This pattern is especially dangerous in leaderboard and feed scenarios where users page through large result sets.

Use OFFSET/LIMIT only when:

  • The total result set is small (< 1,000 items)

  • You need random access to a specific page (rare)

  • Deep pagination is impossible (e.g., top 100 only)

Incorrect (OFFSET/LIMIT for pagination):

// ❌ Anti-pattern: OFFSET increases cost linearly with page number
public async Task<List<Product>> GetProductsPage(int page, int pageSize)
{
    // Page 1: Skip 0, Page 100: Skip 9900
    var offset = (page - 1) * pageSize;
    
    // OFFSET must scan and discard all previous items!
    var query = $"SELECT * FROM c ORDER BY c.name OFFSET {offset} LIMIT {pageSize}";
    
    var results = await container.GetItemQueryIterator<Product>(query).ReadNextAsync();
    return results.ToList();
    
    // Page 1: Scans 100 items
    // Page 100: Scans 10,000 items, returns 100
    // RU cost grows linearly with page depth!
}

Correct (continuation token pagination):

public class PagedResult<T>
{
    public List<T> Items { get; set; }
    public string ContinuationToken { get; set; }
    public bool HasMore => !string.IsNullOrEmpty(ContinuationToken);
}

public async Task<PagedResult<Product>> GetProductsPage(
    int pageSize, 
    string continuationToken = null)
{
    var query = new QueryDefinition("SELECT * FROM c ORDER BY c.name");
    
    var options = new QueryRequestOptions
    {
        MaxItemCount = pageSize  // Items per page
    };
    
    var iterator = container.GetItemQueryIterator<Product>(
        query,
        continuationToken: continuationToken,  // Resume from last position
        requestOptions: options);
    
    var response = await iterator.ReadNextAsync();
    
    return new PagedResult<Product>
    {
        Items = response.ToList(),
        ContinuationToken = response.ContinuationToken  // For next page
    };
    
    // Every page costs the same RU regardless of depth!
}

// Usage in API
[HttpGet("products")]
public async Task<IActionResult> GetProducts(
    [FromQuery] int pageSize = 20,
    [FromQuery] string continuationToken = null)
{
    // Decode token if passed as query param (URL-safe encoding)
    var token = continuationToken != null 
        ? Encoding.UTF8.GetString(Convert.FromBase64String(continuationToken))
        : null;
    
    var result = await GetProductsPage(pageSize, token);
    
    // Encode token for URL safety
    var nextToken = result.ContinuationToken != null
        ? Convert.ToBase64String(Encoding.UTF8.GetBytes(result.ContinuationToken))
        : null;
    
    return Ok(new { result.Items, NextPage = nextToken });
}
// Streaming through all results
public async IAsyncEnumerable<Product> GetAllProducts()
{
    string continuationToken = null;
    
    do
    {
        var page = await GetProductsPage(100, continuationToken);
        
        foreach (var product in page.Items)
        {
            yield return product;
        }
        
        continuationToken = page.ContinuationToken;
    }
    while (continuationToken != null);
}
⚠️ Unbounded Query Anti-Pattern

Fetching all results without any pagination is even worse than OFFSET/LIMIT. This is commonly seen when developers skip pagination entirely, assuming result sets are small. At scale, unbounded queries cause:

  • Excessive RU consumption — reading thousands of documents in one call

  • Timeouts — queries exceeding the 5-second execution limit

  • Memory pressure — loading all results into memory

  • Cascading failures — high RU consumption triggers 429 throttling for other operations

// ❌ Anti-pattern: No pagination — returns ALL matching documents
public List<Task> getTasksByProject(String tenantId, String projectId) {
    String query = "SELECT * FROM c WHERE c.tenantId = @tenantId " +
                   "AND c.type = 'task' AND c.projectId = @projectId";
    SqlQuerySpec spec = new SqlQuerySpec(query,
        Arrays.asList(new SqlParameter("@tenantId", tenantId),
                      new SqlParameter("@projectId", projectId)));
    // Returns ALL tasks — at 500 tasks/project this is wasteful,
    // at 50,000 tasks/project this causes timeouts
    return container.queryItems(spec, new CosmosQueryRequestOptions(), Task.class)
        .stream().collect(Collectors.toList());
}

// ✅ Correct: Return paginated results with continuation token
public PagedResult<Task> getTasksByProject(
        String tenantId, String projectId,
        int pageSize, String continuationToken) {
    String query = "SELECT * FROM c WHERE c.tenantId = @tenantId " +
                   "AND c.type = 'task' AND c.projectId = @projectId " +
                   "ORDER BY c.createdAt DESC";
    CosmosQueryRequestOptions options = new CosmosQueryRequestOptions();
    options.setMaxBufferedItemCount(pageSize);
    // Use iterableByPage for continuation token support
    CosmosPagedIterable<Task> results = container.queryItems(
        new SqlQuerySpec(query, params), options, Task.class);
    // Process first page only, return continuation token for next page
}

Rule of thumb: If a query can return more than 100 items, it must use pagination.

Reference: Pagination in Azure Cosmos DB

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

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Attribution and licence

Created by
cosmosdb-agent-kit
Publisher
Cline (Official)
Distributed by
Cline
Source
cline/skills · skills/cosmosdb-best-practices · commit 2637846
Licence
MIT License (redistribution allowed)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by cosmosdb-agent-kit or Cline.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download cosmosdb-best-practices.zip