How To Configure And Customize The Go SDK For Azure Cosmos DB

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Azure Cosmos DB is a globally distributed, multi-model NoSQL database built for high availability and low-latency workloads. When integrating Cosmos DB with applications written in Go, developers use the Azure Cosmos DB Go SDK, which provides a rich set of APIs for database and container operations, query execution, and configuration features such as retry policies, HTTP pipeline customization, OpenTelemetry integration, and query diagnostics.

This article offers a practical guide to configuring and customizing the Go SDK for Azure Cosmos DB, covering advanced features with hands-on examples.

Prerequisites

Before we begin, ensure you have the following:

  • A Cosmos DB account with SQL API enabled.

  • Go 1.18+ installed on your system.

  • A working Go project using Go modules.

  • The Azure Cosmos DB Go SDK: github.com/Azure/azure-sdk-for-go/sdk/data/azcosmos

Install the SDK with:

bash
go get github.com/Azure/azure-sdk-for-go/sdk/data/azcosmos

Connecting To Cosmos DB

First, let’s create a Cosmos DB client with the SDK.

go

package main

import (
“context”
“fmt”
“github.com/Azure/azure-sdk-for-go/sdk/data/azcosmos”
)

func createClient() (*azcosmos.Client, error) {
endpoint := “<YOUR_COSMOS_DB_ENDPOINT>”
key := “<YOUR_PRIMARY_KEY>”

cred, err := azcosmos.NewKeyCredential(key)
if err != nil {
return nil, err
}

client, err := azcosmos.NewClientWithKey(endpoint, cred, nil)
if err != nil {
return nil, err
}

return client, nil
}

Configuring Retry Policies

Retry policies help applications handle transient faults gracefully. Cosmos DB’s Go SDK uses a built-in exponential backoff strategy, which can be customized.

go

import "time"

func createClientWithRetryPolicy() (*azcosmos.Client, error) {
endpoint := “<YOUR_COSMOS_DB_ENDPOINT>”
key := “<YOUR_PRIMARY_KEY>”

cred, err := azcosmos.NewKeyCredential(key)
if err != nil {
return nil, err
}

options := &azcosmos.ClientOptions{
Retry: azcosmos.RetryOptions{
MaxRetries: 5,
RetryDelay: 200 * time.Millisecond,
MaxRetryDelay: 3 * time.Second,
},
}

client, err := azcosmos.NewClientWithKey(endpoint, cred, options)
if err != nil {
return nil, err
}

return client, nil
}

This configuration will retry up to 5 times with a starting delay of 200ms, capped at 3 seconds. Customize these values depending on network conditions or workload tolerance.

Customizing the HTTP Pipeline

Azure SDKs use a modular pipeline approach for HTTP communication. This allows you to inject custom middleware into requests, e.g., logging, authentication, or metrics.

You can customize the pipeline by adding policies:

go
import (
"github.com/Azure/azure-sdk-for-go/sdk/azcore"
"github.com/Azure/azure-sdk-for-go/sdk/azcore/policy"
"log"
)type customLogPolicy struct{}func (clp *customLogPolicy) Do(req *policy.Request) (*http.Response, error) {
log.Printf(“Sending request to %s”, req.Raw().URL)
return req.Next()
}func createClientWithCustomPipeline() (*azcosmos.Client, error) {
endpoint := “<YOUR_COSMOS_DB_ENDPOINT>”
key := “<YOUR_PRIMARY_KEY>”cred, err := azcosmos.NewKeyCredential(key)
if err != nil {
return nil, err
}customPolicy := &customLogPolicy{}

options := &azcosmos.ClientOptions{
PerCallPolicies: []policy.Policy{customPolicy},
}

client, err := azcosmos.NewClientWithKey(endpoint, cred, options)
if err != nil {
return nil, err
}

return client, nil
}

This adds a logging policy to print outgoing request URLs. You can similarly add authentication headers or retry logic.

Implementing OpenTelemetry Tracing

OpenTelemetry provides observability into the execution of your distributed applications. To enable tracing in Cosmos DB SDK operations:

  1. Set up OpenTelemetry and configure a tracer provider.

  2. Pass context.Context with a span into SDK calls.

Here’s a basic setup:

go
import (
"go.opentelemetry.io/otel"
"go.opentelemetry.io/otel/exporters/stdout/stdouttrace"
"go.opentelemetry.io/otel/sdk/resource"
sdktrace "go.opentelemetry.io/otel/sdk/trace"
"go.opentelemetry.io/otel/trace"
)func initTracer() (trace.Tracer, error) {
exporter, err := stdouttrace.New(stdouttrace.WithPrettyPrint())
if err != nil {
return nil, err
}tp := sdktrace.NewTracerProvider(
sdktrace.WithBatcher(exporter),
sdktrace.WithResource(resource.Default()),
)otel.SetTracerProvider(tp)return otel.Tracer(“example.com/azure-cosmos-go”), nil
}

Then, instrument the SDK call:

go
func executeTracedOperation(client *azcosmos.Client, tracer trace.Tracer) {
ctx, span := tracer.Start(context.Background(), "ExecuteCosmosQuery")
defer span.End()// Call Cosmos DB SDK with traced context
databaseName := “mydb”
containerName := “items”containerClient, err := client.NewContainer(databaseName, containerName)
if err != nil {
span.RecordError(err)
return
}query := azcosmos.NewQuery(“SELECT * FROM c”)
pager := containerClient.NewQueryItemsPager(query, nil)for pager.More() {
page, err := pager.NextPage(ctx)
if err != nil {
span.RecordError(err)
break
}
fmt.Println(“Received items:”, len(page.Items))
}
}

Tracing allows you to analyze slow queries, retry behavior, and failure points.

Analyzing Detailed Query Metrics

Cosmos DB supports query diagnostics which can be used to inspect execution latency, request charge, and other performance metrics.

You can enable metrics collection during queries:

go
func queryWithMetrics(containerClient *azcosmos.ContainerClient) error {
ctx := context.Background()query := azcosmos.NewQuery(“SELECT * FROM c WHERE c.type = @type”)
params := []azcosmos.QueryParameter{
{Name: “@type”, Value: “user”},
}
options := &azcosmos.QueryOptions{
QueryParameters: params,
}pager := containerClient.NewQueryItemsPager(query, options)for pager.More() {
resp, err := pager.NextPage(ctx)
if err != nil {
return err
}fmt.Printf(“Request Charge: %.2f RUs\n”, resp.RequestCharge)
fmt.Printf(“Diagnostics: %v\n”, resp.Diagnostics)
}return nil
}

You can extract diagnostics to troubleshoot query slowness, such as indexing delays or partition fan-out.

Combining All Together in a Sample App

A complete application might look like this:

go
func main() {
tracer, err := initTracer()
if err != nil {
log.Fatal(err)
}client, err := createClientWithRetryPolicy()
if err != nil {
log.Fatal(err)
}containerClient, err := client.NewContainer(“mydb”, “items”)
if err != nil {
log.Fatal(err)
}// Execute traced operation
executeTracedOperation(client, tracer)// Analyze metrics
err = queryWithMetrics(containerClient)
if err != nil {
log.Fatal(err)
}
}

Conclusion

Configuring and customizing the Azure Cosmos DB Go SDK is essential for building scalable, resilient, and observable cloud-native applications. As application requirements grow in complexity—whether due to increasing traffic, diverse data patterns, or operational constraints—developers must move beyond basic SDK usage and embrace advanced customization options offered by Azure Cosmos DB’s Go SDK.

The first step in this journey is implementing custom retry policies, which are critical in cloud environments where transient faults are not just possible—they are expected. By tailoring retry strategies such as exponential backoff, max retry delays, and total retry attempts, developers can balance fault tolerance with performance, ensuring that temporary disruptions do not translate into system failures or degraded user experiences.

Equally important is the ability to customize the HTTP pipeline. This low-level capability provides a powerful hook for introducing cross-cutting concerns like structured logging, request auditing, performance profiling, and custom telemetry. By inserting custom middleware or policies into the HTTP transport stack, developers can enforce organizational standards, gain insights into internal SDK behaviors, and build secure, consistent networking layers.

OpenTelemetry tracing introduces a new dimension of observability. In modern distributed systems, understanding the flow of a request across service boundaries is critical. By embedding tracing into Cosmos DB interactions, developers can visualize latency bottlenecks, identify slow-running queries, and measure the downstream impact of infrastructure changes. This level of insight is particularly valuable in microservices architectures, where data calls to Cosmos DB may form just one part of a larger, interdependent transaction.

In addition to operational observability, query diagnostics and metrics analysis serve as the foundation for performance tuning and cost management. Cosmos DB is a consumption-based service, so every query, read, or write incurs a cost measured in Request Units (RUs). By inspecting diagnostics such as request charges, index utilization, partition fan-out, and retry metrics, developers can pinpoint inefficient queries and refactor them for better performance and lower cost. These metrics also help in capacity planning and proactively mitigating service limits.

When combined, these customizations foster a development culture rooted in resilience, efficiency, and observability. Applications that make intelligent use of retry policies are more reliable. Systems with custom pipelines and logging are easier to troubleshoot and maintain. Services instrumented with OpenTelemetry are far more transparent and debuggable. Workloads that are monitored using query diagnostics are easier to optimize and scale predictably.

Moreover, adopting these patterns positions your team for success in production environments. Whether you are operating at small scale or managing enterprise-level data across regions, these best practices ensure that your Cosmos DB integrations are robust, cost-effective, and aligned with your organization’s SRE and DevOps goals.

In conclusion, Azure Cosmos DB’s Go SDK is not just a simple client for accessing a NoSQL database—it is a powerful, extensible framework that can be molded to meet the demands of modern applications. By investing in its configuration and observability capabilities today, you equip your Go applications to perform reliably, scale effortlessly, and deliver outstanding user experiences tomorrow.