Optimizing AWS Lambda Functions for DynamoDB Write Efficiency

Efficient serverless infrastructure is less about flashy tricks and more about correctness under load. Write-heavy Lambda workloads targeting DynamoDB are a notorious source of hidden costs and transient latency.

Typical post-deploy spike:

ValidationException: Throughput exceeds the current capacity of your table or index.

This isn’t just theory—mismanaged Lambda-to-DynamoDB integration burns money and introduces scaling bottlenecks. Address the pain, not the symptoms.

Batch Writes: Reduce RPC Chatter

Batching is fundamental. DynamoDB’s BatchWriteItem lets you insert or delete up to 25 items (or 16 MB) per request, drastically reducing network overhead.

Pattern: Aggregate records in memory, dispatch as a batch on flush.

Example (Node.js, aws-sdk v2.1370.0):

const AWS = require('aws-sdk');
const docClient = new AWS.DynamoDB.DocumentClient();

async function batchWriteRecords(tableName, records) {
  const putRequests = records.map(Item => ({ PutRequest: { Item } }));
  // Dynamically chunk: even if you get 170 records, still OK
  for (let i = 0; i < putRequests.length; i += 25) {
    const batch = putRequests.slice(i, i + 25);
    const params = { RequestItems: { [tableName]: batch } };
    let unprocessed = params;
    let retries = 0;

    while (Object.keys(unprocessed.RequestItems).length && retries < 4) {
      const result = await docClient.batchWrite(unprocessed).promise();
      unprocessed = result.UnprocessedItems && Object.keys(result.UnprocessedItems).length
        ? { RequestItems: result.UnprocessedItems }
        : { RequestItems: {} };
      retries++;
      if (retries > 1) await new Promise(r => setTimeout(r, 2 ** retries * 50));
    }

    if (Object.keys(unprocessed.RequestItems).length) {
      // Gotcha: consider DLQ or SQS backup here for failed writes
      throw new Error("DynamoDB batch write failed after retries.");
    }
  }
}

Note: You’ll occasionally see “UnprocessedItems”, especially during event spikes. Don’t skip retry logic.

IAM: Scope Down Aggressively

Lambda’s execution role needs only minimal rights. Overly broad IAM policies risk lateral movement in case of credential leak.

Minimal policy snippet (single-table, write-only):

{
  "Effect": "Allow",
  "Action": [
    "dynamodb:BatchWriteItem",
    "dynamodb:PutItem"
  ],
  "Resource": "arn:aws:dynamodb:REGION:ACCOUNT_ID:table/YourTableName"
}

Tip: Never grant wildcard "*". Also—if using KMS encryption on the table, add permission for kms:GenerateDataKey.

Table Capacity: Trade-offs

On-Demand: Useful for spiky workloads, pay-per-request model.
Provisioned: Stable traffic? Use provisioned with auto-scaling for predictable costs and better control of throttle rates.

Critically, set a CloudWatch alarm on ThrottledRequests either way. Operators neglecting this metric will end up blind to hard-to-debug write drops.

Data Model and Payload: Keep Writes Lean

Every item attribute adds bytes, and bytes add latency/cost.

• Avoid large binary/blobs in hot tables. Use S3 for >256KB payloads.
• Restrict attribute set: Only store what needs to be queried. DynamoDB charges for provisioned capacity and storage.
• Prefer flat structures: Arrays and deeply nested maps slow scans and inflate item size.

Known issue: Removing attributes from items doesn’t reclaim storage instantly. Table bloat lingers during high-churn workloads unless regularly compacted.

Conditional Writes

Minimize use of ConditionExpression unless strict idempotence is required. Each condition is an extra read hidden among the writes.

Lambda Cold Starts: They Happen, Hide the Cost

Lambda cold start latency is nontrivial (100-500ms typical for Node.js 18.x, us-east-1).

Mitigation:

• Place SDK clients outside the handler to maximize connection reuse.
• Use provisioned concurrency only on latency-critical endpoints—expensive, but effective.

Handler skeleton:

const AWS = require('aws-sdk');
const docClient = new AWS.DynamoDB.DocumentClient();

exports.handler = async (event) => {
  // Avoid redeclaring or reinitializing AWS clients here
};

Note: For noisy deployments (CI/CD every 10 minutes), expect frequent cold starts unless using provisioned concurrency.

Error Handling: Backoff and Escalate

Transient DynamoDB write failures aren’t rare, especially during heavy traffic or region events. Always build retry logic around batchWrite, using exponential backoff.

Minimal retry snippet:

async function robustBatchWrite(params, attempt = 1) {
  // ...
  // See full batchWriteRecords example above for template
}

Non-obvious tip: When all retries fail, consider putting failed records onto a dedicated SQS DLQ. Don’t drop them blindly.

Practical Table: Lambda–DynamoDB Optimization Summary

Side note: some teams adopt EventBridge or SQS as ingest buffer and have Lambda batch from there into DynamoDB. This adds complexity, but hardens against spikes and preserves failing records for replay.

Imperfect but Operational

No optimization eliminates all bottlenecks—network jitter, regional throttles, or upstream slowdowns can always intrude. Engineers need to weigh code complexity (e.g., elaborate DLQ pipelines vs. rare lost records) according to SLA, traffic, and team capacity.

One last pointer: CloudWatch metrics on ConsumedWriteCapacityUnits and ThrottledRequests catch issues long before users notice. Watch them closely.

For code artifacts, a working starter set with these patterns is available on request.

— End of note.