Error handling with DynamoDB

This section describes runtime errors and how to handle them. It also describes error messages and codes that are specific to Amazon DynamoDB. For a list of common errors that apply to all AWS services, see Access Management

Error components

When your program sends a request, DynamoDB attempts to process it. If the request is successful, DynamoDB returns an HTTP success status code (200 OK), along with the results from the requested operation.

If the request is unsuccessful, DynamoDB returns an error. Each error has three components:

• An HTTP status code (such as 400).

• An exception name (such as ResourceNotFoundException).

• An error message (such as Requested resource not found: Table: tablename not found).

The AWS SDKs take care of propagating errors to your application so that you can take appropriate action. For example, in a Java program, you can write try-catch logic to handle a ResourceNotFoundException.

If you are not using an AWS SDK, you need to parse the content of the low-level response from DynamoDB. The following is an example of such a response.

HTTP/1.1 400 Bad Request
x-amzn-RequestId: LDM6CJP8RMQ1FHKSC1RBVJFPNVV4KQNSO5AEMF66Q9ASUAAJG
Content-Type: application/x-amz-json-1.0
Content-Length: 240
Date: Thu, 15 Mar 2012 23:56:23 GMT

{"__type":"com.amazonaws.dynamodb.v20120810#ResourceNotFoundException",
"message":"Requested resource not found: Table: tablename not found"}

Transactional errors

For information on transactional errors, please see Transaction conflict handling in DynamoDB

Error messages and codes

The following is a list of exceptions returned by DynamoDB, grouped by HTTP status code. If OK to retry? is Yes, you can submit the same request again. If OK to retry? is No, you need to fix the problem on the client side before you submit a new request.

HTTP status code 400

An HTTP 400 status code indicates a problem with your request, such as authentication failure, missing required parameters, or exceeding a table's provisioned throughput. You have to fix the issue in your application before submitting the request again.

AccessDeniedException

Message: Access denied.

The client did not correctly sign the request. If you are using an AWS SDK, requests are signed for you automatically; otherwise, go to the Signature version 4 signing process in the AWS General Reference.

OK to retry? No

ConditionalCheckFailedException

Message: The conditional request failed.

You specified a condition that evaluated to false. For example, you might have tried to perform a conditional update on an item, but the actual value of the attribute did not match the expected value in the condition.

OK to retry? No

IncompleteSignatureException

Message: The request signature does not conform to AWS standards.

The request signature did not include all of the required components. If you are using an AWS SDK, requests are signed for you automatically; otherwise, go to the Signature Version 4 signing process in the AWS General Reference.

OK to retry? No

ItemCollectionSizeLimitExceededException

Message: Collection size exceeded.

For a table with a local secondary index, a group of items with the same partition key value has exceeded the maximum size limit of 10 GB. For more information on item collections, see Item collections in Local Secondary Indexes.

OK to retry? Yes

LimitExceededException

Message: Too many operations for a given subscriber.

There are too many concurrent control plane operations. The cumulative number of tables and indexes in the CREATING, DELETING, or UPDATING state cannot exceed 500.

OK to retry? Yes

MissingAuthenticationTokenException

Message: Request must contain a valid (registered) AWS Access Key ID.

The request did not include the required authorization header, or it was malformed. See DynamoDB low-level API.

OK to retry? No

ProvisionedThroughputExceededException

Message: You exceeded your maximum allowed provisioned throughput for a table or for one or more global secondary indexes. To view performance metrics for provisioned throughput vs. consumed throughput, open the Amazon CloudWatch console.

Example: Your request rate is too high. The AWS SDKs for DynamoDB automatically retry requests that receive this exception. Your request is eventually successful, unless your retry queue is too large to finish. Reduce the frequency of requests using Error retries and exponential backoff.

OK to retry? Yes

RequestLimitExceeded

Message: Throughput exceeds the current throughput limit for your account. To request a limit increase, contact AWS Support at https://aws.amazon.com/support.

Example: Rate of on-demand requests exceeds the allowed account throughput and the table cannot be scaled further.

OK to retry? Yes

ResourceInUseException

Message: The resource which you are attempting to change is in use.

Example: You tried to re-create an existing table, or delete a table currently in the CREATING state.

OK to retry? No

ResourceNotFoundException

Message: Requested resource not found.

Example: The table that is being requested does not exist, or is too early in the CREATING state.

OK to retry? No

ThrottlingException

Message: Rate of requests exceeds the allowed throughput.

This exception is returned as an AmazonServiceException response with a THROTTLING_EXCEPTION status code. This exception might be returned if you perform control plane API operations too rapidly.

For tables using on-demand mode, this exception might be returned for any data plane API operation if your request rate is too high. To learn more about on-demand scaling, see Initial throughput and scaling properties.

OK to retry? Yes

UnrecognizedClientException

Message: The Access Key ID or security token is invalid.

The request signature is incorrect. The most likely cause is an invalid AWS access key ID or secret key.

OK to retry? Yes

ValidationException

Message: Varies, depending upon the specific error(s) encountered

This error can occur for several reasons, such as a required parameter that is missing, a value that is out of range, or mismatched data types. The error message contains details about the specific part of the request that caused the error.

OK to retry? No

HTTP status code 5xx

An HTTP 5xx status code indicates a problem that must be resolved by AWS. This might be a transient error, in which case you can retry your request until it succeeds. Otherwise, go to the AWS Service Health Dashboard to see if there are any operational issues with the service.

For more information, see How do I resolve HTTP 5xx errors in Amazon DynamoDB?

InternalServerError (HTTP 500)

DynamoDB could not process your request.

OK to retry? Yes

Note

You might encounter internal server errors while working with items. These are expected during the lifetime of a table. Any failed requests can be retried immediately.

When you receive a status code 500 on a write operation, the operation may have succeeded or failed. If the write operation is a TransactWriteItem request, then it is OK to retry the operation. If the write operation is a single-item write request such as PutItem, UpdateItem, or DeleteItem, then your application should read the state of the item before retrying the operation, and/or use DynamoDB condition expression CLI example to ensure that the item remains in a correct state after retrying regardless of whether the prior operation succeeded or failed. If idempotency is a requirement for the write operation, please use TransactWriteItem, which supports idempotent requests by automatically specifying a ClientRequestToken to disambiguate multiple attempts to perform the same action.

ServiceUnavailable (HTTP 503)

DynamoDB is currently unavailable. (This should be a temporary state.)

OK to retry? Yes

Error handling in your application

For your application to run smoothly, you need to add logic to catch and respond to errors. Typical approaches include using try-catch blocks or if-then statements.

The AWS SDKs perform their own retries and error checking. If you encounter an error while using one of the AWS SDKs, the error code and description can help you troubleshoot it.

You should also see a Request ID in the response. The Request ID can be helpful if you need to work with AWS Support to diagnose an issue.

Error retries and exponential backoff

Numerous components on a network, such as DNS servers, switches, load balancers, and others, can generate errors anywhere in the life of a given request. The usual technique for dealing with these error responses in a networked environment is to implement retries in the client application. This technique increases the reliability of the application.

Each AWS SDK implements retry logic automatically. You can modify the retry parameters to your needs. For example, consider a Java application that requires a fail-fast strategy, with no retries allowed in case of an error. With the AWS SDK for Java, you could use the ClientConfiguration class and provide a maxErrorRetry value of 0 to turn off the retries. For more information, see the AWS SDK documentation for your programming language.

If you're not using an AWS SDK, you should retry original requests that receive server errors (5xx). However, client errors (4xx, other than a ThrottlingException or a ProvisionedThroughputExceededException) indicate that you need to revise the request itself to correct the problem before trying again.

In addition to simple retries, each AWS SDK implements an exponential backoff algorithm for better flow control. The concept behind exponential backoff is to use progressively longer waits between retries for consecutive error responses. For example, up to 50 milliseconds before the first retry, up to 100 milliseconds before the second, up to 200 milliseconds before third, and so on. However, after a minute, if the request has not succeeded, the problem might be the request size exceeding your provisioned throughput, and not the request rate. Set the maximum number of retries to stop around one minute. If the request is not successful, investigate your provisioned throughput options.

Note

The AWS SDKs implement automatic retry logic and exponential backoff.

Most exponential backoff algorithms use jitter (randomized delay) to prevent successive collisions. Because you aren't trying to avoid such collisions in these cases, you do not need to use this random number. However, if you use concurrent clients, jitter can help your requests succeed faster. For more information, see the blog post about Exponential backoff and jitter.

Batch operations and error handling

The DynamoDB low-level API supports batch operations for reads and writes. BatchGetItem reads items from one or more tables, and BatchWriteItem puts or deletes items in one or more tables. These batch operations are implemented as wrappers around other non-batch DynamoDB operations. In other words, BatchGetItem invokes GetItem once for each item in the batch. Similarly,BatchWriteItem invokes DeleteItem or PutItem, as appropriate, for each item in the batch.

A batch operation can tolerate the failure of individual requests in the batch. For example, consider a BatchGetItem request to read five items. Even if some of the underlying GetItem requests fail, this does not cause the entire BatchGetItem operation to fail. However, if all five read operations fail, then the entire BatchGetItem fails.

The batch operations return information about individual requests that fail so that you can diagnose the problem and retry the operation. For BatchGetItem, the tables and primary keys in question are returned in the UnprocessedKeys value of the response. For BatchWriteItem, similar information is returned in UnprocessedItems.

The most likely cause of a failed read or a failed write is throttling. For BatchGetItem, one or more of the tables in the batch request does not have enough provisioned read capacity to support the operation. For BatchWriteItem, one or more of the tables does not have enough provisioned write capacity.

If DynamoDB returns any unprocessed items, you should retry the batch operation on those items. However, we strongly recommend that you use an exponential backoff algorithm. If you retry the batch operation immediately, the underlying read or write requests can still fail due to throttling on the individual tables. If you delay the batch operation using exponential backoff, the individual requests in the batch are much more likely to succeed.