amazon-dynamodb — quality + safety report

---
name: amazon-dynamodb
description: "Amazon DynamoDB API skill. Use when working with Amazon DynamoDB for root. Covers 57 endpoints."
version: 1.0.0
generator: lapsh
---

# Amazon DynamoDB
API version: 2012-08-10

## Auth
AWS SigV4

## Base URL
Not specified.

## Setup
1. Configure auth: AWS SigV4
3. POST / -- create first resource

## Endpoints

57 endpoints across 1 groups. See references/api-spec.lap for full details.

### root
| Method | Path | Description |
|--------|------|-------------|
| POST | / | This operation allows you to perform batch reads or writes on data stored in DynamoDB, using PartiQL. Each read statement in a BatchExecuteStatement must specify an equality condition on all key attributes. This enforces that each SELECT statement in a batch returns at most a single item. For more information, see Running batch operations with PartiQL for DynamoDB .  The entire batch must consist of either read statements or write statements, you cannot mix both in one batch.   A HTTP 200 response does not mean that all statements in the BatchExecuteStatement succeeded. Error details for individual statements can be found under the Error field of the BatchStatementResponse for each statement. |
| POST | / | The BatchGetItem operation returns the attributes of one or more items from one or more tables. You identify requested items by primary key. A single operation can retrieve up to 16 MB of data, which can contain as many as 100 items. BatchGetItem returns a partial result if the response size limit is exceeded, the table's provisioned throughput is exceeded, more than 1MB per partition is requested, or an internal processing failure occurs. If a partial result is returned, the operation returns a value for UnprocessedKeys. You can use this value to retry the operation starting with the next item to get.  If you request more than 100 items, BatchGetItem returns a ValidationException with the message "Too many items requested for the BatchGetItem call."  For example, if you ask to retrieve 100 items, but each individual item is 300 KB in size, the system returns 52 items (so as not to exceed the 16 MB limit). It also returns an appropriate UnprocessedKeys value so you can get the next page of results. If desired, your application can include its own logic to assemble the pages of results into one dataset. If none of the items can be processed due to insufficient provisioned throughput on all of the tables in the request, then BatchGetItem returns a ProvisionedThroughputExceededException. If at least one of the items is successfully processed, then BatchGetItem completes successfully, while returning the keys of the unread items in UnprocessedKeys.  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. For more information, see Batch Operations and Error Handling in the Amazon DynamoDB Developer Guide.  By default, BatchGetItem performs eventually consistent reads on every table in the request. If you want strongly consistent reads instead, you can set ConsistentRead to true for any or all tables. In order to minimize response latency, BatchGetItem may retrieve items in parallel. When designing your application, keep in mind that DynamoDB does not return items in any particular order. To help parse the response by item, include the primary key values for the items in your request in the ProjectionExpression parameter. If a requested item does not exist, it is not returned in the result. Requests for nonexistent items consume the minimum read capacity units according to the type of read. For more information, see Working with Tables in the Amazon DynamoDB Developer Guide. |
| POST | / | The BatchWriteItem operation puts or deletes multiple items in one or more tables. A single call to BatchWriteItem can transmit up to 16MB of data over the network, consisting of up to 25 item put or delete operations. While individual items can be up to 400 KB once stored, it's important to note that an item's representation might be greater than 400KB while being sent in DynamoDB's JSON format for the API call. For more details on this distinction, see Naming Rules and Data Types.   BatchWriteItem cannot update items. If you perform a BatchWriteItem operation on an existing item, that item's values will be overwritten by the operation and it will appear like it was updated. To update items, we recommend you use the UpdateItem action.  The individual PutItem and DeleteItem operations specified in BatchWriteItem are atomic; however BatchWriteItem as a whole is not. If any requested operations fail because the table's provisioned throughput is exceeded or an internal processing failure occurs, the failed operations are returned in the UnprocessedItems response parameter. You can investigate and optionally resend the requests. Typically, you would call BatchWriteItem in a loop. Each iteration would check for unprocessed items and submit a new BatchWriteItem request with those unprocessed items until all items have been processed. For tables and indexes with provisioned capacity, if none of the items can be processed due to insufficient provisioned throughput on all of the tables in the request, then BatchWriteItem returns a ProvisionedThroughputExceededException. For all tables and indexes, if none of the items can be processed due to other throttling scenarios (such as exceeding partition level limits), then BatchWriteItem returns a ThrottlingException.  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. For more information, see Batch Operations and Error Handling in the Amazon DynamoDB Developer Guide.  With BatchWriteItem, you can efficiently write or delete large amounts of data, such as from Amazon EMR, or copy data from another database into DynamoDB. In order to improve performance with these large-scale operations, BatchWriteItem does not behave in the same way as individual PutItem and DeleteItem calls would. For example, you cannot specify conditions on individual put and delete requests, and BatchWriteItem does not return deleted items in the response. If you use a programming language that supports concurrency, you can use threads to write items in parallel. Your application must include the necessary logic to manage the threads. With languages that don't support threading, you must update or delete the specified items one at a time. In both situations, BatchWriteItem performs the specified put and delete operations in parallel, giving you the power of the thread pool approach without having to introduce complexity into your application. Parallel processing reduces latency, but each specified put and delete request consumes the same number of write capacity units whether it is processed in parallel or not. Delete operations on nonexistent items consume one write capacity unit. If one or more of the following is true, DynamoDB rejects the entire batch write operation:   One or more tables specified in the BatchWriteItem request does not exist.   Primary key attributes specified on an item in the request do not match those in the corresponding table's primary key schema.   You try to perform multiple operations on the same item in the same BatchWriteItem request. For example, you cannot put and delete the same item in the same BatchWriteItem request.     Your request contains at least two items with identical hash and range keys (which essentially is two put operations).    There are more than 25 requests in the batch.   Any individual item in a batch exceeds 400 KB.   The total request size exceeds 16 MB.   Any individual items with keys exceeding the key length limits. For a partition key, the limit is 2048 bytes and for a sort key, the limit is 1024 bytes. |
| POST | / | Creates a backup for an existing table.  Each time you create an on-demand backup, the entire table data is backed up. There is no limit to the number of on-demand backups that can be taken.   When you create an on-demand backup, a time marker of the request is cataloged, and the backup is created asynchronously, by applying all changes until the time of the request to the last full table snapshot. Backup requests are processed instantaneously and become available for restore within minutes.  You can call CreateBackup at a maximum rate of 50 times per second. All backups in DynamoDB work without consuming any provisioned throughput on the table.  If you submit a backup request on 2018-12-14 at 14:25:00, the backup is guaranteed to contain all data committed to the table up to 14:24:00, and data committed after 14:26:00 will not be. The backup might contain data modifications made between 14:24:00 and 14:26:00. On-demand backup does not support causal consistency.   Along with data, the following are also included on the backups:    Global secondary indexes (GSIs)   Local secondary indexes (LSIs)   Streams   Provisioned read and write capacity |
| POST | / | Creates a global table from an existing table. A global table creates a replication relationship between two or more DynamoDB tables with the same table name in the provided Regions.   This documentation is for version 2017.11.29 (Legacy) of global tables, which should be avoided for new global tables. Customers should use Global Tables version 2019.11.21 (Current) when possible, because it provides greater flexibility, higher efficiency, and consumes less write capacity than 2017.11.29 (Legacy). To determine which version you're using, see Determining the global table version you are using. To update existing global tables from version 2017.11.29 (Legacy) to version 2019.11.21 (Current), see Upgrading global tables.  If you want to add a new replica table to a global table, each of the following conditions must be true:   The table must have the same primary key as all of the other replicas.   The table must have the same name as all of the other replicas.   The table must have DynamoDB Streams enabled, with the stream containing both the new and the old images of the item.   None of the replica tables in the global table can contain any data.    If global secondary indexes are specified, then the following conditions must also be met:     The global secondary indexes must have the same name.     The global secondary indexes must have the same hash key and sort key (if present).     If local secondary indexes are specified, then the following conditions must also be met:     The local secondary indexes must have the same name.     The local secondary indexes must have the same hash key and sort key (if present).      Write capacity settings should be set consistently across your replica tables and secondary indexes. DynamoDB strongly recommends enabling auto scaling to manage the write capacity settings for all of your global tables replicas and indexes.   If you prefer to manage write capacity settings manually, you should provision equal replicated write capacity units to your replica tables. You should also provision equal replicated write capacity units to matching secondary indexes across your global table. |
| POST | / | The CreateTable operation adds a new table to your account. In an Amazon Web Services account, table names must be unique within each Region. That is, you

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