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When building an application using Amazon DynamoDB, you may need to ensure that new entries in a table receive a continuously incrementing sequence number. This feature, commonly referred to as auto-increment in other databases, can automatically assign a value upon insertion. Typical use cases for this might include assigning numeric identifiers to customer orders or support tickets.
Although DynamoDB does not offer an auto-increment attribute type, there are several methods to create a continuously increasing sequence number. This article outlines two straightforward and cost-effective approaches.
Solution Overview
Before diving in, it’s essential to assess whether you genuinely require a consistently increasing sequence number. Randomly generated identifiers usually provide better scalability, as they do not necessitate a centralized coordination point. Scenarios where simulating auto-increment in DynamoDB makes sense generally fall into two categories:
- Migrating from a relational database where users or systems are accustomed to the existing auto-increment behavior.
- The application must generate a user-friendly growing numeric identifier for new items, such as an employee number or ticket number.
In the sections that follow, we will explore how to implement an ever-increasing sequence number using a counter or a sort key.
Implementation with a Counter
The first method for generating a continuously increasing sequence number involves utilizing an atomic counter. This process entails two steps: first, you send a request to increment the counter and receive the new value in response; second, you use that new value in a subsequent write operation.
Here’s a Python example that updates an atomic counter to retrieve the next order ID and subsequently inserts an order where the ID serves as the partition key. You can also choose to use a different value for the partition key and store the ID in a separate attribute.
import boto3
table = boto3.resource('dynamodb').Table('orders')
# Increment the counter and obtain the new value
response = table.update_item(
Key={'pk': 'orderCounter'},
UpdateExpression="ADD #cnt :val",
ExpressionAttributeNames={'#cnt': 'count'},
ExpressionAttributeValues={':val': 1},
ReturnValues="UPDATED_NEW"
)
# Get the new value
nextOrderId = response['Attributes']['count']
# Use the new value
table.put_item(
Item={'pk': str(nextOrderId), 'deliveryMethod' : 'expedited'}
)
This design eliminates race conditions since all writes to a single item in DynamoDB are processed sequentially, ensuring that each counter value is unique. The cost of this method includes one write to update the counter item, in addition to the regular write costs for storing the new item. The maximum throughput is determined by the counter item; a single small item in DynamoDB has a throughput limit equivalent to that of a partition.
Gaps may occur in the sequence if a failure happens between the counter update and the new item write. For instance, the client application might halt after the first step, or the AWS SDK’s automatic retry feature may increment the counter multiple times if a network failure interrupts the process. Note that gaps can also happen with auto-increment columns in traditional databases.
If you need more than one sequence value for your table, you can maintain multiple counters concurrently.
Implementation with a Sort Key
The second approach employs the maximum value of the sort key within an item collection to monitor the highest sequence value for that collection.
In a DynamoDB table, items consist of a partition key and an optional sort key as part of their primary key. Items sharing the same partition key but differing sort keys form an item collection. A DynamoDB query can target an item collection to retrieve all items or specify a sort key condition to get a subset.
To efficiently track the maximum value of the sequence, you can design the sort key to represent an item’s sequential value. For instance, consider a table that holds projects and their associated issues. The project identifier serves as the partition key, while the issue number acts as the sort key (remember to declare the sort key as numeric type when creating the table, or as a string with zero-padding for correct lexicographical sorting). Each project’s issue number increments independently. The highest issue number for any project corresponds to the highest value within that item collection.
To add a new item to an item collection with the next sequence value, you will follow a two-step process: first, query to find the highest sort key value for that collection; second, attempt to write the new item using the highest value plus one. The write operation must include a condition expression requiring the item to not already exist in the table, thereby preventing race conditions with clients attempting to read the same value simultaneously.
Here’s a Python example that demonstrates querying for the highest used value in an item collection (representing a project) and writing an item with the next value as the sort key. The example retries with an incremented sort key until it is successful.
import boto3
from boto3.dynamodb.conditions import Key
PROJECT_ID = 'projectA'
dynamo = boto3.resource('dynamodb')
client = dynamo.Table('projects')
highestIssueId = 0
saved = False
# Query for the last sorted value in the given item collection
response = client.query(
KeyConditionExpression=Key('pk').eq(PROJECT_ID),
ScanIndexForward=False,
Limit=1
)
# Get the sort key value
if response['Count'] > 0:
highestIssueId = int(response['Items'][0]['sk'])
while not saved:
try:
# Attempt to write with the next value in the sequence, only if the item doesn’t exist
response = client.put_item(
Item={
'pk': PROJECT_ID,
'sk' : highestIssueId + 1,
'priority' : 'low'
},
ConditionExpression='attribute_not_exists(pk)'
)
saved = True
# If an exception occurs, it indicates a race condition
except dynamo.meta.client.exceptions.ConditionalCheckFailedException as e:
highestIssueId = highestIssueId + 1
The cost of this method includes 0.5 read units for querying the highest value used so far, plus the usual write costs for the new item. You will incur charges for any rejected write attempts due to the condition check identifying that the item already exists, making the cost of this approach rise with higher contention and retries. If you anticipate contention, consider using a strongly consistent read, which costs 1.0 read units for the query but guarantees retrieval of the latest value.
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