Choosing the Right DynamoDB Partition Key
This blog post covers important considerations and strategies for choosing the right partition key for designing a schema that uses Amazon DynamoDB. Choosing the right partition key is an important step in the design and building of scalable and reliable applications on top of DynamoDB.
What is a partition key?
DynamoDB supports two types of primary keys:
- Partition key: A simple primary key, composed of one attribute known as the partition key. Attributes in DynamoDB are similar in many ways to fields or columns in other database systems.
- Partition key and sort key: Referred to as a composite primary key, this type of key is composed of two attributes. The first attribute is the partition key, and the second attribute is the sort key. All data under a partition key is sorted by the sort key value. The following is an example.
Why do I need a partition key?
DynamoDB stores data as groups of attributes, known as items. Items are similar to rows or records in other database systems. DynamoDB stores and retrieves each item based on the primary key value, which must be unique. Items are distributed across 10-GB storage units, called partitions (physical storage internal to DynamoDB). Each table has one or more partitions, as shown in the following illustration. For more information, see Partitions and Data Distribution in the DynamoDB Developer Guide.
DynamoDB uses the partition key’s value as an input to an internal hash function. The output from the hash function determines the partition in which the item is stored. Each item’s location is determined by the hash value of its partition key.
In most cases, all items with the same partition key are stored together in a collection, which we define as a group of items with the same partition key but different sort keys. For tables with composite primary keys, the sort key may be used as a partition boundary. DynamoDB splits partitions by sort key if the collection size grows bigger than 10 GB.
Partition keys and request throttling
DynamoDB automatically supports your access patterns using the throughput you have provisioned, or up to your account limits in the on-demand mode. Regardless of the capacity mode you choose, if your access pattern exceeds 3000 RCU or 1000 WCU for a single partition key value, your requests might be throttled with a
Reading or writing above the limit can be caused by these issues:
- Uneven distribution of data due to the wrong choice of partition key
- Frequent access of the same key in a partition (the most popular item, also known as a hot key)
- A request rate greater than the provisioned throughput or on-demand account limits
To avoid request throttling, design your DynamoDB table with the right partition key to meet your access requirements and provide even distribution of data.
Recommendations for partition keys
Use high-cardinality attributes. These are attributes that have distinct values for each item, like
orderid, and so on.
Use composite attributes. Try to combine more than one attribute to form a unique key, if that meets your access pattern. For example, consider an orders table with
customerid#productid#countrycode as the partition key and
order_date as the sort key, where the symbol # is used to split different field.
Cache the popular items when there is a high volume of read traffic using Amazon DynamoDB Accelerator (DAX). The cache acts as a low-pass filter, preventing reads of unusually popular items from swamping partitions. For example, consider a table that has deals information for products. Some deals are expected to be more popular than others during major sale events like Black Friday or Cyber Monday. DAX is a fully managed, in-memory cache for DynamoDB that doesn’t require developers to manage cache invalidation, data population, or cluster management. DAX also is compatible with DynamoDB API calls, so developers can incorporate it more easily into existing applications.
Add random numbers or digits from a predetermined range for write-heavy use cases. Suppose that you expect a large volume of writes for a partition key (for example, greater than 1000 1 K writes per second). In this case, use an additional prefix or suffix (a fixed number from predetermined range, say 0–9) and add it to the partition key.
For example, consider a table of invoice transactions. A single invoice can contain thousands of transactions per client. How do we enforce uniqueness and ability to query and update the invoice details for high-volumetric clients?
Following is the recommended table layout for this scenario:
- Partition key: Add a random suffix (for example 0–9 or 0–99) with the
InvoiceNumber, depending on the number of transactions per
InvoiceNumber. For example, assume that a single
InvoiceNumbercontains up to 50,000 1K items and that you expect 5000 writes per second. In this case, you can use the following formula to estimate the suffix range: ((Number of writes per second * CEILING(item size in KB, 1) ) /1000). Using this formula requires a minimum of five partitions to distribute writes, and hence you might want to set the range as 0-4.
- Sort key:
Client#txId. This value is the client id followed by a separator character followed by the transaction Id.
Attribute1 InvoiceNumber#121212#0 Client#1#txid#1 InvoiceDate#2018-05-17 01.36.45 InvoiceNumber#121212#0 Client#1#txid#2 InvoiceDate#2018-05-18 01.36.30 InvoiceNumber#121212#1 Client#2#txid#1 InvoiceDate#2018-06-15 01.36.20 InvoiceNumber#121212#1 Client#2#txid#2 InvoiceDate#2018-07-1 01.36.15
- This combination gives us a good spread through the partitions. You can use the sort key to filter for a specific client (for example, where
- Because we have a random number appended to our partition key (0–4), we need to query the table five times for a given
InvoiceNumber. Our partition key could be
InvoiceNumber#121212#[0-4], so we need to query where partition key is
Client#txIdbegins_with Client#1. We need to repeat this for 121212#1, on up to 121212#4 and then merge the results.
Note: After the suffix range is decided, there is no easy way to further spread the data because suffix modifications also require application-level changes. Therefore, consider how hot each partition key might get and add enough of a random suffix (with buffer) to accommodate the anticipated future growth.
This option induces additional latency for reads due to X number of read requests per query.
As mentioned in the DynamoDB documentation, a randomizing strategy can greatly improve write throughput. But it’s difficult to read a specific item because you don’t know which suffix value was used when writing the item.
To make it easier to read individual items, consider sharding by using calculated suffixes, as explained in Using Write Sharding to Distribute Workloads Evenly in the DynamoDB Developer Guide.For example, suppose that a large number of invoice transactions are being processed but the read pattern is to retrieve small number of items for a particular
sourceid by date range. In this case, it’s more effective to distribute the items across a range of partitions using a particular attribute, in this case
sourceid. You can hash the
sourceId to annotate the partition key rather than using random number strategy. This way, you know which partition to query and retrieve the results from.
As with tables, we recommend that you consider a sharding approach for global secondary indexes (GSI) if you are anticipating a hot key scenario with a global secondary index partition key.
For example, consider the schema layout of an
InvoiceTransaction table. It has a header row for each invoice and contains attributes such as total amount due and tx_country, which are unique for each invoice. Assuming we need to find the list of invoices issued for each transaction country, we can create a global secondary index with GSI partition key
tx_country. However, this approach leads to a hot key write scenario, because the number of invoices per country are unevenly distributed.
The following table shows the recommended layout with a sharding approach that avoids a hot GSI partition.
|Partition Key||Sort Key||Attribute1||Attribute2||Attribute3||
GSI Partition Key
Random prefix range
GSI Sort Key
Following is the global secondary index (GSI) for the preceding scenario.
Random prefix range
In the preceding example, you might want to identify the list of invoice numbers associated with the USA. In this case, you can issue parallel queries to the global secondary index with GSI
partition key = (0-N) and
GSI sort key = USA.
Antipatterns for partition keys
Use sequences or unique IDs generated by the DB engine as the partition key, especially when you are migrating from relational databases. It’s common to use sequences (
schema.sequence.NEXTVAL) as the primary key to enforce uniqueness in Oracle tables. Sequences are not usually used for accessing the data.
The following is an example schema layout for an order table that has been migrated from Oracle to DynamoDB. The main table partition key (
TransactionID) is populated by a UID. A GSI is created on
Order_Date for query purposes.
(Also GSI partition key)
(Also GSI sort key)
Following are the potential issues with this approach:
- In most cases you won’t use
TransactionIDfor any query purposes, so you lose the ability to use the partition key to perform a fast lookup of data. To expand this reasoning, consider the traditional order history view on an e-commerce site. Normally orders are retrieved by customer ID or Order ID, not a UID such as a transaction ID that was synthetically generated during checkout. It’s better to choose a natural partition key than generate a synthetic one that won’t be used for querying.
- GSIs support eventual consistency only, with additional costs for reads and writes. This means that lookups for brand new orders sent to the GSI could return no results for a short time until the changes are propagated.
Note: You can use the conditional writes feature instead of sequences to enforce uniqueness and prevent the overwriting of an item.
Using low-cardinality attributes like Product_SKU as the partition key and Order_Date as the sort key greatly increases the likelihood of hot partition issues. Specifically, you may create a hot partition under a specific partition key when transactions are created and items inserted into the table or index. For example, if one product is more popular, then the reads and writes for that partition key are high resulting in throttling issues. This is because partition keys have the largest influence on which partition an item falls on, and items with the same partition key are usually on the same underlying DynamoDB partition.
Scan, DynamoDB API operations require an equality operator (EQ) on the partition key for tables and GSIs. As a result, the partition key must be something that is easily queried by your application with a simple lookup. An example is using
key=value, which returns either a unique item or fewer items. There is a 1-MB limit on items that you can fetch through a single query operation, which means that you need to paginate using
LastEvaluatedKey, which takes time for large collections.
In short: Do not lift and shift primary keys from the source database without analyzing the data model and access patterns of the target DynamoDB table.
When it comes to DynamoDB partition key strategies, no single solution fits all use cases. You should evaluate various approaches based on your data ingestion and access pattern, then choose the most appropriate key with the least probability of hitting throttling issues. Along with the best partition key design, DynamoDB adaptive capacity can protect your application from throttling issues against an uneven data access pattern.
For further guidance on schema design for various scenarios, see NoSQL Design for DynamoDB in the DynamoDB Developer Guide.
About the Authors
Gowri Balasubramanian is a senior solutions architect at Amazon Web Services. He works with AWS customers to provide guidance and technical assistance on both relational as well as NoSQL database services, helping them improve the value of their solutions when using AWS.
Sean Shriver is a Dallas-based senior NoSQL specialist solutions architect focused on DynamoDB.