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This section covers some best practices for query and scan operations.
When you create a table, you set its read and write capacity unit requirements. For
reads, the capacity units are expressed as the number of strongly consistent 4 KB
data read requests per second. For eventually consistent reads, a read capacity unit is
two 4 KB read requests per second. A
Scan operation performs
eventually consistent reads, and it can return up to 1 MB (one page) of
data. Therefore, a single
Scan request can consume
(1 MB page size / 4 KB item size) / 2 (eventually consistent reads)
= 128 read operations. This represents a sudden burst of usage, compared to the
configured read capacity for the table. This sudden use of capacity units by a scan
prevents other potentially more important requests for the same table from using the
available capacity units. As a result, you likely get a
ProvisionedThroughputExceeded exception for those
Note that it is not just the burst of capacity units the
uses that is a problem. It is also because the scan is likely to consume all of its
capacity units from the same partition because the scan requests read items that are
next to each other on the partition. This means that the request is hitting the same
partition, causing all of its capacity units to be consumed, and throttling other
requests to that partition. If the request to read data had been spread across multiple
partitions, then the operation would not have throttled a specific partition.
The following diagram illustrates the impact of a sudden burst of capacity unit usage
Scan operations, and its impact
on your other requests against the same table.
Instead of using a large
Scan operation, you can use the
following techniques to minimize the impact of a scan on a table’s provisioned
Reduce Page Size
Because a Scan operation reads an entire page (by default,
1 MB), you can reduce the impact of the scan operation by setting
a smaller page size. The
Scan operation provides a
Limit parameter that you can use to set the page size
for your request. Each
request that has a smaller page size uses fewer read operations and creates a
"pause" between each request. For example, if each item is 4 KB and you
set the page size to 40 items, then a
Query request would
consume only 10 strongly consistent read operations or 5 eventually consistent
read operations. A larger number of smaller
Query operations would allow your other critical
requests to succeed without throttling.
Isolate Scan Operations
Amazon DynamoDB is designed for easy scalability. As a result, an application can create tables for distinct purposes, possibly even duplicating content across several tables. You want to perform scans on a table that is not taking "mission-critical" traffic. Some applications handle this load by rotating traffic hourly between two tables – one for critical traffic, and one for bookkeeping. Other applications can do this by performing every write on two tables: a "mission-critical" table, and a "shadow" table.
You should configure your application to retry any request that receives a response code that indicates you have exceeded your provisioned throughput, or increase the provisioned throughput for your table using the UpdateTable operation. If you have temporary spikes in your workload that cause your throughput to exceed, occasionally, beyond the provisioned level, retry the request with exponential backoff. For more information about implementing exponential backoff, see Error Retries and Exponential Backoff.
Many applications can benefit from using parallel
rather than sequential scans. For example, an application that processes a large table
of historical data can perform a parallel scan much faster than a sequential one.
Multiple worker threads in a background "sweeper" process could scan a table at a low
priority without affecting production traffic. In each of these examples, a parallel
Scan is used in such a way that it does not starve other
applications of provisioned throughput resources.
Although parallel scans can be beneficial, they can place a heavy demand on
provisioned throughput. With a parallel scan, your application will have multiple
workers that are all running
Scan operations concurrently, which
can very quickly consume all of your table's provisioned read capacity. In that case,
other applications that need to access the table might be throttled.
A parallel scan can be the right choice if the following conditions are met:
The table size is 20 GB or larger.
The table's provisioned read throughput is not being fully utilized.
Scan operations are too
The best setting for
TotalSegments depends on your specific data, the
table's provisioned throughput settings, and your performance requirements. You will
probably need to experiment to get it right. We recommend that you begin with a
simple ratio, such as one segment per 2 GB of data. For example, for a 30 GB table,
you could set
TotalSegments to 15 (30 GB / 2 GB). Your application
would then use fifteen workers, with each worker scanning a different
You can also choose a value for
TotalSegments that is based on client
resources. You can set
TotalSegments to any number from 1 to 4096, and
Amazon DynamoDB will allow you to scan that number of segments. If, for example, your client
limits the number of threads that can run concurrently, you can gradually increase
TotalSegments until you get the best
performance with your application.
You will need to monitor your parallel scans to optimize your provisioned throughput
utilization, while also making sure that your other applications aren't starved of
resources. Increase the value for
TotalSegments if you do not consume
all of your provisioned throughput but still experience throttling in your
Scan requests. Reduce the value for
TotalSegments if the
Scan requests consume
more provisioned throughput than you want to use.