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Saturday, October 21, 2023

How We Diminished DynamoDB Prices by Utilizing DynamoDB Streams and Scans Extra Effectively

Lots of our customers implement operational reporting and analytics on DynamoDB utilizing Rockset as a SQL intelligence layer to serve dwell dashboards and purposes. As an engineering group, we’re consistently trying to find alternatives to enhance their SQL-on-DynamoDB expertise.


For the previous few weeks, we have now been onerous at work tuning the efficiency of our DynamoDB ingestion course of. Step one on this course of was diving into DynamoDB’s documentation and performing some experimentation to make sure that we have been utilizing DynamoDB’s learn APIs in a approach that maximizes each the soundness and efficiency of our system.

Background on DynamoDB APIs

AWS presents a Scan API and a Streams API for studying knowledge from DynamoDB. The Scan API permits us to linearly scan a complete DynamoDB desk. That is costly, however generally unavoidable. We use the Scan API the primary time we load knowledge from a DynamoDB desk to a Rockset assortment, as we have now no technique of gathering all the information apart from scanning by way of it. After this preliminary load, we solely want to observe for updates, so utilizing the Scan API could be fairly wasteful. As an alternative, we use the Streams API which supplies us a time-ordered queue of updates utilized to the DynamoDB desk. We learn these updates and apply them into Rockset, giving customers realtime entry to their DynamoDB knowledge in Rockset!

Dynamo Ingester Architecture

The problem we’ve been endeavor is to make ingesting knowledge from DynamoDB into Rockset as seamless and cost-efficient as doable given the constraints introduced by knowledge sources, like DynamoDB. Following, I’ll talk about a number of of points we bumped into in tuning and stabilizing each phases of our DynamoDB ingestion course of whereas protecting prices low for our customers.


How we measure scan efficiency

Throughout the scanning part, we intention to constantly maximize our learn throughput from DynamoDB with out consuming greater than a user-specified variety of RCUs per desk. We would like ingesting knowledge into Rockset to be environment friendly with out interfering with present workloads operating on customers’ dwell DynamoDB tables.

Understanding methods to set scan parameters

From very preliminary testing, we observed that our scanning part took fairly a very long time to finish so we did some digging to determine why. We ingested a DynamoDB desk into Rockset and noticed what occurred through the scanning part. We anticipated to constantly eat the entire provisioned throughput.

Initially, our RCU consumption appeared like the next:

Scan Initial RCU

We noticed an inexplicable stage of fluctuation within the RCU consumption over time, significantly within the first half of the scan. These fluctuations are dangerous as a result of every time there’s a significant drop within the throughput, we find yourself lengthening the ingestion course of and growing our customers DynamoDB prices.

The issue was clear however the underlying trigger was not apparent. On the time, there have been a number of variables that we have been controlling fairly naively. DynamoDB exposes two essential variables: web page dimension and section rely, each of which we had set to mounted values. We additionally had our personal charge limiter which throttled the variety of DynamoDB Scan API calls we made. We had additionally set the restrict this charge limiter was imposing to a set worth. We suspected that one in every of these variables being sub-optimally configured was the probably reason for the huge fluctuations we have been observing.

Some investigation revealed that the reason for the fluctuation was primarily the speed limiter. It turned out the mounted restrict we had set on our charge limiter was too low, so we have been getting throttled too aggressively by our personal charge limiter. We determined to repair this downside by configuring our limiter based mostly on the quantity of RCU allotted to the desk. We are able to simply (and do plan to) transition to utilizing a user-specified variety of RCU for every desk, which is able to permit us to restrict Rockset’s RCU consumption even when customers have RCU autoscaling enabled.

public int getScanRateLimit(AmazonDynamoDB consumer, String tableName,
                            int numSegments) {
    TableDescription tableDesc = consumer.describeTable(tableName).getTable();
    // Observe: it will return 0 if the desk has RCU autoscaling enabled
    remaining lengthy tableRcu = tableDesc.getProvisionedThroughput().getReadCapacityUnits();
    remaining int numSegments = config.getNumSegments();
    return desiredRcuUsage / numSegments;

For every section, we carry out a scan, consuming capability on our charge limiter as we eat DynamoDB RCU’s.

public void doScan(AmazonDynamoDb consumer, String tableName, int numSegments) {
    RateLimiter rateLimiter = RateLimiter.create(getScanRateLimit(consumer, 
                                                 tableName, numSegments))
    whereas (!executed) {
        ScanResult outcome = consumer.scan(/* feed scan request in */);
        // do processing ...

The results of our new Scan configuration was the next:

Dynamo After RCU

We have been comfortable to see that, with our new configuration, we have been in a position to reliably management the quantity of throughput we consumed. The issue we found with our charge limiter delivered to gentle our underlying want for extra dynamic DynamoDB Scan configurations. We’re persevering with to run experiments to find out methods to dynamically set the web page dimension and section rely based mostly on table-specific knowledge, however we additionally moved onto coping with among the challenges we have been going through with DynamoDB Streams.


How we measure streaming efficiency

Our aim through the streaming part of ingestion is to reduce the period of time it takes for an replace to enter Rockset after it’s utilized in DynamoDB whereas protecting the associated fee utilizing Rockset as little as doable for our customers. The first value issue for DynamoDB Streams is the variety of API calls we make. DynamoDB’s pricing permits customers 2.5 million free API calls and prices $0.02 per 100,000 requests past that. We wish to attempt to keep as near the free tier as doable.

Beforehand we have been querying DynamoDB at a charge of ~300 requests/second as a result of we encountered plenty of empty shards within the streams we have been studying. We believed that we’d must iterate by way of all of those empty shards whatever the charge we have been querying at. To mitigate the load we placed on customers’ Dynamo tables (and in flip their wallets), we set a timer on these reads after which stopped studying for five minutes if we didn’t discover any new information. On condition that this mechanism ended up charging customers who didn’t even have a lot knowledge in DynamoDB and nonetheless had a worst case latency of 5 minutes, we began investigating how we might do higher.

Lowering the frequency of streaming calls

We ran a number of experiments to make clear our understanding of the DynamoDB Streams API and decide whether or not we might scale back the frequency of the DynamoDB Streams API calls our customers have been being charged for. For every experiment, we various the period of time we waited between API calls and measured the typical period of time it took for an replace to a DynamoDB desk to be mirrored in Rockset.

Inserting information into the DynamoDB desk at a continuing charge of two information/second, the outcomes have been as follows:

Dynamo Table 1

Inserting information into the DynamoDB desk in a bursty sample, the outcomes have been as follows:

Dynamo Table 2

The outcomes above confirmed that making 1 API name each second is loads to make sure that we preserve sub-second latencies. Our preliminary assumptions have been improper, however these outcomes illuminated a transparent path ahead. We promptly modified our ingestion course of to question DynamoDB Streams for brand new knowledge solely as soon as per second so as give us the efficiency we’re in search of at a a lot diminished value to our customers.

Calculating our value discount

Since with DynamoDB Streams we’re straight accountable for our customers prices, we determined that we would have liked to exactly calculate the associated fee our customers incur as a result of approach we use DynamoDB Streams. There are two components which wholly decide the quantity that customers might be charged for DynamoDB Streams: the variety of Streams API calls made and the quantity of knowledge transferred. The quantity of knowledge transferred is essentially past our management. Every API name response unavoidably transfers a small quantity (768 bytes) of knowledge. The remaining is all consumer knowledge, which is barely learn into Rockset as soon as. We targeted on controlling the variety of DynamoDB Streams API calls we make to customers’ tables as this was beforehand the driving force of our customers’ DynamoDB prices.

Following is a breakdown of the associated fee we estimate with our newly transformed ingestion course of:

Dynamo Table 3

We have been comfortable to see that, with our optimizations, our customers ought to incur just about no extra value on their DynamoDB tables resulting from Rockset!


We’re actually excited that the work we’ve been doing has efficiently pushed DynamoDB prices down for our customers whereas permitting them to work together with their DynamoDB knowledge in Rockset in realtime!

This can be a simply sneak peek into among the challenges and tradeoffs we’ve confronted whereas working to make ingesting knowledge from DynamoDB into Rockset as seamless as doable. In the event you’re fascinated about studying extra about methods to operationalize your DynamoDB knowledge utilizing Rockset try a few of our current materials and keep tuned for updates as we proceed to construct Rockset out!

If you would like to see Rockset and DynamoDB in motion, you need to try our transient product tour.

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