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Lots of our customers implement operational reporting and analytics on DynamoDB utilizing Rockset as a SQL intelligence layer to serve reside dashboards and purposes. As an engineering staff, we’re consistently trying to find alternatives to enhance their SQL-on-DynamoDB expertise.
For the previous few weeks, we’ve been laborious at work tuning the efficiency of our DynamoDB ingestion course of. Step one on this course of was diving into DynamoDB’s documentation and doing a little experimentation to make sure that we have been utilizing DynamoDB’s learn APIs in a method that maximizes each the soundness and efficiency of our system.
Background on DynamoDB APIs
AWS gives a Scan API and a Streams API for studying knowledge from DynamoDB. The Scan API permits us to linearly scan a whole 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’ve no technique of gathering all the info apart from scanning via 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!
The problem we’ve been endeavor is to make ingesting knowledge from DynamoDB into Rockset as seamless and cost-efficient as attainable given the constraints offered by knowledge sources, like DynamoDB. Following, I’ll focus on just a few of points we bumped into in tuning and stabilizing each phases of our DynamoDB ingestion course of whereas conserving prices low for our customers.
Scans
How we measure scan efficiency
Throughout the scanning part, we purpose 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 working on customers’ reside DynamoDB tables.
Understanding the way to set scan parameters
From very preliminary testing, we seen 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 in the course of the scanning part. We anticipated to constantly eat all the provisioned throughput.
Initially, our RCU consumption seemed like the next:
We noticed an inexplicable stage of fluctuation within the RCU consumption over time, notably within the first half of the scan. These fluctuations are unhealthy as a result of every time there’s a significant drop within the throughput, we find yourself lengthening the ingestion course of and rising our customers DynamoDB prices.
The issue was clear however the underlying trigger was not apparent. On the time, there have been just a few variables that we have been controlling fairly naively. DynamoDB exposes two essential variables: web page dimension and section depend, 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 implementing to a hard and fast worth. We suspected that considered one of these variables being sub-optimally configured was the doubtless explanation for the large 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 primarily based on the quantity of RCU allotted to the desk. We will simply (and do plan to) transition to utilizing a user-specified variety of RCU for every desk, which is able to enable us to restrict Rockset’s RCU consumption even when customers have RCU autoscaling enabled.
public int getScanRateLimit(AmazonDynamoDB shopper, String tableName,
int numSegments) {
TableDescription tableDesc = shopper.describeTable(tableName).getTable();
// Be aware: it will return 0 if the desk has RCU autoscaling enabled
ultimate lengthy tableRcu = tableDesc.getProvisionedThroughput().getReadCapacityUnits();
ultimate 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 shopper, String tableName, int numSegments) {
RateLimiter rateLimiter = RateLimiter.create(getScanRateLimit(shopper,
tableName, numSegments))
whereas (!carried out) {
ScanResult end result = shopper.scan(/* feed scan request in */);
// do processing ...
rateLimiter.purchase(end result.getConsumedCapacity().getCapacityUnits());
}
}
The results of our new Scan configuration was the next:
We have been completely satisfied to see that, with our new configuration, we have been capable of reliably management the quantity of throughput we consumed. The issue we found with our charge limiter delivered to mild our underlying want for extra dynamic DynamoDB Scan configurations. We’re persevering with to run experiments to find out the way to dynamically set the web page dimension and section depend primarily based on table-specific knowledge, however we additionally moved onto coping with a few of the challenges we have been dealing with with DynamoDB Streams.
Streams
How we measure streaming efficiency
Our aim in the course of the streaming part of ingestion is to attenuate the period of time it takes for an replace to enter Rockset after it’s utilized in DynamoDB whereas conserving the associated fee utilizing Rockset as little as attainable for our customers. The first price issue for DynamoDB Streams is the variety of API calls we make. DynamoDB’s pricing permits customers 2.5 million free API calls and expenses $0.02 per 100,000 requests past that. We need to attempt to keep as near the free tier as attainable.
Beforehand we have been querying DynamoDB at a charge of ~300 requests/second as a result of we encountered a number of empty shards within the streams we have been studying. We believed that we’d must iterate via 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 data. 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.
Decreasing 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 cut back the frequency of the DynamoDB Streams API calls our customers have been being charged for. For every experiment, we diverse 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 data into the DynamoDB desk at a continuing charge of two data/second, the outcomes have been as follows:
Inserting data into the DynamoDB desk in a bursty sample, the outcomes have been as follows:
The outcomes above confirmed that making 1 API name each second is a lot to make sure that we keep sub-second latencies. Our preliminary assumptions have been unsuitable, 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 searching for at a a lot decreased price to our customers.
Calculating our price discount
Since with DynamoDB Streams we’re instantly liable for our customers prices, we determined that we would have liked to exactly calculate the associated fee our customers incur as a result of method 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 centered on controlling the variety of DynamoDB Streams API calls we make to customers’ tables as this was beforehand the motive force of our customers’ DynamoDB prices.
Following is a breakdown of the associated fee we estimate with our newly reworked ingestion course of:
We have been completely satisfied to see that, with our optimizations, our customers ought to incur nearly no extra price on their DynamoDB tables attributable to Rockset!
Conclusion
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 a few of the challenges and tradeoffs we’ve confronted whereas working to make ingesting knowledge from DynamoDB into Rockset as seamless as attainable. In the event you’re thinking about studying extra about the way 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!
Different DynamoDB assets:
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