How to determine read capacity unit for a table when get query returns different number of items in each api call(eg:- one get query returns 50 items , another get query returns 500 items from the same table )
Its all about averages.
If your average fluctuates significantly over some time period e.g. over the course of a day, you can use autoscaling.
If your table doesn't see enough requests to have a stable average throughput, you probably don't need to worry too much. Give yourself some breathing room but also keep in mind that DynamoDB allows bursting so you don't need to be too exact over time.
Also consider how your data is distributed and the relative temperatures of your data in your table. Read and write throughput gets spread across all partitions equally, meaning cold partitions get an equal read throughput as hot partitions. It is always the goal to structure your data so that it is evenly distributed and equal temperature.
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I am using DynamoDB for storing data. And I see 1MB is the hard limit for a query to return. I have a case that queries a table to fetch 1MB of data in one partition. I'd like to know what the best performance I can get.
Based on DynamoDB doc, one partition can have a maximum of 3000 RCU. If I send an eventual consistency read, it should support responding 3000 * 8KB = 24000KB = 23MB per second.
If I send one query request to fetch 1MB from one partition, does this mean it should respond 1/23 second = 43 milliseconds?
I am testing in a lambda sends a query to DynamoDB with XRay enabled. It shows me the query takes 300ms more based on XRay trace. So I don't understand why may cause the long latency.
What can I do if I want to reduce the latency to a single-digit millisecond? I don't want to split the partition since 1MB is not really big size.
DynamoDB really is capable of single-digit millisecond latency, but if the item size is small enough to fit into 1 RCU. Reading 1 MB of data from a database in <10ms is a challenging task itself.
Here is what you can try:
Split your read operation into two.
One will query with ScanIndexForward: true + Limit: N/2 and another will query with ScanIndexForward: false + Limit: N/2. The idea is to query the same data from both ends to the middle.
Do this in parallel and then you merge two responses into one.
However, this is likely will decrease latency from 300 to 150ms, which is still not <10ms.
Use DAX - DynamoDB Caching Layer
If your 1 MB of data is spread across thousands of items, consider using fewer items but each item will hold more data inside itself.
Consider using a compression algorithm like brotli to compress the data you store in 1 DynamoDB item. Once I had success with this approach. Depending on the format, it can easily reduce your data size by 4x, which translates into ~4x faster query time! Which could be 8x faster with the approach described in item #1.
Also, beware, that constantly reading 1 MB of data from a database will incur huge costs.
I made a table with 1346 items, each item being less than 4KB in size. I provisioned 1 read capacity unit, so I'd expect on average 1 item read per second. However, a simple scan of all 1346 items returns almost immediately.
What am I missing here?
This is likely down to burst capacity in which you gain your capacity over a 300 second period to use for burstable actions (such as scanning an entire table).
This would mean if you used all of these credits other interactions would suffer as they not have enough capacity available to them.
You can see the amount of consumed WCU/RCU via either CloudWatch metrics or within the DynamoDB interface itself (via the Metrics tab).
You don't give a size for your entries except to say "each item being less than 4KB". How much less?
1 RCU will support 2 eventually consistent reads per second of items up to 4KB.
To put that another way, with 1 RCU and eventually consistent reads, you can read 8KB of data per second.
If you records are 4KB, then you get 2 records/sec
1KB, 8/sec
512B, 16/sec
256B, 32/sec
So the "burst" capability already mentioned allowed you to use 55 RCU.
But the small size of your records allowed that 55 RCU to return the data "almost immediately"
There are two things working in your favor here - one is that a Scan operation takes significantly fewer RCUs than you thought it did for small items. The other thing is the "burst capacity". I'll try to explain both:
The DynamoDB pricing page says that "For items up to 4 KB in size, one RCU can perform two eventually consistent read requests per second.". This suggests that even if the item is 10 bytes in size, it costs half an RCU to read it with eventual consistency. However, although they don't state this anywhere, this cost is only true for a GetItem operation to retrieve a single item. In a Scan or Query, it turns out that you don't pay separately for each individual item. Instead, these operations scan data stored on disk sequentially, and you pay for the amount of data thus read. If you 1000 tiny items and the total size that DynamoDB had to read from disk was 80KB, you will pay 80KB/4KB/2, or 10 RCUs, not 500 RCUs.
This explains why you read 1346 items, and measured only 55 RCUs, not 1346/2 = 673.
The second thing working in your favor is that DynamoDB has the "burst capacity" capability, described here:
DynamoDB currently retains up to 5 minutes (300 seconds) of unused read and write capacity. During an occasional burst of read or write activity, these extra capacity units can be consumed quickly—even faster than the per-second provisioned throughput capacity that you've defined for your table.
So if your database existed for 5 minutes prior to your request, DynamoDB saved 300 RCUs for you, which you can use up very quickly. Since 300 RCUs is much more than you needed for your scan (55), your scan happened very quickly, without throttling.
When you do a query, the RCU count applies to the quantity of data read without considering the number of items read. So if your items are small, say a few bytes each, they can easily be queried inside a single 4KB RCU.
This is especially useful when reading many items from DynamoDB as well. It's not immediately obvious that querying many small items is far cheaper and more efficient than BatchGetting them.
Recently, I've experienced an issue with AWS Athena when there is quite high number of partitions.
The old version had a database and tables with only 1 partition level, say id=x. Let's take one table; for example, where we store payment parameters per id (product), and there are not plenty of IDs. Assume its around 1000-5000. Now while querying that table with passing id number on where clause like ".. where id = 10". The queries were returned pretty fast actually. Assume we update the data twice a day.
Lately, we've been thinking to add another partition level for day like, "../id=x/dt=yyyy-mm-dd/..". This means that partition number grows xID times per day if a month passes and if we have 3000 IDs, we'd approximately get 3000x30=90000 partitions a month. Thus, a rapid grow in number of partitions.
On, say 3 months old data (~270k partitions), we'd like to see a query like the following would return in at most 20 seconds or so.
select count(*) from db.table where id = x and dt = 'yyyy-mm-dd'
This takes like a minute.
The Real Case
It turns out Athena first fetches the all partitions (metadata) and s3 paths (regardless the usage of where clause) and then filter those s3 paths that you would like to see on where condition. The first part (fetching all s3 paths by partitions lasts long proportionally to the number of partitions)
The more partitions you have, the slower the query executed.
Intuitively, I expected that Athena fetches only s3 paths stated on where clause, I mean this would be the one way of magic of the partitioning. Maybe it fetches all paths
Does anybody know a work around, or do we use Athena in a wrong way ?
Should Athena be used only with small number of partitions ?
Edit
In order to clarify the statement above, I add a piece from support mail.
from Support
...
You mentioned that your new system has 360000 which is a huge number.
So when you are doing select * from <partitioned table>, Athena first download all partition metadata and searched S3 path mapped with
those partitions. This process of fetching data for each partition
lead to longer time in query execution.
...
Update
An issue opened on AWS forums. The linked issue raised on aws forums is here.
Thanks.
This is impossible to properly answer without knowing the amount of data, what file formats, and how many files we're talking about.
TL; DR I suspect you have partitions with thousands of files and that the bottleneck is listing and reading them all.
For any data set that grows over time you should have a temporal partitioning, on date or even time, depending on query patterns. If you should have partitioning on other properties depends on a lot of factors and in the end it often turns out that not partitioning is better. Not always, but often.
Using reasonably sized (~100 MB) Parquet can in many cases be more effective than partitioning. The reason is that partitioning increases the number of prefixes that have to be listed on S3, and the number of files that have to be read. A single 100 MB Parquet file can be more efficient than ten 10 MB files in many cases.
When Athena executes a query it will first load partitions from Glue. Glue supports limited filtering on partitions, and will help a bit in pruning the list of partitions – so to the best of my knowledge it's not true that Athena reads all partition metadata.
When it has the partitions it will issue LIST operations to the partition locations to gather the files that are involved in the query – in other words, Athena won't list every partition location, just the ones in partitions selected for the query. This may still be a large number, and these list operations are definitely a bottleneck. It becomes especially bad if there is more than 1000 files in a partition because that's the page size of S3's list operations, and multiple requests will have to be made sequentially.
With all files listed Athena will generate a list of splits, which may or may not equal the list of files – some file formats are splittable, and if files are big enough they are split and processed in parallel.
Only after all of that work is done the actual query processing starts. Depending on the total number of splits and the amount of available capacity in the Athena cluster your query will be allocated resources and start executing.
If your data was in Parquet format, and there was one or a few files per partition, the count query in your question should run in a second or less. Parquet has enough metadata in the files that a count query doesn't have to read the data, just the file footer. It's hard to get any query to run in less than a second due to the multiple steps involved, but a query hitting a single partition should run quickly.
Since it takes two minutes I suspect you have hundreds of files per partition, if not thousands, and your bottleneck is that it takes too much time to run all the list and get operations in S3.
Lets say, I have several items in the dynamodb with the same partition-key and different sort-keys.
Is there any difference between consumed read capacity units if I query the records using a sort-key constraint in a single go v/s query each item individually? Assume that the number of sort-keys to be fetched at-a-time are around 50. The official-documentation says that
One read capacity unit represents one strongly consistent read per
second, or two eventually consistent reads per second, for an item up
to 4 KB in size.
From this definition, it doesn't seem that there should be a difference since this definition is independent of how we query the database.
Apart from additional network delay, does the second approach have any other downside?
Please note that the costing is based on Read Capacity Units (RCU) and Write Capacity Units (WCU).
RCU formula:-
RCU = read capacity unit per item × number of reads per second
Before going into the below calculation, calculate the item size. You can get the item size from AWS console.
Go to the dynamodb table on AWS console --> Overview tab --> See at the bottom.
Lets talk about RCU. In the above case,
Scenario 1 - Getting all the data in one go using hash key only:-
In this scenario, the number of items read will be high (i.e. 50 items data). Calculate the size and check how many RCU required.
Scenario 2 - Getting the data multiple times using hash key and sort key:-
In this scenario, the API will be called multiple times. So, the number of reads per second will go up. Calculate the number of reads required and check how many RCU required.
Compare the RCU calculated in scenario 1 and 2. Choose the option which has less RCU in order to save cost.
I would like to store 1M+ different time series in Amazon's DynamoDb database. Each time series will have about 50K data points. A data point is comprised of a timestamp and a value.
The application will add new data points to time series frequently (all the time) and will retrieve (usually the whole time series) time series from time to time, for analytics.
How should I structure the database? Should I create a separate table for each timeseries? Or should I put all data points in one table?
Assuming your data is immutable and given the size, you may want to consider Amazon Redshift; it's written for petabyte-sized reporting solutions.
In Dynamo, I can think of a few viable designs. In the first, you could use one table, with a compound hash/range key (both strings). The hash key would be the time series name, the range key would be the timestamp as an ISO8601 string (which has the pleasant property that alphabetical ordering is also chronological ordering), and there would be an extra attribute on each item; a 'value'. This gives you the abilty to select everything from a time series (Query on hashKey equality) and a subset of a time series (Query on hashKey equality and rangeKey BETWEEN clause). However, your main problem is the "hotspot" problem: internally, Dynamo will partition your data by hashKey, and will disperse your ProvisionedReadCapacity over all your partitions. So you may have 1000 KB of reads a second, but if you have 100 partitions, then you have only 10 KB a second for each partition, and reading all data from a single time series (single hashKey) will only hit one partition. So you may think your 1000 KB of reads gives you 1 MB a second, but if you have 10 MB stored it might take you much longer to read it, as your single partition will throttle you much more heavily.
On the upside, DynamoDB has an extremely high but costly upper-bound on scaling; if you wanted you could pay for 100,000 Read Capacity units, and have sub-second response times on all of that data.
Another theoretical design would be to store every time series in a separate table, but I don't think DynamoDB is meant to scale to millions of tables, so this is probably a no-go.
You could try and spread out your time series across 10 tables where "highly read" data goes in table 1, "almost never read data" in table 10, and all other data somewhere in between. This would let you "game" the provisioned throughput / partition throttling rules, but at a high degree of complexity in your design. Overall, it's probably not worth it; where do you new time series? How do you remember where they all are? How do you move a time series?
I think DynamoDB supports some internal "bursting" on these kinds of reads from my own experience, and it's possible my numbers are off, and you will get adequete performance. However my verdict is to look into Redshift.
How about dripping each time series into JSON or similar and store in S3. At most you'd need a lookup from somewhere like Dynamo.
You still may need redshift to process your inputs.