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.
Related
I was wondering what the usage of using a partitioned table in BigQuery is. It seems most of the queries seem to take about the same time to finish regardless of size (ignoring extremes, I'm generalizing), is this mainly a matter of using it to reduce costs on the bytes processed, or what is the main use case of partitioning tables in BQ?
https://cloud.google.com/bigquery/docs/creating-column-partitions
There are multiple benefits, mainly costs.
by writing a query to read only eg: 7 days of partitions instead of 7 years you have lower costs
partitions you don't touch for older than 90 days are at lower costs
you can clearly reload a day's data much more easier than having to work around
you are still recommended to use YEARly tables eg mytable_2018, but you are no longer required to have daily tables eg: mytable_20180101, this further leads to have simpler queries, also no longer a problem to read more than 1000 tables (which is a hard limit).
when you modify schema, you need to modify a few tables, you no longer need to script alters on thousands of table
this also means it's lover bytes processed and in the cloud platform can be better optimized and needs fewer resources
by reorganizing data into partitioned tables the query times will benefit in the future. As customers will move data, the cloud engineering team will optimize the service for better usage.
you see clear cost wise benefits if your existing data is at least a couple of terabytes.
I am having issues with amazon athena, I have a small bucket ( 36430 objects , 9.7 mb ) with 4 levels of partition ( my-bucket/p1=ab/p2=cd/p3=ef/p4=gh/file.csv ) but when I run the command
MSCK REPAIR TABLE db.table
is taking over 25 minutes, and I have plans to put data of the magnitude of TB on Athena and I won't do it if this issue remains
Does anybody know why is taking too long?
Thanks in advance
MSCK REPAIR TABLE can be a costly operation, because it needs to scan the table's sub-tree in the file system (the S3 bucket). Multiple levels of partitioning can make it more costly, as it needs to traverse additional sub-directories. Assuming all potential combinations of partition values occur in the data set, this can turn into a combinatorial explosion.
If you are adding new partitions to an existing table, then you may find that it's more efficient to run ALTER TABLE ADD PARTITION commands for the individual new partitions. This avoids the need to scan the table's entire sub-tree in the file system. It is less convenient than simply running MSCK REPAIR TABLE, but sometimes the optimization is worth it. A viable strategy is often to use MSCK REPAIR TABLE for an initial import, and then use ALTER TABLE ADD PARTITION for ongoing maintenance as new data gets added into the table.
If it's really not feasible to use ALTER TABLE ADD PARTITION to manage the partitions directly, then the execution time might be unavoidable. Reducing the number of partitions might reduce execution time, because it won't need to traverse as many directories in the file system. Of course, then the partitioning is different, which might impact query execution time, so it's a trade-off.
While the marked answer is technically correct, it doesn't address your real issue, which is that you have too many files.
I have a small bucket ( 36430 objects , 9.7 mb ) with 4 levels of
partition ( my-bucket/p1=ab/p2=cd/p3=ef/p4=gh/file.csv )
For such a small table, 36430 files creates a huge amount of overhead on S3, and the partitioning with 4 levels is super-overkill. The partitioning has hindered query performance rather than optimizing it. MSCK is slow because it is waiting for S3 listing among other things.
Athena would read the entire 9.7MB table if it were in one file faster than it would be able to list that huge directory structure.
I recommend removing the partitions completely, or if you really must have them then remove p2, p3 and p4 levels. Also consider processing it into another table to compact the files into larger ones.
Some suggest optimal file sizes are between 64MB and 4GB, which relates to the native block sizes on S3. It's also helpful to have a number of files that is some multiple of the workers in the cluster, although that is unknown with Athena. Your data is smaller than that range, so 1 or perhaps 8 files at most would be appropriate.
Some references:
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/#OptimizeFileSizes
https://www.upsolver.com/blog/small-file-problem-hdfs-s3
Use Athena Projection to manage partitions automatically.
I wonder why unloading from a big table (>100 bln rows) when selecting by a column, which is NOT a sort key or a part of sort key, is immensely faster for newly added data. How Redshift understands that it is time to stop sequential scan in the second scenario?
Time the query spent executing. 39m 37.02s:
UNLOAD ('SELECT * FROM production.some_table WHERE daytime BETWEEN
\\'2017-01-15\\' AND \\'2017-01-16\\'') TO ...
vs.
Time the query spent executing. 23.01s :
UNLOAD ('SELECT * FROM production.some_table WHERE daytime BETWEEN
\\'2017-06-24\\' AND \\'2017-06-25\\'') TO ...
Thanks!
Amazon Redshift uses zone maps to identify the minimum and maximum value stored in each 1MB block on disk. Each block only stores data related to a single column (eg daytime).
If the SORTKEY is not set to daytime, then the data is unsorted and any particular date could appear in many different blocks. If SORTKEY is used, then a particular date will only appear in a minimum number of blocks.
Your second query possibly executes faster, even without a SORTKEY, because you are querying data that was probably added recently and is therefore all stored together in just a few blocks. The historical data might be spread in many blocks because a VACUUM probably reordered the data based upon the correct SORTKEY. In fact, if you did a VACUUM now, you might find that your second query becomes slower.
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.
Is there a way to instruct Hive to split data into multiple output files? Or maybe cap the size of the output files.
I'm planning to use Redshift, which recommends splitting data into multiple files to allow parallel loading http://docs.aws.amazon.com/redshift/latest/dg/t_splitting-data-files.html
We preprocess all out data in hive, and I'm wondering if there's a way to create, say 10 1GB files which might make copying to redshift faster.
I was looking at https://cwiki.apache.org/Hive/adminmanual-configuration.html and https://cwiki.apache.org/confluence/display/Hive/Configuration+Properties but I can't find anything
There are a couple of ways you could go about splitting Hive output. The first and easiest way is to set the number of reducers. Since each reduces writes to its own output file, the number of reducers you specify will correspond to the number of output files written. Note that some Hive queries will not result in the number of reducers you specify (for example, SELECT COUNT(*) FROM some_table always results in one reducer). To specify the number of reducers run this before your query:
set mapred.reduce.tasks=10
Another way you could split into multiple output files would be to have Hive insert the results of your query into a partitioned table. This would result in at least one file per partition. For this to make sense you must have some reasonable column to partition on. For example, you wouldn't want to partition on a unique id column or you would have one file for each record. This approach will guarantee at least output file per partition, and at most numPartitions * numReducers. Here's an example (don't worry too much about hive.exec.dynamic.partition.mode, it needs to be set for this query to work).
hive.exec.dynamic.partition.mode=nonstrict
CREATE TABLE table_to_export_to_redshift (
id INT,
value INT
)
PARTITIONED BY (country STRING)
INSERT OVERWRITE TABLE table_to_export_to_redshift
PARTITION (country)
SELECT id, value, country
FROM some_table
To get more fine grained control, you can write your own reduce script to pass to hive and have that reduce script write to multiple files. Once you are writing your own reducer, you can do pretty much whatever you want.
Finally, you can forgo trying to maneuver Hive into outputting your desired number of files and just break them apart yourself once Hive is done. By default, Hive stores its tables uncompressed and in plain text in it's warehouse directory (ex, /apps/hive/warehouse/table_to_export_to_redshift). You can use Hadoop shell commands, a MapReduce job, Pig, or pull them into Linux and break them apart however you like.
I don't have any experience with Redshift, so some of my suggestions may not be appropriate for consumption by Redshift for whatever reason.
A couple of notes: Splitting files into more, smaller files is generally bad for Hadoop. You might get a speed increase for Redshift, but if the files are consumed by other parts of the Hadoop ecosystem (MapReduce, Hive, Pig, etc) you might see a performance loss if the files are too small (though 1GB would be fine). Also make sure that the extra processing/developer time is worth the time savings you get for paralleling your Redshift data load.