I have a large set of history log files on aws s3 that sum billions of lines,
I used a glue crawler with a grok deserializer to generate an external table on Athena, but querying it has proven to be unfeasible.
My queries have timed out and I am trying to find another way of handling this data.
From what I understand, through Athena, external tables are not actual database tables, but rather, representations of the data in the files, and queries are run over the files themselves, not the database tables.
How can I turn this large dataset into a query friendly structure?
Edit 1: For clarification, I am not interested in reshaping the hereon log files, those are taken care of. Rather, I want a way to work with the current file base I have on s3. I need to query these old logs and at its current state it's impossible.
I am looking for a way to either convert these files into an optimal format or to take advantage of the current external table to make my queries.
Right now, by default of the crawler, the external tables are only partitined by day and instance, my grok pattern explodes the formatted logs into a couple more columns that I would love to repartition on, if possible, which I believe would make my queries easier to run.
Your where condition should be on partitions (at-least one condition). By sending support ticket, you may increase athena timeout. Alternatively, you may use Redshift Spectrum
But you may seriously thing to optimize query. Athena query timeout is 30min. It means your query ran for 30mins before timed out.
By default athena times out after 30 minutes. This timeout period can be increased but raising a support ticket with AWS team. However, you should first optimize your data and query as 30 minutes is good time for executing most of the queries.
Here are few tips to optimize the data that will give major boost to athena performance:
Use columnar formats like orc/parquet with compression to store your data.
Partition your data. In your case you can partition your logs based on year -> month -> day.
Create larger and lesser number of files per partition instead of small and more number of files.
The following AWS article gives detailed information for performance tuning in amazon athena
Top 10 performance tuning tips for amazon-athena
Related
Based on my research, the easiest and the most straight forward way to get metadata out of Glue's Data Catalog, is using Athena and querying the information_schema database. The article below has come up frequently in my research and is written by Amazon's team:
Querying AWS Glue Data Catalog
However, under the section titled Considerations and limitations the following is written:
Querying information_schema is most performant if you have a small to moderate amount of AWS Glue metadata. If you have a large amount of metadata, errors can occur.
Unfortunately, in this article, there do not seem to be any indications or suggestion regarding what constitutes as "large amount of metadata" and exactly what errors could occur when the metadata is large and one needs to query the metadata.
My question is, how to deal with the issue related to the ever growing size of data catalog's metadata so that one would never encounter errors when using Athena to query the metadata?
Is there a best practice for this? Or perhaps a better solution for getting the same metadata that querying the catalog using Athena provides without multiple or great many API calls (using boto3, Hive DDL etc)?
I talked to AWS Support and did some research on this. Here's what I gathered:
The information_schema is built at query execution time, there doesn't seem to be any caching.
If you access information_schema.tables, it will make separate calls for each schema you have to the Hive Metastore (Glue Data Catalog).
If you access information_schema.columns, it will make separate calls for each schema and each table in that schema you have to the Hive Metastore.
These queries are affected by the general service quotas. In this case, DML queries like your select must finish within 30 minutes.
If your Glue Data Catalog has many thousands of schemas, tables, and columns all of this may result in slow performance. As a rough guesstimate support told me that you should be fine as long as you have less than ~ 10000 tables, which should be the case for most people.
I am running a simple Athena query as in
SELECT * FROM "logs"
WHERE parse_datetime(requestdatetime,'dd/MMM/yyyy:HH:mm:ss Z')
BETWEEN parse_datetime('2021-12-01:00:00:00','yyyy-MM-dd:HH:mm:ss')
AND
parse_datetime('2021-12-21:19:00:00','yyyy-MM-dd:HH:mm:ss');
However this times out due to the default DML 30 min timeout.
The entries of the path I am querying are a few millions.
Is there a way to address this in Athena or is there a better suited alternative for this purpose?
This is normally solved with partitioning. For data that's organized by date, partition projection is the way to go (versus an explicit partition list that's updated manually or via Glue crawler).
That, of course, assumes that your data is organized by the partition (eg, s3://mybucket/2021/12/21/xxx.csv). If not, then I recommend changing your ingest process as a first step.
You my want to change your ingest process anyway: Athena isn't very good at dealing with a large number of small files. While the tuning guide doesn't give an optimal filesize, I recommend at least a few tens of megabytes. If you're getting a steady stream of small files, use a scheduled Lambda to combine them into a single file. If you're using Firehose to aggregate files, increase the buffer sizes / time limits.
And while you're doing that, consider moving to a columnar format such as Parquet if you're not already using it.
I am looking into different Big Data solutions and have not been able to find a clear answer or documentation on what might be the best approach and frameworks/services to use to address my Big Data use-case.
My Use-case:
I have a data producer that will be sending ~1-2 billion events to a
Kinesis Data Firehose delivery stream daily.
This data needs to be stored in some data lake / data warehouse, aggregated, and then
loaded into DynamoDB for our service to consume the aggregated data
in its business logic.
The DynamoDB table needs to be updated hourly. (hourly is not a hard requirement but we would like DynamoDB to be updated as soon as possible, at the longest intervals of daily updates if required)
The event schema is similar to: customerId, deviceId, countryCode, timestamp
The aggregated schema is similar to: customerId, deviceId, countryCode (the aggregation is on the customerId's/deviceId's MAX(countryCode) for each day over the last 29 days, and then the MAX(countryCode) overall over the last 29 days.
Only the CustomerIds/deviceIds that had their countryCode change from the last aggregation (from an hour ago) should be written to DynamoDB to keep required write capacity units low.
The raw data stored in the data lake / data warehouse needs to be deleted after 30 days.
My proposed solution:
Kinesis Data Firehose delivers the data to a Redshift staging table (by default using S3 as intermediate storage and then using the COPY command to load to Redshift)
An hourly Glue job that:
Drops the 30 day old time-series table and creates a new time-series table for today in Redshift if this is the first job run of a new day
Loads data from staging table to the appropriate time-series table
Creates a view on top of the last 29 days of time-series tables
Aggregates by customerId, deviceId, date, and MAX(CountryCode)
Then aggregates by customerId, deviceId, MAX(countryCode)
Writes the aggregated results to an S3 bucket
Checks the previous hourly Glue job's run aggregated results vs. the current runs aggregated results to find the customerIds/deviceIds that had their countryCode change
Writes the customerIds/deviceIds rows that had their countryCode change to DynamoDB
My questions:
Is Redshift the best storage choice here? I was also considering using S3 as storage and directly querying data from S3 using a Glue job, though I like the idea of a fully-managed data warehouse.
Since our data has a fixed retention period of 30 days, AWS documentation: https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-time-series-tables.html suggests to use time-series tables and running DROP TABLE on older data that needs to be deleted. Are there other approaches (outside of Redshift) that would make the data lifecycle management easier? Having the staging table, creating and loading into new time-series tables, dropping older time-series tables, updating the view to include the new time-series table and not the one that was dropped could be error prone.
What would be an optimal way to find the the rows (customerId/deviceId combinations) that had their countryCode change since the last aggregation? I was thinking the Glue job could create a table from the previous runs aggregated results S3 file and another table from the current runs aggregated results S3 file, run some variation of a FULL OUTER JOIN to find the rows that have different countryCodes. Is there a better approach here that I'm not aware of?
I am a newbie when it comes to Big Data and Big Data solutions so any and all input is appreciated!
tldr: Use step functions, not Glue. Use Redshift Spectrum with data in S3. Otherwise you overall structure looks on track.
You are on the right track IMHO but there are a few things that could be better. Redshift is great for sifting through tons of data and performing analytics on it. However I'm not sure you want to COPY the data into Redshift if all you are doing is building aggregates to be loaded into DDB. Do you have other analytic workloads being done that will justify storing the data in Redshift? Are there heavy transforms being done between the staging table and the time series event tables? If not you may want to make the time series tables external - read directly from S3 using Redshift Spectrum. This could be a big win as the initial data grouping and aggregating is done in the Spectrum layer in S3. This way the raw data doesn't have to be moved.
Next I would advise not using Glue unless you have a need (transform) that cannot easily be done elsewhere. I find Glue to require some expertise to get to do what you want and it sounds like you would just be using it for a data movement orchestrator. If this impression is correct you will be better off with a step function or even a data pipeline. (I've wasted way too much time trying to get Glue to do simple things. It's a powerful tool but make sure you'll get value from the time you will spend on it.)
If you are only using Redshift to do these aggregations and you go the Spectrum route above you will want to get as small a cluster as you can get away with. Redshift can be pricy and if you don't use its power, not cost effective. In this case you can run the cluster only as needed but Redshift boot up times are not fast and the smallest clusters are not expensive. So this is a possibility but only in the right circumstances. Depending on how difficult the aggregation is that you are doing you might want to look at Athena. If you are just running a few aggregating queries per hour then this could be the most cost effective approach.
Checking against the last hour's aggregations is just a matter of comparing the new aggregates against the old which are in S3. This is easily done with Redshift Spectrum or Athena as they can makes files (or sets of files) the source for a table. Then it is just running the queries.
In my opinion Glue is an ETL tool that can do high power transforms. It can do a lot of things but is not my first (or second) choice. It is touchy, requires a lot of configuration to do more than the basics, and requires expertise that many data groups don't have. If you are a Glue expert, knock you self out; If not, I would avoid.
As for data management, yes you don't want to be deleting tons of rows from the beginning of tables in Redshift. It creates a lot of data reorganization work. So storing your data in "month" tables and using a view is the right way to go in Redshift. Dropping tables doesn't create this housekeeping. That said if you organize you data in S3 in "month" folders then unneeded removing months of data can just be deleting these folders.
As for finding changing country codes this should be easy to do in SQL. Since you are comparing aggregate data to aggregate data this shouldn't be expensive either. Again Redshift Spectrum or Athena are tools that allow you to do this on S3 data.
As for being a big data newbie, not a worry, we all started there. The biggest difference from other areas is how important it is to move the data the fewest number of times. It sounds like you understand this when you say "Is Redshift the best storage choice here?". You seem to be recognizing the importance of where the data resides wrt the compute elements which is on target. If you need the horsepower of Redshift and will be accessing the data over and over again then the Redshift is the best option - The data is moved once to a place where the analytics need to run. However, Redshift is an expensive storage solution - it's not what it is meant to do. Redshift Spectrum is very interesting in that the initial aggregations of data is done in S3 and much reduced partial results are sent to Redshift for completion. S3 is a much cheaper storage solution and if your workload can be pattern-matched to Spectrum's capabilities this can be a clear winner.
I want to be clear that you have only described on area where you need a solution and I'm assuming that you don't have other needs for a Redshift cluster operating on the same data. This would change the optimization point.
We are building an web application to allow customers insight into their activity based on events currently streaming into ElasticSearch. A customer is an organisation sending messages to people.
A concern has been raised that a requirement to host this data for three years infers a very large amount of storage and high cost of implementation given Elasticsearch.
An alternative is to process each day's data into a report CSV stored in S3 and use something like Amazon Athena to perform the queries. Is Athena something that our application can send ad-hoc queries to in response to a web browser request? It is unlikely to generate a large volume of requests all the time, but I'm uncertain what the latency could be like.
Yes, Athena would be a possible solution to this use case – and done right it could also be fairly cheap.
Athena is not a low latency query engine, but for reporting purposes it's usually good enough. There's no way to say for sure without knowing more, but done right we're talking low single digit seconds.
You can approach this in different ways, either you do as you say and generate a CSV every day, store these for as long as you need, and run queries against them as needed. From your description it sounds like these CSVs would already be aggregates, and I assume they would be significantly less than a megabyte per customer per day. If you partition by customer and month you should be able to run queries for arbitrary time periods in seconds.
Another approach would be to store all your data on S3 and run queries on the full data set. As you stream data into ElasticSearch, stream it to S3 too. Depending on how you do that you probably need some ETL in the form of Lambda functions that partitions the data per customer and time (day or month depending on the volume). You can then run Athena queries on the full historical data set. The downside would be slower queries (double digit seconds for most queries, but I don't know your data volumes), but the upside would be full flexibility on what you can query.
With more details about the particulars of the use case I could help you with the details.
Athena is serverless. You can quickly query your data without having to set up and manage any servers or data warehouses. Just point to your data in Amazon S3, define the schema, and start querying using the built-in query editor.
Amazon Athena automatically executes queries in parallel, so most results come back within seconds/mins.
I am trying to use AWS Athena to provide analytics for an existing platform. Currently the flow looks like this:
Data is pumped into a Kinesis Firehose as JSON events.
The Firehose converts the data to parquet using a table in AWS Glue and writes to S3 either every 15 mins or when the stream reaches 128 MB (max supported values).
When the data is written to S3 it is partitioned with a path /year=!{timestamp:yyyy}/month=!{timestamp:MM}/day=!{timestamp:dd}/...
An AWS Glue crawler update a table with the latest partition data every 24 hours and makes it available for queries.
The basic flow works. However, there are a couple of problems with this...
The first (and most important) is that this data is part of a multi-tenancy application. There is a property inside each event called account_id. Every query that will ever be issued will be issued by a specific account and I don't want to be scanning all account data for every query. I need to find a scalable way query only the relevant data. I did look into trying to us Kinesis to extract the account_id and use it as a partition. However, this currently isn't supported and with > 10,000 accounts the AWS 20k partition limit quickly becomes a problem.
The second problem is file size! AWS recommend that files not be < 128 MB as this has a detrimental effect on query times as the execution engine might be spending additional time with the overhead of opening Amazon S3 files. Given the nature of the Firehose I can only ever reach a maximum size of 128 MB per file.
With that many accounts you probably don't want to use account_id as partition key for many reasons. I think you're fine limits-wise, the partition limit per table is 1M, but that doesn't mean it's a good idea.
You can decrease the amount of data scanned significantly by partitioning on parts of the account ID, though. If your account IDs are uniformly distributed (like AWS account IDs) you can partition on a prefix. If your account IDs are numeric partitioning on the first digit would decrease the amount of data each query would scan by 90%, and with two digits 99% – while still keeping the number of partitions at very reasonable levels.
Unfortunately I don't know either how to do that with Glue. I've found Glue very unhelpful in general when it comes to doing ETL. Even simple things are hard in my experience. I've had much more success using Athena's CTAS feature combined with some simple S3 operation for adding the data produced by a CTAS operation as a partition in an existing table.
If you figure out a way to extract the account ID you can also experiment with separate tables per account, you can have 100K tables in a database. It wouldn't be very different from partitions in a table, but could be faster depending on how Athena determines which partitions to query.
Don't worry too much about the 128 MB file size rule of thumb. It's absolutely true that having lots of small files is worse than having few large files – but it's also true that scanning through a lot of data to filter out just a tiny portion is very bad for performance, and cost. Athena can deliver results in a second even for queries over hundreds of files that are just a few KB in size. I would worry about making sure Athena was reading the right data first, and about ideal file sizes later.
If you tell me more about the amount of data per account and expected life time of accounts I can give more detailed suggestions on what to aim for.
Update: Given that Firehose doesn't let you change the directory structure of the input data, and that Glue is generally pretty bad, and the additional context you provided in a comment, I would do something like this:
Create an Athena table with columns for all properties in the data, and date as partition key. This is your input table, only ETL queries will be run against this table. Don't worry that the input data has separate directories for year, month, and date, you only need one partition key. It just complicates things to have these as separate partition keys, and having one means that it can be of type DATE, instead of three separate STRING columns that you have to assemble into a date every time you want to do a date calculation.
Create another Athena table with the same columns, but partitioned by account_id_prefix and either date or month. This will be the table you run queries against. account_id_prefix will be one or two characters from your account ID – you'll have to test what works best. You'll also have to decide whether to partition on date or a longer time span. Dates will make ETL easier and cheaper, but longer time spans will produce fewer and larger files, which can make queries more efficient (but possibly more expensive).
Create a Step Functions state machine that does the following (in Lambda functions):
Add new partitions to the input table. If you schedule your state machine to run once per day it can just add the partition that correspond to the current date. Use the Glue CreatePartition API call to create the partition (unfortunately this needs a lot of information to work, you can run a GetTable call to get it, though. Use for example ["2019-04-29"] as Values and "s3://some-bucket/firehose/year=2019/month=04/day=29" as StorageDescriptor.Location. This is the equivalent of running ALTER TABLE some_table ADD PARTITION (date = '2019-04-29) LOCATION 's3://some-bucket/firehose/year=2019/month=04/day=29' – but doing it through Glue is faster than running queries in Athena and more suitable for Lambda.
Start a CTAS query over the input table with a filter on the current date, partitioned by the first character(s) or the account ID and the current date. Use a location for the CTAS output that is below your query table's location. Generate a random name for the table created by the CTAS operation, this table will be dropped in a later step. Use Parquet as the format.
Look at the Poll for Job Status example state machine for inspiration on how to wait for the CTAS operation to complete.
When the CTAS operation has completed list the partitions created in the temporary table created with Glue GetPartitions and create the same partitions in the query table with BatchCreatePartitions.
Finally delete all files that belong to the partitions of the query table you deleted and drop the temporary table created by the CTAS operation.
If you decide on a partitioning on something longer than date you can still use the process above, but you also need to delete partitions in the query table and the corresponding data on S3, because each update will replace existing data (e.g. with partitioning by month, which I would recommend you try, every day you would create new files for the whole month, which means that the old files need to be removed). If you want to update your query table multiple times per day it would be the same.
This looks like a lot, and looks like what Glue Crawlers and Glue ETL does – but in my experience they don't make it this easy.
In your case the data is partitioned using Hive style partitioning, which Glue Crawlers understand, but in many cases you don't get Hive style partitions but just Y/M/D (and I didn't actually know that Firehose could deliver data this way, I thought it only did Y/M/D). A Glue Crawler will also do a lot of extra work every time it runs because it can't know where data has been added, but you know that the only partition that has been added since yesterday is the one for yesterday, so crawling is reduced to a one-step-deal.
Glue ETL is also makes things very hard, and it's an expensive service compared to Lambda and Step Functions. All you want to do is to convert your raw data form JSON to Parquet and re-partition it. As far as I know it's not possible to do that with less code than an Athena CTAS query. Even if you could make the conversion operation with Glue ETL in less code, you'd still have to write a lot of code to replace partitions in your destination table – because that's something that Glue ETL and Spark simply doesn't support.
Athena CTAS wasn't really made to do ETL, and I think the method I've outlined above is much more complex than it should be, but I'm confident that it's less complex than trying to do the same thing (i.e. continuously update and potentially replace partitions in a table based on the data in another table without rebuilding the whole table every time).
What you get with this ETL process is that your ingestion doesn't have to worry about partitioning more than by time, but you still get tables that are optimised for querying.