Goal/Help Requested:
Given a bunch of tables in Athena, write an sql query that writes a scheduled new file into s3 to be used for other purposes.
Limited aws background - currently teaching myself but could use some help if possible.
Background
I have a very large number of Athena tables that draw upon a very large number of s3 folders with extensive nesting.
For example a 'sales' Athena table that draws upon a 'sales' s3 bucket that has depth of countries>regions>states>cities>stores>week>day. I have similar other types of information.
I want to write various sql queries that take this information and apply some logic that writes back single files into s3 to be used for various other purposes I have.
What I'm looking for
Any sort of links/direction on how to do this. Again I'm pretty new to aws and maybe this is a simple ask, I just can't articulate it properly.
Related
I am fairly new to AWS and am a bit overwhelmed with the options for a task I have.
Have: I have a dimensional model in an S3 bucket (read only access), that has a folder structure and contains partitioned parquet files. This bucket will be updated daily (+40GB a day), with changes to both dim and fact tables. I need to get this data out of S3, but it's extremely inefficient to set up a boto3 connection and repeatedly pull the entire raw data and continuously check if the data has even been updated.
What I was thinking for a solution: To maintain updated tables in another S3 bucket that I create (likely Athena query outputs), where I can just pull in the updated changes, so that boto can just check if there is data in the new bucket and pull, reducing load.
Considerations:
I need some kind of event notification that triggers the Athena query. I was looking into Lambda or Cloudwatch, but unsure which is better or restraints.
For the fact tables, I need an Athena query that gets the most recent "Last Updated" timestamp from the updated data. And then updates the updated bucket tables to include all the raw data that is greater than the found timestamp.
FYI: I am working with partitioned data, and I am not sure if I can just work with the tables as partitions (part-0000dim-table-3.parquet) or if additional steps are required to work with partitions.
For the dim tables, I need to somehow scan the entire table for changes (dim tables are a combination of SCD 0,1,2)... unsure how best to do this. In the worst case, I could just point the boto3 connection to the raw dim tables whenever the fact tables update.
What AWS APIs, workflows, should I think about using?
I am unclear on the constraints that I could run into with either Lambda, Cloudwatch, Athena step functions, etc. and trying to learn as I go. I am also struggling on how to compare Athena query results across the two buckets.
Thank you very much & if there is any more information that would help, just let me know!!
I am working on a POC where we have millions of existing S3 compressed json files (uncompressed 3+ MB, with nested objects and arrays) and more being added every few minutes. We need to perform computations on top of the uncompressed data (per file basis) and store it to a DB table where we can then perform some column operations. The most common solution I found online is
S3 (Add/update event notification) => SQS (main queue => dlq queue) <=> AWS lambda
We have a DB table for all S3 bucket key names that are being successfully loaded, so I can query this table and use the AWS SDK Node.js package to send messages to the SQS main queue. For newly added/updated files, S3 event notification will take care of it.
I think the above architecture will work in my case, but are there any other AWS services I should look at?
I looked at AWS Athena which can read my compressed files and can give me the raw output but since I have big nested objects and arrays on top of which I need to perform computation, I am not sure if it's ideal to write such complex logic in SQL.
I would really appreciate some guidance here.
If you plan to query the data in the future in ways you can't anticipate, I would strongly suggest you explore the Athena solution, since you would be plugging a very powerful SQL engine on top of your data. Athena can query directly compressed json and export to other data formats that are a lot more efficient to query (like parquet or orc) and support complex data structures.
The flow would be:
S3 (new file) => Athena ETL (json to, say, parquet)
see e.g. here.
For already existing data you can do a one-off query to convert it to the appropriate format (partitioning would be useful if your data volume is big as it seems it is). Having good partitioning is key to obtain good performance on Athena and you will need to think carefully about it on your ETL. More on partitioning, e.g., there.
At my organization, we are using a stack of AWS S3, AWS Glue, and Athena to drive some reporting of internal metrics. In general, this stack is great for quick set up for reporting off of raw data (stored in S3). The problem we've come against is what to do if we notice we need to somehow update the data that's already stored in S3. For example, we want to update values in a column that have a certain string to update that value.
Unlike a database, we can't just run a query to update all the existing data. I've tried to see if we can utilize Glue Jobs to accomplish this, but from my limited understanding, it doesn't seem like it's meant to do ETL from a bucket back to the same bucket.
The only thing I can think is to write a custom tool that iterates through an S3 bucket, loads a file, provides the transformation, and puts it back, overwriting the original. It seems there has to be a better way though.
Updates are not handled in a native way in a traditional hive-like warehousing solution, which I deem Athena to be. A common solution is a kind of engineering workaround where you do "insert overwrite" a partition (borrowing Hive syntax, possible in Presto and hopefully also possible in Athena, which is based on Presto).
Other solutions include creating new tables and atomically replacing a view, which users are supposed to query, instead of querying the underlying table(s) directly.
As this is a common problem, there are also some ready to use solutions to it, but I do not know whether which/whether they are possible with Athena. They are certainly possible with Presto (Presto SQL):
Hive ACID transactional tables (updates currently required Hive runtime)
Data Lake (open sourced by Databricks; updates currently require Spark runtime)
Hudi (I know little about this one)
For a project we've inherited we have a large-ish set of legacy data, 600GB, that we would like to archive, but still have available if need be.
We're looking at using the AWS data pipeline to move the data from the database to be in S3, according to this tutorial.
https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-object-copyactivity.html
However, we would also like to be able to retrieve a 'row' of that data if we find the application is actually using a particular row.
Apparently that tutorial puts all of the data from a table into a single massive CSV file.
Is it possible to split the data up into separate files, with 100 rows of data in each file, and giving each file a predictable file name, such as:
foo_data_10200_to_10299.csv
So that if we realise we need to retrieve row 10239, we can know which file to retrieve, and download just that, rather than all 600GB of the data.
If your data is stored in CSV format in Amazon S3, there are a couple of ways to easily retrieve selected data:
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL.
S3 Select (currently in preview) enables applications to retrieve only a subset of data from an object by using simple SQL expressions.
These work on compressed (gzip) files too, to save storage space.
See:
Welcome - Amazon Athena
S3 Select and Glacier Select – Retrieving Subsets of Objects
I have a web app that needs to send reports on its usage, I want to use Amazon RedShift as a data warehouse for that purpose,
How should i collect the data ?
Every time, the user interact with my app, i want to report that.. so when should i write the files to S3 ? and how many ?
What i mean is:
- If do not send the info immediately, then I might lose it as a result of a connection lost, or from some bug in my system while its been collected and get ready to be sent to S3...
- If i do write files to S3 on each user interaction, i will end up with hundreds of files (on each file has minimal data), that need to be managed, sorted, deleted after been copied to RedShift.. that dose not seems like a good solution .
What am i missing? Should i use DynamoDB instead, Should i use simple insert into Redshift instead !?
If i do need to write the data to DynamoDB, should i delete the hold table after been copied .. what are the best practices ?
On any case what are the best practices to avoid data duplication in RedShift ?
Appreciate the help!
It is preferred to aggregate event logs before ingesting them into Amazon Redshift.
The benefits are:
You will use the parallel nature of Redshift better; COPY on a set of larger files in S3 (or from a large DynamoDB table) will be much faster than individual INSERT or COPY of a small file.
You can pre-sort your data (especially if the sorting is based on event time) before loading it into Redshift. This is also improve your load performance and reduce the need for VACUUM of your tables.
You can accumulate your events in several places before aggregating and loading them into Redshift:
Local file to S3 - the most common way is to aggregate your logs on the client/server and every x MB or y minutes upload them to S3. There are many log appenders that are supporting this functionality, and you don't need to make any modifications in the code (for example, FluentD or Log4J). This can be done with container configuration only. The down side is that you risk losing some logs and these local log files can be deleted before the upload.
DynamoDB - as #Swami described, DynamoDB is a very good way to accumulate the events.
Amazon Kinesis - the recently released service is also a good way to stream your events from the various clients and servers to a central location in a fast and reliable way. The events are in order of insertion, which makes it easy to load it later pre-sorted to Redshift. The events are stored in Kinesis for 24 hours, and you can schedule the reading from kinesis and loading to Redshift every hour, for example, for better performance.
Please note that all these services (S3, SQS, DynamoDB and Kinesis) allow you to push the events directly from the end users/devices, without the need to go through a middle web server. This can significantly improve the high availability of your service (how to handle increased load or server failure) and the cost of the system (you only pay for what you use and you don't need to have underutilized servers just for logs).
See for example how you can get temporary security tokens for mobile devices here: http://aws.amazon.com/articles/4611615499399490
Another important set of tools to allow direct interaction with these services are the various SDKs. For example for Java, .NET, JavaScript, iOS and Android.
Regarding the de-duplication requirement; in most of the options above you can do that in the aggregation phase, for example, when you are reading from a Kinesis stream, you can check that you don't have duplications in your events, but analysing a large buffer of events before putting into the data store.
However, you can do this check in Redshift as well. A good practice is to COPY the data into a staging tables and then SELECT INTO a well organized and sorted table.
Another best practice you can implement is to have a daily (or weekly) table partition. Even if you would like to have one big long events table, but the majority of your queries are running on a single day (the last day, for example), you can create a set of tables with similar structure (events_01012014, events_01022014, events_01032014...). Then you can SELECT INTO ... WHERE date = ... to each of this tables. When you want to query the data from multiple days, you can use UNION_ALL.
One option to consider is to create time series tables in DynamoDB where you create a table every day or week in DynamoDB to write every user interaction. At the end of the time period (day, hour or week), you can copy the logs on to Redshift.
For more details, on DynamoDB time series table see this pattern: http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/GuidelinesForTables.html#GuidelinesForTables.TimeSeriesDataAccessPatterns
and this blog:
http://aws.typepad.com/aws/2012/09/optimizing-provisioned-throughput-in-amazon-dynamodb.html
For Redshift DynamoDB copy: http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/RedshiftforDynamoDB.html
Hope this helps.
Though there is already an accepted answer here, AWS launched a new service called Kinesis Firehose which handles the aggregation according to user defined intervals, a temporary upload to s3 and the upload (SAVE) to redshift, retries and error handling, throughput management,etc...
This is probably the easiest and most reliable way to do so.
You can write data to CSV file on local disk and then run Python/boto/psycopg2 script to load data to Amazon Redshift.
In my CSV_Loader_For_Redshift I do just that:
Compress and load data to S3 using boto Python module and multipart upload.
conn = boto.connect_s3(AWS_ACCESS_KEY_ID,AWS_SECRET_ACCESS_KEY)
bucket = conn.get_bucket(bucket_name)
k = Key(bucket)
k.key = s3_key_name
k.set_contents_from_file(file_handle, cb=progress, num_cb=20,
reduced_redundancy=use_rr )
Use psycopg2 COPY command to append data to Redshift table.
sql="""
copy %s from '%s'
CREDENTIALS 'aws_access_key_id=%s;aws_secret_access_key=%s'
DELIMITER '%s'
FORMAT CSV %s
%s
%s
%s;""" % (opt.to_table, fn, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY,opt.delim,quote,gzip, timeformat, ignoreheader)
Just being a little selfish here and describing exactly what Snowplow ,an event analytics platform does. They use this awesome unique way of collecting event logs from the client and aggregating it on S3.
They use Cloudfront for this. What you can do is, host a pixel in one of the S3 buckets and put that bucket behind a CloudFront distribution as an origin. Enable logs to an S3 bucket for the same CloudFront.
You can send logs as url parameters whenever you call that pixel on your client (similar to google analytics). These logs can then be enriched and added to Redshift database using Copy.
This solves the purpose of aggregation of logs. This setup will handle all of that for you.
You can also look into Piwik which is an open source analytics service and see if you can modify it specific to your needs.