Data warehouse technologies are advancing towards interactive, real-time, and analytical solutions. In particular, cloud-based data warehouse technologies have reached new heights with the help of modern tools like Amazon Athena and Amazon Redshift. Comparing Athena to Redshift is not simple. Amazon Athena has an edge in terms of portability and cost, whereas Redshift stands tall in terms of performance and scale.
Athena is portable; its users need only to log in to the console, create a table, and start querying. Athena is a serverless service and does not need any infrastructure to create, manage, or scale data sets. It works directly on top of Amazon S3 data sets. Athena has an edge in terms of portability and cost, whereas Redshift stands tall in terms of performance and scale. On the other hand, Redshift is a petabyte-scale data warehouse used together with business intelligence tools for modern analytical solutions.
Unlike Athena, Redshift requires a cluster for which we need to upload the data extracts and build tables before we can query. Redshift is based on PostgreSQL 8. It can process structured, unstructured, and semi-structured data formats.
It is recommended to use Amazon Redshift on large sets of structured data. It is scalable enough that even if new nodes are added to the cluster, it can be easily accommodated with few configuration changes. Because it contains a number of replicaseven if any node is down, it interacts with other nodes and rebuilds the drive. It can be used for log analysis, clickstream events, and real-time data sets. Amazon Redshift requires a cluster to set itself up.
A significant amount of time is required to prepare and set up the cluster. Once the cluster is ready to use, we need to load data into the tables. This also comes with a lag time depending on the amount of data being loaded. In comparison, Amazon Athena is free from all such dependencies as it does not need infrastructure at all; it just creates its own external tables on top of Amazon S3 data sets.
Partitioning is important for reducing cost and improving performance. With Amazon Athena, partitioning limits the scope of data to be scanned. The number of partitions in Athena is restricted to 20, per table.
Amazo Redshift has distribution keys that are defined while loading the data in the server. It is very important to properly define distribution keys as they may have further consequences and impact on performances. Python packages like Numpy, Pandas, and Scipy are supported with Python version 2.
Although users cannot make network calls using UDFs, it facilitates the handling of complex Regex expressions that are not user-friendly. Amazon Athena does not have UDFs at all, thereby coming up short if the user has a very specific requirement that needs UDF implementation.
Amazon Redshift does not enforce any Primary Key constraint. We can upload the same data a number of times, however this can sometimes be dangerous as multiplied data can give inaccurate results. If we need a Primary Key constraint in our warehouse, it must be declared at the onset.
Amazon Athena works on top of the S3 data set only, therefore duplication is only possible if the S3 data sets contain duplicate values. Primary Keys in Athena are informational only and are not mandatory.
It supports all compressed formats, except LZO, for which can use Snappy instead. Amazon Athena supports complex data types like arrays, maps, and structs.
Redshift does not support complex data types like arrays and Object Identifier Types.If you've got a moment, please tell us what we did right so we can do more of it. Thanks for letting us know this page needs work. We're sorry we let you down. If you've got a moment, please tell us how we can make the documentation better. CloudTrail generates encrypted log files and stores them in Amazon S3. For example, you can use queries to identify trends and further isolate activity by attributes, such as source IP address or user.
A common application is to use CloudTrail logs to analyze operational activity for security and compliance. You can do this one of two ways:. Before you begin creating tables, you should understand a little more about CloudTrail and how it stores data.
This can help you create the tables that you need, whether you create them from the CloudTrail console or from Athena. The location of the log files depends on how you set up trails, the AWS Region or Regions in which you are logging, and other factors. CloudTrail Log File Examples.
CloudTrail Record Contents. CloudTrail Event Reference. To collect logs and save them to Amazon S3, enable CloudTrail for the console. Note the destination Amazon S3 bucket where you save the logs. Using the highest level in the object hierarchy gives you the greatest flexibility when you query using Athena. You can automatically create tables for querying CloudTrail logs directly from the CloudTrail console.
This is a fairly straightforward method of creating tables, but you can only create tables this way if the Amazon S3 bucket that contains the log files for the trail is in a Region supported by Amazon Athena, and you are logged in with an IAM user or role that has sufficient permissions to create tables in Athena.
Supported Data Types
For more information, see Setting Up. To create a table for a CloudTrail trail in the CloudTrail console. In Event historychoose Run advanced queries in Amazon Athena. For Storage locationchoose the Amazon S3 bucket where log files are stored for the trail to query. You can find out what bucket is associated with a trail by going to Trails and choosing the trail.
The bucket name is displayed in Storage location. Choose Create table.It can be extremely cost-effective both in terms of storage and in terms of query time to use nested fields rather than flatten out all your data.
Nested, repeated fields are very powerful, but the SQL required to query them looks a bit unfamiliar. Using these three in combination also makes some kinds of queries much, much easier to write.
In this article, I will build it piece-by-piece. But this gives us a jumble of rows that meet the necessary criteria. What we need is to get an ordered list of locations for each hurricane. Why not? Try it out! What we want is:. Selecting the name from the hurricanes table is quite obvious. But what does selecting track do?
Because track is an array, you get the whole array. How do we find the time at which the maximum category is reached? Do play around with some variants to understand what is happening:. If this article was helpful, tweet it. Learn to code for free. Get started. Stay safe, friends.
Learn to code from home. Use our free 2, hour curriculum. My first step was to create a history of hurricane locations. So, I make the four fields time, lat, lon, hurricane strength a struct. The struct allows me to retain the element-by-element relationship between these four columns. Order the array by time. Time at which maximum category is reached How do we find the time at which the maximum category is reached? Do play around with some variants to understand what is happening: Why do I have the.
Hint: it has to do with the name of the column.With the cloud wars heating up, Google and AWS tout two directly-competing serverless querying tools: Amazon Athena, an interactive query service that runs over Amazon S3; and Google BigQuery, a high-performance, decoupled database.
In this document we will take a closer look at these two services, and compare their real-world performance executing a series of SQL queries against the same dataset. There are plenty of good feature-by-feature comparison of BigQuery and Athena out there e. However, what we felt was lacking was a very clear and comprehensive comparison between what are arguably the two most important factors in a querying service: costs and performance.
Hence, the scope of this document is simple: evaluate how quickly the two services would execute a series of fairly complex SQL queries, and how much these queries would end up adding to your cloud bill.
We used the popular taxi rides in New York datasetwhich has also been used in previous benchmarking tests. We used the entire dataset of rides from to The process was straightforward enough for both services - however, as we shall proceed to see, data ingestion practices made a big difference. Query performance in Athena is dramatically impacted by implementing data preparation best practices on the data stored in S3.
While we ran our tests on a static dataset, in the real world Athena is often used to query streaming data. In these cases, there is often a separate challenge around access to fresh data, which requires continuously orchestrating complex ETL jobs on data being ingested at high-velocity. In order to address this, we simulated the way data would be ingested using batch or micro-batch processing toolssuch as AWS Glue or Apache Spark, by writing files containing a few minutes of data to S3 based on event processing time.
The end result was that we ran our Athena queries on three different versions of the same original dataset:. As you will see below, Athena queries on optimized data ran significantly faster, especially when in the case of 1-minute Parquet files fresher data. When the data is optimized, Athena and BigQuery performance is similar - queries took about the same time to return on average, with certain queries running faster in Athena, and others performing better in BigQuery.
All in all, BigQuery managed to eke out a 5. These results were even more dramatic on the 1-minute Parquet filesmeant to simulate streaming data being processed in near real-time. In this case, the fact that the data was not compacted meant Athena had to scan a much larger amount of files, which caused a significant slow-down in query performance and placed it far behind BigQuery. Here Athena wins in a knockout: Athena is significantly less expensive than BigQueryregardless of whether we optimize the data beforehand.
This could be explained by the fact that Athena pricing is based on scanning compressed data, whereas BigQuery looks at the size of the same data decompressed, or by some internal optimization in the way queries are executed, which we are not privy to. We will proceed to detail each query that we ran and the results we got from each service. You can find a summary of these results in this Google Sheet.
Calculate the percentage of rides in which an unusually high tip is left by the passenger, per pickup date:. Return aggregated taxi data including: trip distance, fare, tip, toll charges and amount of passengers:. Tutorials FAQ Documentation. The Data We used the popular taxi rides in New York datasetwhich has also been used in previous benchmarking tests.
Data Ingestion, Storage Optimization and Data Freshness Query performance in Athena is dramatically impacted by implementing data preparation best practices on the data stored in S3. Simulating Data Freshness While we ran our tests on a static dataset, in the real world Athena is often used to query streaming data. The end result was that we ran our Athena queries on three different versions of the same original dataset: Parquet files generated on every 5 minutes of data, to simulate 5-minute batch processing this is the shortest batch window you can get with AWS Glue todayand without optimizing the data on S3 in terms of file sizes and partitioning Parquet files generated on every 1 minute of data, to simulate 1-minute batch processing, also without optimizing storage An Upsolver Athena outputwhich processes data as a stream and automatically creates optimized data on S3: writing 1-minute Parquet files, but later merging these into larger files learn more about compaction and how we deal with small files ; as well as ensuring optimal partitioning, compression and Hive integration.
Athena and BigQuery both rely on pooled resources, which means they do not guarantee consistent performance. The same query could take 10 seconds to return once, and 7 seconds immediately afterwards.
To try and address this we ran each query several times and the data we present is an average sample.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. For more information about working with data sources, see Connecting to Data Sources. When you run a Data Definition Language DDL query that modifies schema, Athena writes the metadata to the metastore associated with the data source.How do I use the results of my Amazon Athena query in another query?
When you run a query, Athena saves the results of a query in a query result location that you specify. This allows you to view query history and to download and view query results sets. This section provides guidance for running Athena queries on common data sources and data types using a variety of SQL statements.
General guidance is provided for working with common structures and operators—for example, working with arrays, concatenating, filtering, flattening, and sorting. Skip to content. Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Branch: master. Find file Copy path. Cannot retrieve contributors at this time.
Raw Blame History. This is true even after storage class objects are restored. To make the restored objects that you want to query readable by Athena, copy the restored objects back into Amazon S3 to change their storage class.
However, as long the as the Amazon S3 bucket policy does not explicitly deny requests to objects not made through Amazon S3 access points, the objects should be accessible from Athena for requestors that have the right object access permissions.
To work around this limitation, rename the files. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.If you've got a moment, please tell us what we did right so we can do more of it. Thanks for letting us know this page needs work. We're sorry we let you down. If you've got a moment, please tell us how we can make the documentation better. Your source data often contains arrays with complex data types and nested structures.
Examples in this section show how to change element's data type, locate elements within arrays, and find keywords using Athena queries. Filtering Arrays Using the. Filtering Arrays with Nested Values. The examples in this section use ROW as a means to create sample data to work with. When you query tables within Athena, you do not need to create ROW data types, as they are already created from your data source.
Large arrays often contain nested structures, and you need to be able to filter, or search, for values within them.
Querying AWS CloudTrail Logs
To filter an array that includes a nested structure by one of its child elements, issue a query with an UNNEST operator. It takes as an input a regular expression pattern to evaluate, or a list of terms separated by a pipeevaluates the pattern, and determines if the specified string contains it.
The regular expression pattern needs to be contained within the string, and does not have to match it. Consider an array of sites containing their hostname, and a flaggedActivity element.
Please refer to your browser's Help pages for instructions. Did this page help you? Thanks for letting us know we're doing a good job! Document Conventions. Using Arrays to Create Maps.This is very clear and concise.
Have you tried such code in a context of a query for a web page backend? I am curious what performance of something like this look like and if it is only functional for ETL. Heyy thanks for the question! First comment on the blog. Even with simple data sets and queries total response times easily get up to seconds. If that's ok though?
How real time would it need to be? If possible, I would consider caching the result CSV in S3 and only go back to Athena when you need to refresh the file. Thanks for the guide! Thanks for the question!
If you have multiple profiles configured in your. But I suppose for simplicity it's best to leave that out or at least clarify it a bit more.
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Popular Posts. Simple way to query Amazon Athena in python with boto3. AWS Glue for dummies. Monday, April 30, Simple way to query Amazon Athena in python with boto3. JayDeBeApi looked like a hassle to set up. Boto3 was something I was already familiar with.
With boto3, you specify the S3 path where you want to store the results, wait for the query execution to finish and fetch the file once it is there.
And clean up afterwards. Once all of this is wrapped in a function, it gets really manageable. If you want to see the code, go ahead and copy-paste this gist: query Athena using boto3. I'll explain the code below.
First let's start with our configurations. Fill these with your own details of course. Ilkka Peltola data analytics professional at Zervanta blogger and an aspiring speaker. Contact: ilkkapel gmail. Unknown March 26, at PM. Ilkka Peltola March 27, at AM.
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