Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Presto is an open source tool with 9. Here's a link to Presto's open source repository on GitHub. To cut down on costs, we started deleting older, obsolete data to free up space for new data. To overcome these challenges, Uber rebuilt their big data platform around Hadoop.
To improve platform scalability and efficiency, Uber transitioned from JSON to Parquet, and built a central schema service to manage schemas and integrate different client libraries. Job queue and log data is sent to Kafka then persisted to S3 using an open source tool called Secor, which was created by Pinterest. Presto is then used for ad-hoc questions, validating data assumptions, exploring smaller datasets, and creating visualizations for some internal tools. Hive is used for larger data sets or longer time series data, and Spark allows teams to write efficient and robust batch and aggregation jobs.
Most of the Spark pipeline is written in Scala. Earlier this year, he commented on a Quora question summarizing their current stack. We store data in an Amazon S3 based data warehouse. Because our storage layer s3 is decoupled from our processing layer, we are able to scale our compute environment very elastically.Presto: Fast SQL on Everything (Facebook)
We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.
At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally.
Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 e. This provides our data scientist a one-click method of getting from their algorithms to production.
DataScience DataStack Data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data. We have hundreds of petabytes of data and tens of thousands of Apache Hive tables.
Our Presto clusters are comprised of a fleet of r4. Presto clusters together have over TBs of memory and 14K vcpu cores. Each query submitted to Presto cluster is logged to a Kafka topic via Singer.Brd farm rogue
Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.
Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly.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.Mammut ropes canada
With Athena, there are no versions to manage. We have transparently upgraded the underlying engine in Athena to a version based on Presto version 0. No action is required on your end. With the upgrade, you can now use Presto 0. Major updates for this release, including the community-contributed fixes, include:. Support for ignoring headers.
You can use the skip.
Thanks for letting us know we're doing a good job! January 19, Support for correlated subqueries. Support for Presto Lambda expressions and functions.
Document Conventions. February 2, November 13, Send us feedback. This feature is in Public Preview in Databricks Runtime 5. Presto and Athena support reading from external tables using a manifest filewhich is a text file containing the list of data files to read for querying a table.Coinpot token hack
When an external table is defined in the Hive metastore using manifest files, Presto and Athena can use the list of files in the manifest rather than finding the files by directory listing. This article describes how to set up a Presto and Athena to Delta Lake integration using manifest files and query Delta tables.
Using a cluster running Databricks Runtime 5. In other words, the files in this directory will contain the names of the data files that is, Parquet files that should be read for reading a snapshot of the Delta table. The SymlinkTextInputFormat configures Presto or Athena to compute file splits for mytable by reading the manifest file instead of using a directory listing to find data files.
The tool you use to run the command depends on whether Databricks and Presto or Athena use the same Hive metastore. This is needed because the manifest of a partitioned table is itself partitioned in the same directory structure as the table.
Run this command using the same tool used to create the table. Furthermore, you should run this command:. For Presto running in EMR, you may need additional configuration changes. To fix this issue, you must configure Presto to use its own default file systems instead of EMRFS using the following steps:.Dizziness after hair dye
Change the key hive. When the data in a Delta table is updated you must regenerate the manifests using either of the following approaches:. Update automatically : You can configure a Delta table so that all write operations on the table automatically update the manifests. To enable this automatic mode, set the corresponding table property using the following SQL command. To disable this automatic mode, set this property to false. After enabling automatic mode on a partitioned table, each write operation updates only manifests corresponding to the partitions that operation wrote to.
This incremental update ensures that the overhead of manifest generation is low for write operations. However, this also means that if the manifests in other partitions are stale, enabling automatic mode will not automatically fix it. Whether to update explicitly or automatically depends on the concurrent nature of write operations on the Delta table and the desired data consistency. For example, if automatic mode is enabled, then concurrent write operations leads to concurrent overwrites to the manifest files.
With such unordered writes, the manifest files are not guaranteed to point to the latest version of the table after the write operations complete. Hence, if concurrent writes are expected and you want to avoid stale manifests, you may consider explicitly updating the manifest after the expected write operations have completed.
A common setup with Databricks and Presto or Athena is to have both of them configured to use the same Hive metastore. Here is the recommended workflow for creating Delta tables, writing to them from Databricks, and querying them from Presto or Athena in such a configuration. Generate the manifests using the Delta.
Note the manifest location.
Create another table only for Presto or Athena using the manifest location. Delta Lake maintains files corresponding to multiple versions of the table, and querying all the files crawled by Glue will generate incorrect results. Whenever Delta Lake generates updated manifests, it atomically overwrites existing manifest files. Therefore, Presto and Athena will always see a consistent view of the data files; it will see all of the old version files or all of the new version files. However, the granularity of the consistency guarantees depends on whether the table is partitioned or not.
Very large numbers of files can hurt the performance of Presto and Athena.Athena Charanne R. Presto WAG: 1. As a namesake of the Greek Goddess of Wisdom, Athena believes that wisdom is complemented by experience. She does not limit herself to learning by book alone, but also by highs and lows of the daily choices she randomly makes. Sometimes, she makes the right choices. Other times, she does not.
However, Athena always makes it a point to choose happiness no matter what happens and stay positive that with every mistake, comes a lesson.
She is also a team player and is a member of various UPD organizations. She has been an officer in four organizations and a president of one. Much like theory and practice, she believes that her membership in different types of groups opened many opportunities to share her acquired-knowledge for the benefit of the people.
In turn, the skills she obtained from these organizations were of big help in her academic life, most especially when it comes to group works.
Lastly, Athena is someone who puts premium to family and friends. Growing up in a close-knit family, she learned that surrounding herself with the people she loves is a big factor for her success. Her friends served as her family in the University.Why not just use AWS Athena instead of going through the trouble of deploying your own cluster?
I probably should have addressed this in the original blog post, but since I didn't - let's do the complete reasoning for when you should and shouldn't consider your own Presto cluster. If your organization has a lot of data but only few queries per day, then Athena is definitely the economic choice. However, if your company is data-driven and has a team of analysis and BI users then it's a completely different story.
Their dashboards and queries will be in the dozens if not hundreds or thousands per day, and possibly scanning many TBs of data each. By deploying Presto yourself you can drastically lower the cost-per-query. You pay a constant fee for the compute instances you are running EC2 instances cost.
The more machines you run and the bigger they are - the higher the fee, yes. But Presto is very efficient and if your data is correcly stored, a few commodity machines will do a great job.
If you are running your Presto cluster on the same region as your S3 bucket, and within one AZ, then there is no network or data transfer costs at all. S3 reads will be billed by API calls, but those are priced a fraction of a cent per 10k calls. Presto might leverage S3 Select in queries, which costs a bit more but will boost queries performance and reduce the compute units required.
The compute costs can be further optimized by using spot instances for worker nodes, and completely shutting them down off-hours where applicable. Presto can deal with a lost worker node - which might slow down some queries but spot instances come at a great discount.
Cost per query can be order of magnitude cheaper, and even several orders of magnitue cheaper, with your own Presto cluster. This will be mostly true if you run queries extensively and each query scans significant amounts of data. Obviously, this doesn't account S3 Storage costs - but those would be the same also for the data Athena queries.
AWS Athena vs your own Presto cluster on AWS
Either way, make sure you store your data with proper partitions, and use a columnar file format like Parquet or ORC. Usually the argument pro-SaaS solutions is DevOps costs. That is a true argument, but as always - it depends how much the SaaS will really cost compared to the DevOps costs you will evantually pay. With out presto-cloud-deploy Terraform solution, it's really easy to set-up your Presto cluster on AWS.
After the initial setup and tuning, which could take several days at most, you are done. Except from occasional massages to the cluster, you will hardly even remember you deployed a cluster of machines. The Presto web UI is a great query monitoring tool, showing you all executed and failed queries, along with performance statistics which let you fine-tune your cluster for faster and cheaper queries.
Athena doesn't give you anything even remotely close to that. Presto has an impressive set of Connectors out of the box, with some connectors you can find on the net and plug-in to your Presto deployment. If you want to execute queries against those stores is when you want your own Presto cluster. Presto also lets you use multiple data sources in one query e.Presto and Athena support reading from external tables using a manifest filewhich is a text file containing the list of data files to read for querying a table.
When an external table is defined in the Hive metastore using manifest files, Presto and Athena can use the list of files in the manifest rather than finding the files by directory listing.
This article describes how to set up a Presto and Athena to Delta Lake integration using manifest files and query Delta tables.
In other words, the files in this directory will contain the names of the data files that is, Parquet files that should be read for reading a snapshot of the Delta table. We recommend that you define the Delta table in a location that Presto or Athena read directly.
January 19, 2018
The SymlinkTextInputFormat configures Presto or Athena to compute file splits for mytable by reading the manifest file instead of using a directory listing to find data files. The tool you use to run the command depends on whether Apache Spark and Presto or Athena use the same Hive metastore. This is needed because the manifest of a partitioned table is itself partitioned in the same directory structure as the table. Run this command using the same tool used to create the table.
Furthermore, you should run this command:. Whenever Delta Lake generates updated manifests, it atomically overwrites existing manifest files. Therefore, Presto and Athena will always see a consistent view of the data files; it will see all of the old version files or all of the new version files.
However, the granularity of the consistency guarantees depends on whether the table is partitioned or not. Depending on what storage system you are using for Delta tables, it is possible to get incorrect results when Presto or Athena concurrently queries the manifest while the manifest files are being rewritten.
In file system implementations that lack atomic file overwrites, a manifest file may be momentarily unavailable.Flyvpn register
Hence, use manifests with caution if their updates are likely to coincide with queries from Presto or Athena. Very large numbers of files can hurt the performance of Presto and Athena. Hence we recommend that you compact the files of the table before generating the manifests. We suggest that the number of files should not exceed for the entire unpartitioned table or for each partition in a partitioned table.
Delta Lake supports schema evolution and queries on a Delta table automatically use the latest schema regardless of the schema defined in the table in the Hive metastore. However, Presto or Athena uses the schema defined in the Hive metastore and will not query with the updated schema until the table used by Presto or Athena is redefined to have the updated schema.
Updated Jan 14, Contribute. Presto and Athena to Delta Lake Integration Presto and Athena support reading from external tables using a manifest filewhich is a text file containing the list of data files to read for querying a table. Note We recommend that you define the Delta table in a location that Presto or Athena read directly. Important This table definition cannot be used in a query in Apache Spark.
It can be used only by Presto and Athena. See later sections to find out how to define tables for Apache Spark and Presto or Athena to interoperate in an integrated environment. Step 3: Update manifests When the data in a Delta table is updated, you must regenerate the manifests.
Limitations The Presto and Athena integration has known limitations in its behavior. Data consistency Whenever Delta Lake generates updated manifests, it atomically overwrites existing manifest files.
Unpartitioned tables : All the files names are written in one manifest file which is updated atomically. In this case Presto and Athena will see full table snapshot consistency. Partitioned tables : A manifest file is partitioned in the same Hive-partitioning-style directory structure as the original Delta table.
This means that each partition is updated atomically, and Presto or Athena will see a consistent view of each partition but not a consistent view across partitions. Furthermore, since all manifests of all partitions cannot be updated together, concurrent attempts to generate manifests can lead to different partitions having manifests of different versions.
Performance Very large numbers of files can hurt the performance of Presto and Athena. Schema evolution Delta Lake supports schema evolution and queries on a Delta table automatically use the latest schema regardless of the schema defined in the table in the Hive metastore.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. The Athena query engine is based on Presto 0. For more information about these functions, see Presto 0. Athena does not support all of Presto's features. For information, see Considerations and Limitations. Logical Operators. Comparison Functions and Operators. Conditional Expressions. Conversion Functions. Mathematical Functions and Operators.
Bitwise Functions. Decimal Functions and Operators. String Functions and Operators. Binary Functions. Date and Time Functions and Operators. Regular Expression Functions. URL Functions. Aggregate Functions. Window Functions. Color Functions. Array Functions and Operators.
Map Functions and Operators.
Presto, Athena Charanne R.
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