Why you should use Presto for ad hoc analytics

Nancy J. Delong

Presto! It is not only an incantation to excite your viewers right after a magic trick, but also a title remaining applied extra and extra when talking about how to churn by massive details. Even though there are several deployments of Presto in the wild, the technologies — a distributed […]

Presto! It is not only an incantation to excite your viewers right after a magic trick, but also a title remaining applied extra and extra when talking about how to churn by massive details. Even though there are several deployments of Presto in the wild, the technologies — a distributed SQL question motor that supports all types of details sources — remains unfamiliar to several developers and details analysts who could profit from making use of it.

In this article, I’ll be talking about Presto: what it is, in which it arrived from, how it is distinctive from other details warehousing solutions, and why you really should look at it for your massive details solutions.

Presto vs. Hive

Presto originated at Facebook again in 2012. Open up-sourced in 2013 and managed by the Presto Foundation (section of the Linux Foundation), Presto has professional a regular increase in reputation over the yrs. These days, several corporations have created a business enterprise product all-around Presto, this kind of as Ahana, with PrestoDB-centered ad hoc analytics offerings.

Presto was created as a means to supply conclude-end users access to tremendous details sets to perform ad hoc assessment. Just before Presto, Facebook would use Hive (also created by Facebook and then donated to the Apache Program Foundation) in get to perform this variety of assessment. As Facebook’s details sets grew, Hive was identified to be insufficiently interactive (read through: way too sluggish). This was mostly mainly because the basis of Hive is MapReduce, which, at the time, expected intermediate details sets to be persisted to HDFS. That meant a good deal of I/O to disk for details that was eventually thrown away. 

Presto takes a distinctive tactic to executing all those queries to help save time. Rather of keeping intermediate details on HDFS, Presto will allow you to pull the details into memory and perform functions on the details there rather of persisting all of the intermediate details sets to disk. If that sounds acquainted, you may perhaps have read of Apache Spark (or any quantity of other systems out there) that have the identical fundamental concept to correctly exchange MapReduce-centered systems. Applying Presto, I’ll keep the details in which it life (in Hadoop or, as we’ll see, wherever) and perform the executions in-memory throughout our distributed process, shuffling details between servers as required. I stay clear of touching any disk, eventually rushing up question execution time.

How Presto operates

Diverse from a classic details warehouse, Presto is referred to as a SQL question execution motor. Knowledge warehouses control how details is penned, in which that details resides, and how it is read through. As soon as you get details into your warehouse, it can verify tricky to get it again out. Presto takes an additional tactic by decoupling details storage from processing, when furnishing aid for the identical ANSI SQL question language you are applied to.

At its core, Presto executes queries over details sets that are delivered by plug-ins, specially Connectors. A Connector provides a means for Presto to read through (and even publish) details to an external details process. The Hive Connector is just one of the typical connectors, making use of the identical metadata you would use to interact with HDFS or Amazon S3. For the reason that of this connectivity, Presto is a fall-in substitution for corporations making use of Hive currently. It is capable to read through details from the identical schemas and tables making use of the identical details formats — ORC, Avro, Parquet, JSON, and extra. In addition to the Hive connector, you will discover connectors for Cassandra, Elasticsearch, Kafka, MySQL, MongoDB, PostgreSQL, and several many others. Connectors are remaining contributed to Presto all the time, giving Presto the prospective to be capable to access details wherever it life.

The benefit of this decoupled storage product is that Presto is capable to supply a one federated see of all of your details — no issue in which it resides. This ramps up the capabilities of ad hoc querying to levels it has never attained just before, when also furnishing interactive question occasions over your significant details sets (as very long as you have the infrastructure to again it up, on-premises or cloud).

Copyright © 2020 IDG Communications, Inc.

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