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Presto vs Apache Spark: What are the differences?

Presto: Distributed SQL Query Engine for Big Data. 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; Apache Spark: Fast and general engine for large-scale data processing. Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto and Apache Spark can be primarily classified as "Big Data" tools.

"Works directly on files in s3 (no ETL)" is the top reason why over 9 developers like Presto, while over 45 developers mention "Open-source" as the leading cause for choosing Apache Spark.

Presto and Apache Spark are both open source tools. It seems that Apache Spark with 22.3K GitHub stars and 19.3K forks on GitHub has more adoption than Presto with 9.22K GitHub stars and 3.12K GitHub forks.

Slack, Shopify, and SendGrid are some of the popular companies that use Apache Spark, whereas Presto is used by Repro, Airbnb, and Netflix. Apache Spark has a broader approval, being mentioned in 263 company stacks & 111 developers stacks; compared to Presto, which is listed in 19 company stacks and 11 developer stacks.

What is Presto?

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.

What is Apache Spark?

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
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    What are some alternatives to Presto and Apache Spark?
    Stan
    A state-of-the-art platform for statistical modeling and high-performance statistical computation. Used for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business.
    Amazon Athena
    Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
    Apache Flink
    Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
    Apache Hive
    Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.
    Druid
    Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
    See all alternatives
    Decisions about Presto and Apache Spark
    StackShare Editors
    StackShare Editors
    Hadoop
    Hadoop
    Apache Spark
    Apache Spark
    Presto
    Presto

    Around 2015, the growing use of Uber’s data exposed limitations in the ETL and Vertica-centric setup, not to mention the increasing costs. “As our company grew, scaling our data warehouse became increasingly expensive. 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. “More specifically, we introduced a Hadoop data lake where all raw data was ingested from different online data stores only once and with no transformation during ingestion.”

    “In order for users to access data in Hadoop, we introduced Presto to enable interactive ad hoc user queries, Apache Spark to facilitate programmatic access to raw data (in both SQL and non-SQL formats), and Apache Hive to serve as the workhorse for extremely large queries.

    See more
    StackShare Editors
    StackShare Editors
    Hadoop
    Hadoop
    Apache Spark
    Apache Spark
    Presto
    Presto

    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.

    While the first generation big data platform was vulnerable to upstream data format changes, “ad hoc data ingestions jobs were replaced with a standard platform to transfer all source data in its original, nested format into the Hadoop data lake.”

    These platform changes enabled the scaling challenges Uber was facing around that time: “On a daily basis, there were tens of terabytes of new data added to our data lake, and our Big Data platform grew to over 10,000 vcores with over 100,000 running batch jobs on any given day.”

    See more
    StackShare Editors
    StackShare Editors
    Kafka
    Kafka
    MySQL
    MySQL
    Scala
    Scala
    Apache Spark
    Apache Spark
    Presto
    Presto

    Slack’s data team works to “provide an ecosystem to help people in the company quickly and easily answer questions about usage, so they can make better and data informed decisions.” To achieve that goal, that rely on a complex data pipeline.

    An in-house tool call Sqooper scrapes MySQL backups and pipe them to S3. 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.

    For compute, Amazon’s Elastic MapReduce (EMR) creates clusters preconfigured for Presto, Hive, and Spark.

    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.

    Thrift binds all of these engines together with a typed schema and structured data.

    Finally, the Hive Metastore serves as the ground truth for all data and its schema.

    See more
    StackShare Editors
    StackShare Editors
    Prometheus
    Prometheus
    Chef
    Chef
    Consul
    Consul
    Memcached
    Memcached
    Hack
    Hack
    Swift
    Swift
    Hadoop
    Hadoop
    Terraform
    Terraform
    Airflow
    Airflow
    Apache Spark
    Apache Spark
    Kubernetes
    Kubernetes
    gRPC
    gRPC
    HHVM (HipHop Virtual Machine)
    HHVM (HipHop Virtual Machine)
    Presto
    Presto
    Kotlin
    Kotlin
    Apache Thrift
    Apache Thrift

    Since the beginning, Cal Henderson has been the CTO of Slack. Earlier this year, he commented on a Quora question summarizing their current stack.

    Apps
    • Web: a mix of JavaScript/ES6 and React.
    • Desktop: And Electron to ship it as a desktop application.
    • Android: a mix of Java and Kotlin.
    • iOS: written in a mix of Objective C and Swift.
    Backend
    • The core application and the API written in PHP/Hack that runs on HHVM.
    • The data is stored in MySQL using Vitess.
    • Caching is done using Memcached and MCRouter.
    • The search service takes help from SolrCloud, with various Java services.
    • The messaging system uses WebSockets with many services in Java and Go.
    • Load balancing is done using HAproxy with Consul for configuration.
    • Most services talk to each other over gRPC,
    • Some Thrift and JSON-over-HTTP
    • Voice and video calling service was built in Elixir.
    Data warehouse
    • Built using open source tools including Presto, Spark, Airflow, Hadoop and Kafka.
    Etc
    See more
    Eric Colson
    Eric Colson
    Chief Algorithms Officer at Stitch Fix · | 19 upvotes · 356K views
    atStitch FixStitch Fix
    Kafka
    Kafka
    PostgreSQL
    PostgreSQL
    Amazon S3
    Amazon S3
    Apache Spark
    Apache Spark
    Presto
    Presto
    Python
    Python
    R
    R
    PyTorch
    PyTorch
    Docker
    Docker
    Amazon EC2 Container Service
    Amazon EC2 Container Service
    #AWS
    #Etl
    #ML
    #DataScience
    #DataStack
    #Data

    The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. 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.

    Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

    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.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

    For more info:

    #DataScience #DataStack #Data

    See more
    Ashish Singh
    Ashish Singh
    Tech Lead, Big Data Platform at Pinterest · | 20 upvotes · 36.6K views
    Apache Hive
    Apache Hive
    Kubernetes
    Kubernetes
    Kafka
    Kafka
    Amazon S3
    Amazon S3
    Amazon EC2
    Amazon EC2
    Presto
    Presto
    #DataScience
    #DataEngineering
    #AWS
    #BigData

    To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

    Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our 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 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

    Each query submitted to Presto cluster is logged to a Kafka topic via Singer. 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.

    Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

    #BigData #AWS #DataScience #DataEngineering

    See more
    Interest over time
    Reviews of Presto and Apache Spark
    No reviews found
    How developers use Presto and Apache Spark
    Avatar of Wei Chen
    Wei Chen uses Apache SparkApache Spark

    Spark is good at parallel data processing management. We wrote a neat program to handle the TBs data we get everyday.

    Avatar of Ralic Lo
    Ralic Lo uses Apache SparkApache Spark

    Used Spark Dataframe API on Spark-R for big data analysis.