Presto vs Apache Spark

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Presto

392
1K
+ 1
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Apache Spark

2.9K
3.5K
+ 1
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Apache Spark vs Impala vs Presto: What are the differences?

  1. Data Processing Architecture: Apache Spark is a unified analytics engine that supports batch processing, real-time streaming, machine learning, and graph processing, while Impala and Presto are primarily designed for interactive SQL querying with support for some analytical functions. Impala is integrated with Hadoop components like HDFS and HBase, while Presto is independent of Hadoop and directly connects to data sources.

  2. Execution Engine: Apache Spark utilizes in-memory processing for faster computations by storing intermediate data in memory, making it suitable for iterative algorithms and interactive data analysis. On the other hand, Impala and Presto rely on a pull-based execution model where data is fetched from the storage layer as needed, offering better efficiency for ad-hoc queries and low-latency response times.

  3. Distributed Computing: Apache Spark relies on a master-slave architecture with a central driver node distributing tasks to worker nodes, whereas Impala and Presto follow a shared-nothing architecture where each node processes its data independently and communicates with other nodes when needed, allowing for better scalability and fault tolerance.

  4. Language Support: Apache Spark provides APIs in Java, Scala, Python, and R, enabling developers to write applications in their preferred language, whereas Impala and Presto primarily support SQL for querying data with limited scripting capabilities, making them more suitable for data analysts and SQL developers.

  5. Storage Integration: Apache Spark can read and write data from various storage systems like HDFS, S3, Kafka, and JDBC sources, offering flexibility in data sources, whereas Impala is tightly integrated with Hadoop ecosystem tools like HDFS and HBase, and Presto has connectors for various data sources but can operate independently without relying on specific file systems.

  6. Use Case: Apache Spark is well-suited for complex data processing tasks, machine learning, and real-time analytics applications, while Impala and Presto are ideal for ad-hoc querying, interactive analysis, and business intelligence use cases where low-latency responses and SQL compatibility are crucial.

In Summary, Apache Spark excels in diverse data processing workloads, while Impala and Presto focus on interactive and ad-hoc SQL querying with different architectural paradigms and use cases.

Advice on Presto and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 528.4K views

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Replies (2)
Recommends
on
ElasticsearchElasticsearch

The first solution that came to me is to use upsert to update ElasticSearch:

  1. Use the primary-key as ES document id
  2. Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.

Cons: The load on ES will be higher, due to upsert.

To use Flink:

  1. Create a KeyedDataStream by the primary-key
  2. In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
  3. When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
  4. When the Timer fires, read the 1st record from the State and send out as the output record.
  5. Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State

Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.

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Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 370.8K views
Recommends
on
Apache SparkApache Spark

Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"

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Decisions about Presto and Apache Spark
Ashish Singh
Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

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

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Karthik Raveendran
CPO at Attinad Software · | 3 upvotes · 211.2K views

The platform deals with time series data from sensors aggregated against things( event data that originates at periodic intervals). We use Cassandra as our distributed database to store time series data. Aggregated data insights from Cassandra is delivered as web API for consumption from other applications. Presto as a distributed sql querying engine, can provide a faster execution time provided the queries are tuned for proper distribution across the cluster. Another objective that we had was to combine Cassandra table data with other business data from RDBMS or other big data systems where presto through its connector architecture would have opened up a whole lot of options for us.

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Pros of Presto
Pros of Apache Spark
  • 18
    Works directly on files in s3 (no ETL)
  • 13
    Open-source
  • 12
    Join multiple databases
  • 10
    Scalable
  • 7
    Gets ready in minutes
  • 6
    MPP
  • 61
    Open-source
  • 48
    Fast and Flexible
  • 8
    One platform for every big data problem
  • 8
    Great for distributed SQL like applications
  • 6
    Easy to install and to use
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation
Cons of Presto
Cons of Apache Spark
    Be the first to leave a con
    • 4
      Speed
    - No public GitHub repository available -

    What is Presto?

    Distributed SQL Query Engine for Big Data

    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 companies use Apache Spark?

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    What tools integrate with Presto?
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    Blog Posts

    Mar 24 2021 at 12:57PM

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    MySQLKafkaApache Spark+6
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    Aug 28 2019 at 3:10AM

    Segment

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    What are some alternatives to Presto and Apache Spark?
    Apache Drill
    Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel.
    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.
    HBase
    Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
    JavaScript
    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.
    Git
    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.