Need advice about which tool to choose?Ask the StackShare community!

Apache Flink

519
860
+ 1
38
Presto

394
1K
+ 1
66
Add tool

Apache Flink vs Presto: What are the differences?

Introduction

Apache Flink and Presto are both powerful data processing engines that are widely used in the industry. While they share some similarities, there are several key differences between the two.

  1. Architecture: Apache Flink is designed as a stream processing engine that also supports batch processing, while Presto is primarily a distributed SQL query engine. Flink uses a master/worker architecture, where the master node coordinates the execution of the tasks, while Presto follows a coordinator/worker architecture, where the coordinator node is responsible for query planning and coordination.

  2. Data Processing Model: Flink operates on stream data and has built-in support for event time and out-of-order processing. It provides stateful processing capabilities, fault tolerance, and exactly-once processing guarantees. On the other hand, Presto is optimized for in-memory batch processing and interactive queries over large datasets, without focusing on stream processing capabilities.

  3. Integration with Ecosystem: Flink extensively integrates with various big data frameworks and storage systems, such as Apache Kafka, Apache Hadoop, and Apache Cassandra. It can also be used with Apache Beam for unified batch and stream processing. Presto, on the other hand, has rich connectors for data sources like Hive, HDFS, MySQL, and other common databases.

  4. Language Support: Flink provides support for both Java and Scala programming languages for writing data processing logic. It also offers a SQL-like API called Table API and a higher-level SQL-oriented API called SQL queries. In contrast, Presto primarily uses SQL for querying data, and it does not provide native support for programming languages like Java or Scala.

  5. Performance: Flink is known for its low latency and high throughput processing. It achieves this by leveraging features like pipelined execution, caching, and efficient memory management. On the other hand, Presto focuses on scalability and supports parallel execution of queries against multiple distributed data sources.

  6. Community and Adoption: Flink has a vibrant and growing community and is widely adopted in the big data ecosystem. It is backed by the Apache Software Foundation, and there are many companies using it in production for real-time data processing. Presto also has a strong community and is used by companies like Facebook, Airbnb, and Netflix for interactive analytics.

In summary, Apache Flink and Presto differ in their architecture, data processing model, integration with the ecosystem, language support, performance characteristics, and community adoption. While both are powerful tools for data processing, the choice between them depends on the specific use case and requirements of the project.

Advice on Apache Flink and Presto
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 516.5K 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.

See more
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.

See more
Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 361.2K 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"

See more
Decisions about Apache Flink and Presto
Ashish Singh
Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 2.8M 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

See more
Karthik Raveendran
CPO at Attinad Software · | 3 upvotes · 207.6K 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.

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Apache Flink
Pros of Presto
  • 16
    Unified batch and stream processing
  • 8
    Easy to use streaming apis
  • 8
    Out-of-the box connector to kinesis,s3,hdfs
  • 4
    Open Source
  • 2
    Low latency
  • 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

Sign up to add or upvote prosMake informed product decisions

- No public GitHub repository available -

What is 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.

What is Presto?

Distributed SQL Query Engine for Big Data

Need advice about which tool to choose?Ask the StackShare community!

What companies use Apache Flink?
What companies use Presto?
See which teams inside your own company are using Apache Flink or Presto.
Sign up for StackShare EnterpriseLearn More

Sign up to get full access to all the companiesMake informed product decisions

What tools integrate with Apache Flink?
What tools integrate with Presto?

Sign up to get full access to all the tool integrationsMake informed product decisions

What are some alternatives to Apache Flink and Presto?
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.
Apache Storm
Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.
Akutan
A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world.
Apache Flume
It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.
Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
See all alternatives