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

PySpark

192
222
+ 1
0
Apache Spark

2.6K
3K
+ 1
137
Add tool

PySpark vs Apache Spark: What are the differences?

What is PySpark? The Python API for Spark. It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.

What is 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.

PySpark can be classified as a tool in the "Data Science Tools" category, while Apache Spark is grouped under "Big Data Tools".

Apache Spark is an open source tool with 22.9K GitHub stars and 19.7K GitHub forks. Here's a link to Apache Spark's open source repository on GitHub.

Uber Technologies, Slack, and Shopify are some of the popular companies that use Apache Spark, whereas PySpark is used by Repro, Autolist, and Shuttl. Apache Spark has a broader approval, being mentioned in 356 company stacks & 564 developers stacks; compared to PySpark, which is listed in 8 company stacks and 6 developer stacks.

Advice on PySpark and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 299.3K 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
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 · 190.2K views
Recommends
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
Get Advice from developers at your company using Private StackShare. Sign up for Private StackShare.
Learn More
Pros of PySpark
Pros of Apache Spark
    Be the first to leave a pro
    • 59
      Open-source
    • 48
      Fast and Flexible
    • 8
      One platform for every big data problem
    • 7
      Great for distributed SQL like applications
    • 6
      Easy to install and to use
    • 3
      Works well for most Datascience usecases
    • 2
      Interactive Query
    • 2
      In memory Computation
    • 2
      Machine learning libratimery, Streaming in real

    Sign up to add or upvote prosMake informed product decisions

    Cons of PySpark
    Cons of Apache Spark
      Be the first to leave a con
      • 3
        Speed

      Sign up to add or upvote consMake informed product decisions

      - No public GitHub repository available -

      What is PySpark?

      It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame 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.

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

      Jobs that mention PySpark and Apache Spark as a desired skillset
      What companies use PySpark?
      What companies use Apache Spark?
      See which teams inside your own company are using PySpark or Apache Spark.
      Sign up for Private StackShareLearn More

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

      What tools integrate with PySpark?
      What tools integrate with Apache Spark?

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

      Blog Posts

      What are some alternatives to PySpark and Apache Spark?
      Scala
      Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.
      Python
      Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.
      Pandas
      Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
      Hadoop
      The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
      PyTorch
      PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
      See all alternatives