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Apache Spark vs Splunk: What are the differences?

Apache Spark vs Splunk

Apache Spark and Splunk are two popular big data processing platforms used for analyzing and processing large volumes of data. While both platforms have similar capabilities in terms of data processing and analytics, there are key differences that set them apart.

  1. Data Processing Model: Apache Spark is a distributed computing platform that operates on an in-memory data processing model. It allows users to process large datasets in parallel across a cluster of machines, resulting in faster and more efficient data processing. On the other hand, Splunk follows a log-based data processing model, which means it ingests data logs generated by various sources and indexes them for search and analysis.

  2. Data Source Compatibility: Apache Spark supports a wide range of data sources, including structured, semi-structured, and unstructured data from various file formats and databases. It can handle data in real-time streaming and batch mode. Splunk, on the other hand, specializes in ingesting and analyzing log data from applications, systems, and network devices. It provides out-of-the-box support for a wide range of log formats and protocols.

  3. Query Language: Apache Spark provides a unified programming model and supports multiple programming languages, including Scala, Java, Python, and R. It also offers a rich set of high-level APIs for data manipulation and analysis. Splunk, on the other hand, uses its proprietary search language called SPL (Splunk Processing Language) for querying and analyzing data. SPL provides powerful search capabilities and allows users to extract valuable insights from log data.

  4. Scalability and Performance: Apache Spark offers excellent scalability and performance by performing data processing operations in parallel across a cluster of machines. It can handle large-scale data processing tasks efficiently and provides fault tolerance mechanisms for handling failures. Splunk, on the other hand, is designed to handle high volumes of log data and provides scalability through distributed indexing and search capabilities. It is optimized for analyzing log data in real-time.

  5. Data Visualization and Reporting: Apache Spark provides various libraries and tools for data visualization, including integration with popular visualization libraries like Matplotlib and D3.js. It also supports interactive data exploration and provides visualization capabilities within its notebooks. Splunk, on the other hand, offers powerful data visualization and reporting features out-of-the-box. It provides customizable dashboards, charts, and graphs to visualize and analyze log data effectively.

  6. Deployment and Management: Apache Spark can be deployed in various environments, including on-premises data centers and cloud platforms. It provides a flexible cluster manager that allows users to deploy and manage Spark clusters efficiently. Splunk, on the other hand, provides a centralized management platform for deploying and configuring Splunk instances across an organization. It offers granular control over user access and permissions and provides extensive monitoring and reporting capabilities.

In summary, Apache Spark and Splunk differ in their data processing models, data source compatibility, query languages, scalability and performance characteristics, data visualization and reporting capabilities, and deployment and management options.

Advice on Apache Spark and Splunk
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 531K 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)
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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 · 373.1K views
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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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Pros of Apache Spark
Pros of Splunk
  • 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
  • 3
    API for searching logs, running reports
  • 3
    Alert system based on custom query results
  • 2
    Dashboarding on any log contents
  • 2
    Custom log parsing as well as automatic parsing
  • 2
    Ability to style search results into reports
  • 2
    Query engine supports joining, aggregation, stats, etc
  • 2
    Splunk language supports string, date manip, math, etc
  • 2
    Rich GUI for searching live logs
  • 1
    Query any log as key-value pairs
  • 1
    Granular scheduling and time window support

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Cons of Apache Spark
Cons of Splunk
  • 4
    Speed
  • 1
    Splunk query language rich so lots to learn

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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.

What is Splunk?

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

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What companies use Apache Spark?
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What are some alternatives to Apache Spark and Splunk?
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.
Cassandra
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
Apache Beam
It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
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.
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.
See all alternatives