Apache Spark vs Stroom

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Stroom

Stacks1

Followers3

Votes0

GitHub Stars452

Forks62

Apache Spark vs Stroom: What are the differences?

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; Stroom: A scalable data storage, processing and analysis platform. It is a data processing, storage and analysis platform. It is scalable - just add more CPUs / servers for greater throughput. It is suitable for processing high volume data such as system logs, to provide valuable insights into IT performance and usage.

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

Some of the features offered by Apache Spark are:

Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
Write applications quickly in Java, Scala or Python
Combine SQL, streaming, and complex analytics

On the other hand, Stroom provides the following key features:

Receive and store large volumes of data such as native format logs. Ingested data is always available in its raw form
Create sequences of XSL and text operations, in order to normalise or export data in any format. It is possible to enrich data using lookups and reference data
Easily add new data formats and debug the transformations if they don't work as expected

Apache Spark and Stroom are both open source tools. It seems that Apache Spark with 24.8K GitHub stars and 20.9K forks on GitHub has more adoption than Stroom with 294 GitHub stars and 32 GitHub forks.

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Advice on Apache Spark, Stroom

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

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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Comments

Detailed Comparison

Apache Spark	Stroom
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.	It is a data processing, storage and analysis platform. It is scalable - just add more CPUs / servers for greater throughput. It is suitable for processing high volume data such as system logs, to provide valuable insights into IT performance and usage.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	Receive and store large volumes of data such as native format logs. Ingested data is always available in its raw form; Create sequences of XSL and text operations, in order to normalise or export data in any format. It is possible to enrich data using lookups and reference data; Easily add new data formats and debug the transformations if they don't work as expected; Create multiple indexes with different retention periods. These can be sharded across your cluster; Run queries against your indexes or statistics and view the results within custom visualisations; Record counts or values of items over time
Statistics
GitHub Stars 42.2K	GitHub Stars 452
GitHub Forks 28.9K	GitHub Forks 62
Stacks 3.1K	Stacks 1
Followers 3.5K	Followers 3
Votes 140	Votes 0
Pros & Cons
Pros 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 Cons 4 Speed	No community feedback yet
Integrations
No integrations available	NGINX MariaDB MySQL IntelliJ IDEA

What are some alternatives to Apache Spark, Stroom?

Papertrail

Papertrail helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs.

Logmatic

Get a clear overview of what is happening across your distributed environments, and spot the needle in the haystack in no time. Build dynamic analyses and identify improvements for your software, your user experience and your business.

Loggly

It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain.

Logentries

Logentries makes machine-generated log data easily accessible to IT operations, development, and business analysis teams of all sizes. With the broadest platform support and an open API, Logentries brings the value of log-level data to any system, to any team member, and to a community of more than 25,000 worldwide users.

Logstash

Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.

Graylog

Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information.

Presto

Distributed SQL Query Engine for Big Data

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.

Sematext

Sematext pulls together performance monitoring, logs, user experience and synthetic monitoring that tools organizations need to troubleshoot performance issues faster.

Fluentd

Fluentd collects events from various data sources and writes them to files, RDBMS, NoSQL, IaaS, SaaS, Hadoop and so on. Fluentd helps you unify your logging infrastructure.

Related Comparisons

Some of the features offered by Apache Spark are:

Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
Write applications quickly in Java, Scala or Python
Combine SQL, streaming, and complex analytics

On the other hand, Stroom provides the following key features:

Receive and store large volumes of data such as native format logs. Ingested data is always available in its raw form
Create sequences of XSL and text operations, in order to normalise or export data in any format. It is possible to enrich data using lookups and reference data
Easily add new data formats and debug the transformations if they don't work as expected

Apache Spark vs Stroom