Apache Spark vs Sparrow

Overview

Sparrow

Stacks6

Followers11

Votes0

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Spark vs Sparrow: What are the differences?

Data Processing: Apache Spark is designed for big data processing and analytics, utilizing in-memory computing to speed up data processing tasks significantly. On the other hand, Sparrow is a lightweight, low-latency scheduling system designed for batch and machine learning workloads, focusing on minimizing job completion times for short tasks.
Fault Tolerance: Spark provides fault tolerance through resilient distributed datasets (RDDs) and lineage information, ensuring reliable data processing even in the presence of failures. Sparrow, on the other hand, does not focus on fault tolerance as heavily, prioritizing low latency and high throughput over fault tolerance mechanisms.
Programming Languages: Spark supports multiple programming languages such as Java, Scala, Python, and R, providing a wide range of options for developers. In contrast, Sparrow is primarily designed to work with Java-based applications, limiting the language flexibility compared to Spark.
Scalability: Spark is known for its scalability, able to handle large volumes of data and scale horizontally to accommodate increasing workloads. Sparrow, although efficient for short tasks, may face scalability challenges when dealing with massive amounts of data or high levels of concurrency.
Use Cases: Apache Spark is commonly used for data processing, analytics, machine learning, and real-time stream processing applications. Sparrow, on the other hand, is best suited for low-latency batch jobs and machine learning workloads that require quick job completion times.
Community and Ecosystem: Apache Spark has a large and active community, extensive documentation, and a rich ecosystem of libraries and tools, making it a popular choice for big data processing projects. In contrast, Sparrow, being a more specialized system, may have a smaller community and fewer available resources in comparison.

In Summary, Apache Spark and Sparrow differ in terms of data processing capabilities, fault tolerance mechanisms, programming languages support, scalability, use cases, and community ecosystems.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Sparrow, Apache Spark

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.

576k views576k

Comments

Detailed Comparison

Sparrow	Apache Spark
Sparrow keeps messages in memory, but persists them to disk, using Sqlite, when the queue is shutdown.	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.
-	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
Statistics
GitHub Stars -	GitHub Stars 42.2K
GitHub Forks -	GitHub Forks 28.9K
Stacks 6	Stacks 3.1K
Followers 11	Followers 3.5K
Votes 0	Votes 140
Pros & Cons
No community feedback yet	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

What are some alternatives to Sparrow, Apache Spark?

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

ActiveMQ

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Presto

Distributed SQL Query Engine for Big Data

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

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.

Related Comparisons

Apache Spark vs Sparrow: What are the differences?

Data Processing: Apache Spark is designed for big data processing and analytics, utilizing in-memory computing to speed up data processing tasks significantly. On the other hand, Sparrow is a lightweight, low-latency scheduling system designed for batch and machine learning workloads, focusing on minimizing job completion times for short tasks.
Fault Tolerance: Spark provides fault tolerance through resilient distributed datasets (RDDs) and lineage information, ensuring reliable data processing even in the presence of failures. Sparrow, on the other hand, does not focus on fault tolerance as heavily, prioritizing low latency and high throughput over fault tolerance mechanisms.
Programming Languages: Spark supports multiple programming languages such as Java, Scala, Python, and R, providing a wide range of options for developers. In contrast, Sparrow is primarily designed to work with Java-based applications, limiting the language flexibility compared to Spark.
Scalability: Spark is known for its scalability, able to handle large volumes of data and scale horizontally to accommodate increasing workloads. Sparrow, although efficient for short tasks, may face scalability challenges when dealing with massive amounts of data or high levels of concurrency.
Use Cases: Apache Spark is commonly used for data processing, analytics, machine learning, and real-time stream processing applications. Sparrow, on the other hand, is best suited for low-latency batch jobs and machine learning workloads that require quick job completion times.
Community and Ecosystem: Apache Spark has a large and active community, extensive documentation, and a rich ecosystem of libraries and tools, making it a popular choice for big data processing projects. In contrast, Sparrow, being a more specialized system, may have a smaller community and fewer available resources in comparison.

In Summary, Apache Spark and Sparrow differ in terms of data processing capabilities, fault tolerance mechanisms, programming languages support, scalability, use cases, and community ecosystems.

Apache Spark vs Sparrow

Overview