Apache Spark vs VoltDB

Overview

VoltDB

Stacks18

Followers72

Votes18

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Spark vs VoltDB: What are the differences?

Apache Spark and VoltDB are two popular software solutions used in data processing and analytics.

Processing Paradigm: One key difference between Apache Spark and VoltDB is their processing paradigms. Apache Spark utilizes in-memory processing, which speeds up computations by keeping data in memory after reading from disk. On the other hand, VoltDB is an in-memory database that processes transactions in real-time, focusing on high-speed data processing and querying.
Scale-Out Capabilities: Apache Spark is designed to distribute data processing tasks across a cluster of machines, allowing for horizontal scaling as the data volume grows. In contrast, VoltDB is primarily optimized for scaling up on a single machine, providing linear scaling performance while maintaining low latency.
Data Consistency: When it comes to data consistency, VoltDB ensures strong consistency by adhering to ACID (Atomicity, Consistency, Isolation, Durability) properties for every transaction. In comparison, Apache Spark focuses more on providing eventual consistency, which may allow for some level of data inconsistency during distributed processing.
Use Cases: Apache Spark is commonly used for big data processing, machine learning, and real-time analytics due to its robust ecosystem, which includes libraries for various data processing tasks. VoltDB, on the other hand, is preferred for real-time transactional workloads, such as financial trading, telecommunications, and gaming applications that require low-latency processing.
Programming Language Support: Apache Spark supports multiple programming languages such as Scala, Java, Python, and R, offering flexibility to developers. In contrast, VoltDB primarily uses SQL for data manipulation and querying, making it easier for SQL developers to work with the database without the need to learn a new programming language.
Fault Tolerance Mechanisms: Apache Spark includes built-in fault tolerance mechanisms like RDD lineage and checkpoints to recover from failures in data processing tasks. VoltDB, being a distributed in-memory database, maintains high availability through data replication across multiple nodes, ensuring no single point of failure.

In summary, Apache Spark is suited for big data processing and analytics with its in-memory computing capabilities and wide range of use cases, while VoltDB is tailored for real-time transactional workloads demanding high-speed processing and strong data consistency.

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Advice on VoltDB, 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.

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Detailed Comparison

VoltDB	Apache Spark
VoltDB is a fundamental redesign of the RDBMS that provides unparalleled performance and scalability on bare-metal, virtualized and cloud infrastructures. VoltDB is a modern in-memory architecture that supports both SQL + Java with data durability and fault tolerance.	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.
In-Memory Performance with On-Disk Durability;Transparent Scalability with Data Consistency;NewSQL – All the benefits of SQL with Unlimited Scalability;JSON Support for Agile Development;ACID Compliant Transactions;Export Data to OLAP Stores and Data Warehouses	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 18	Stacks 3.1K
Followers 72	Followers 3.5K
Votes 18	Votes 140
Pros & Cons
Pros 5 SQL + Java 4 In-memory database 4 A brainchild of Michael Stonebraker 3 Very Fast 2 NewSQL	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 VoltDB, Apache Spark?

Redis

Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.

Presto

Distributed SQL Query Engine for Big Data

Hazelcast

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

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.

Aerospike

Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.

MemSQL

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.

Apache Ignite

It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

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.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Related Comparisons

Apache Spark vs VoltDB: What are the differences?

Apache Spark and VoltDB are two popular software solutions used in data processing and analytics.

Processing Paradigm: One key difference between Apache Spark and VoltDB is their processing paradigms. Apache Spark utilizes in-memory processing, which speeds up computations by keeping data in memory after reading from disk. On the other hand, VoltDB is an in-memory database that processes transactions in real-time, focusing on high-speed data processing and querying.
Scale-Out Capabilities: Apache Spark is designed to distribute data processing tasks across a cluster of machines, allowing for horizontal scaling as the data volume grows. In contrast, VoltDB is primarily optimized for scaling up on a single machine, providing linear scaling performance while maintaining low latency.
Data Consistency: When it comes to data consistency, VoltDB ensures strong consistency by adhering to ACID (Atomicity, Consistency, Isolation, Durability) properties for every transaction. In comparison, Apache Spark focuses more on providing eventual consistency, which may allow for some level of data inconsistency during distributed processing.
Use Cases: Apache Spark is commonly used for big data processing, machine learning, and real-time analytics due to its robust ecosystem, which includes libraries for various data processing tasks. VoltDB, on the other hand, is preferred for real-time transactional workloads, such as financial trading, telecommunications, and gaming applications that require low-latency processing.
Programming Language Support: Apache Spark supports multiple programming languages such as Scala, Java, Python, and R, offering flexibility to developers. In contrast, VoltDB primarily uses SQL for data manipulation and querying, making it easier for SQL developers to work with the database without the need to learn a new programming language.
Fault Tolerance Mechanisms: Apache Spark includes built-in fault tolerance mechanisms like RDD lineage and checkpoints to recover from failures in data processing tasks. VoltDB, being a distributed in-memory database, maintains high availability through data replication across multiple nodes, ensuring no single point of failure.

Apache Spark vs VoltDB

Overview