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Apache Ignite vs VoltDB: What are the differences?

Apache Ignite and VoltDB are both popular in-memory database platforms used for real-time data processing. Below are the key differences between Apache Ignite and VoltDB.

  1. Architecture: Apache Ignite uses a distributed architecture with a shared-nothing design, allowing it to scale horizontally across cluster nodes. On the other hand, VoltDB utilizes a single-node shared-nothing architecture that scales vertically by adding more resources to a single server.

  2. Consistency Model: Apache Ignite supports both strong and eventual consistency models, providing flexibility for different use cases. In contrast, VoltDB strictly adheres to the ACID properties with strong consistency guarantees for all transactions.

  3. SQL Support: Apache Ignite offers full SQL support with a distributed SQL engine for querying and processing data across the cluster. VoltDB, on the other hand, uses a DML-centric SQL dialect specifically optimized for fast transaction processing.

  4. Data Replication: Apache Ignite provides various replication techniques, including partition-based replication and data center replication for high availability and fault tolerance. VoltDB employs a fully replicated data model, storing a copy of each data partition on every server in the cluster.

  5. Data Durability: Apache Ignite allows users to choose between in-memory and disk-based storage options for data durability, providing flexibility based on performance and durability requirements. VoltDB primarily focuses on in-memory storage for low-latency data processing, with the option to persist data to disk for fault tolerance.

In summary, Apache Ignite and VoltDB differ in their architecture, consistency models, SQL support, data replication techniques, and data durability options.

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Pros of Apache Ignite
Pros of VoltDB
  • 5
    Written in java. runs on jvm
  • 5
    Multiple client language support
  • 5
  • 5
    High Avaliability
  • 4
    Sql query support in cluster wide
  • 4
    Rest interface
  • 4
    Load balancing
  • 3
    Distributed compute
  • 3
    Better Documentation
  • 2
    Easy to use
  • 1
    Distributed Locking
  • 5
    SQL + Java
  • 4
    In-memory database
  • 4
    A brainchild of Michael Stonebraker
  • 3
    Very Fast
  • 2

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What is 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

What is VoltDB?

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.

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What tools integrate with Apache Ignite?
What tools integrate with VoltDB?
What are some alternatives to Apache Ignite and VoltDB?
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.
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
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.
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
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.
See all alternatives