For information on how to get started with Apache Ignite, please visit: Getting Started.
You can find the full Apache Ignite documentation here: Full documentation.
Apache Ignite is a memory-centric multi-model distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.
Ignite's durable memory component treats RAM not just as a caching layer but as a complete fully functional storage layer. This means that users can turn the persistence on and off as needed. If the persistence is off, then Ignite can act as a distributed in-memory database or in-memory data grid, depending on whether you prefer to use SQL or key-value APIs. If the persistence is turned on, then Ignite becomes a distributed, horizontally scalable database that guarantees full data consistency and is resilient to full cluster failures.
Ignite Native Persistence is a distributed, ACID, and SQL-compliant disk store that transparently integrates with Ignite's Durable Memory as an optional disk layer storing data and indexes on SSD, Flash, 3D XPoint, and other types of non-volatile storages.
With the Ignite Persistence enabled, you no longer need to keep all the data and indexes in memory or warm it up after a node or cluster restart because the Durable Memory is tightly coupled with persistence and treats it as a secondary memory tier. This implies that if a subset of data or an index is missing in RAM, the Durable Memory will take it from the disk.
Data stored in Ignite is ACID-compliant both in memory and on disk, making Ignite a strongly consistent system. Ignite transactions work across the network and can span multiple servers.
Ignite provides full support for SQL, DDL and DML, allowing users to interact with Ignite using pure SQL without writing any code. This means that users can create tables and indexes as well as insert, update, and query data using only SQL. Having such complete SQL support makes Ignite a one-of-a-kind distributed SQL database.
The in-memory data grid component in Ignite is a fully transactional distributed key-value store that can scale horizontally across 100s of servers in the cluster. When persistence is enabled, Ignite can also store more data than fits in memory and survive full cluster restarts.
Most traditional databases work in a client-server fashion, meaning that data must be brought to the client side for processing. This approach requires lots of data movement from servers to clients and generally does not scale. Ignite, on the other hand, allows for sending light-weight computations to the data, i.e. collocating computations with data. As a result, Ignite scales better and minimizes data movement.
Ignite is an elastic, horizontally scalable distributed system that supports adding and removing cluster nodes on demand. Ignite also allows for storing multiple copies of the data, making it resilient to partial cluster failures. If the persistence is enabled, then data stored in Ignite will also survive full cluster failures. Cluster restarts in Ignite can be very fast, as the data becomes operational instantaneously directly from disk. As a result, the data does not need to be preloaded in-memory to begin processing, and Ignite caches will lazily warm up resuming the in memory performance.
You can view Apache Ignite as a collection of independent, well-integrated components geared to improve performance and scalability of your application.
Some of these components include:
Below you’ll find a brief explanation for each of them:
Ignite nodes can automatically discover each other. This helps to scale the cluster when needed, without having to restart the whole cluster. Developers can also leverage Ignite’s hybrid cloud support that allows establishing connection between private cloud and public clouds such as Amazon Web Services, providing them with best of both worlds.
Ignite data grid is an in-memory distributed key-value store which can be viewed as a distributed partitioned
Dictionary, with every cluster node owning a portion of the overall data. This way the more cluster nodes we add, the more data we can cache.
Unlike other key-value stores, Ignite determines data locality using a pluggable hashing algorithm. Every client can determine which node a key belongs to by plugging it into a hashing function, without a need for any special mapping servers or name nodes.
Ignite data grid supports local, replicated, and partitioned data sets and allows to freely cross query between these data sets using standard SQL and LINQ syntax. Ignite supports standard SQL and LINQ for querying in-memory data including support for distributed joins.
Apache Ignite incorporates distributed SQL database capabilities as a part of its platform. The database is horizontally scalable, fault tolerant and SQL ANSI-99 compliant. It supports all SQL, DDL, and DML commands including SELECT, UPDATE, INSERT, MERGE, and DELETE queries. It also provides support for a subset of DDL commands relevant for distributed databases.
With Ignite Durable Memory architecture, data as well as indexes can be stored both in memory and, optionally, on disk. This allows executing distributed SQL operations across different memory layers achieving in-memory performance with the durability of disk.
You can interact with Apache Ignite using the SQL language via natively developed APIs for Java, .NET and C++, or via the Ignite JDBC or ODBC drivers. This provides a true cross-platform connectivity from languages such as PHP, Ruby and more.
Distributed computations are performed in parallel fashion to gain high performance, low latency, and linear scalability. Ignite compute grid provides a set of simple APIs that allow users distribute computations and data processing across multiple computers in the cluster. Distributed parallel processing is based on the ability to take any computation and execute it on any set of cluster nodes and return the results back.
We support these features, amongst others:
Both. Native persistence in Ignite can be turned on and off. This allows Ignite to store data sets bigger than can fit in the available memory. Essentially, the smaller operational data sets can be stored in-memory only, and larger data sets that do not fit in memory can be stored on disk, using memory as a caching layer for better performance.
Yes. Data in Ignite is either partitioned or replicated across a cluster of multiple nodes. This provides scalability and adds resilience to the system. Ignite automatically controls how data is partitioned, however, users can plug in their own distribution (affinity) functions and collocate various pieces of data together for efficiency.
Not fully. Although Ignite aims to behave like any other relational SQL database, there are differences in how Ignite handles constraints and indexes. Ignite supports primary and secondary indexes, however, the uniqueness can only be enforced for the primary indexes. Ignite also does not support foreign key constraints.
Essentially, Ignite purposely does not support any constraints that would entail a cluster broadcast message for each update and significantly hurt performance and scalability of the system.
Yes. Even though Ignite durable memory works well in-memory and on-disk, the disk persistence can be disabled and Ignite can act as a pure in-memory database.
Not fully. ACID Transactions are supported, but only at key-value API level. Ignite also supports cross-partition transactions, which means that transactions can span keys residing in different partitions on different servers.
At SQL level Ignite supports atomic, but not yet transactional consistency. Ignite community plans to implement SQL transactions in version 2.4.
Yes. Ignite provides a feature rich key-value API, that is JCache (JSR-107) compliant and supports Java, C++, and .NET.
Yes. Ignite is a full-featured data grid, which can be used either in pure in-memory mode or with Ignite native persistence. It can also integrate with any 3rd party database, including any RDBMS or NoSQL store.
Ignite durable memory architecture allows Ignite to extend in-memory computing to disk. It is based on a paged-based off-heap memory allocator which becomes durable by persisting to the write-ahead-log (WAL) and, then, to main Ignite persistent storage. When persistence is disabled, durable memory acts like a pure in-memory storage.
Ignite is a distributed system and, therefore, it is important to be able to collocate data with data and compute with data to avoid distributed data noise. Data collocation becomes especially important when performing distributed SQL joins. Ignite also supports sending user logic (functions, lambdas, etc.) directly to the nodes where the data resides and computing on the data locally.