|author||Pavel Tupitsyn <email@example.com>||Wed Mar 10 18:43:44 2021 +0300|
|committer||Pavel Tupitsyn <firstname.lastname@example.org>||Wed Mar 10 22:47:53 2021 +0300|
IGNITE-14293 .NET: Fix AffinityKey metadata in QueryEntity.KeyType `AffinityKey` system type, when used as a `QueryEntiry` key, got overwritten by `UnmanagedCallbacks.BinaryTypeGet` call, which broke the mapping to a corresponding Java type. * Add missing check for existing registered type (actual fix in Marshaller.cs) * Add check for system type overwrite * Add test for `AffinityKey` + `QueryEntity` Co-authored-by: Igor Sapego <email@example.com> (cherry picked from commit 8cc19b1041b64d7dd3bb368ce4b9cadee07a3718)
Apache Ignite is a distributed database for high-performance computing with in-memory speed.
Apache Ignite is designed to work with memory, disk, and Intel Optane as active storage tiers. The memory tier allows using DRAM and Intel® Optane™ operating in the Memory Mode for data storage and processing needs. The disk tier is optional with the support of two options -- you can persist data in an external database or keep it in the Ignite native persistence. SSD, Flash, HDD, or Intel Optane operating in the AppDirect Mode can be used as a storage device.
Even though Apache Ignite is broadly used as a caching layer on top of external databases, it comes with its native persistence - a distributed, ACID, and SQL-compliant disk-based store. The native persistence integrates into the Ignite multi-tier storage as a disk tier that can be turned on to let Ignite store more data on disk than it can cache in memory and to enable fast cluster restarts.
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.
Apache Ignite comes with a ANSI-99 compliant, horizontally scalable, and fault-tolerant SQL engine that allows you to interact with Ignite as with a regular SQL database using JDBC, ODBC drivers, or native SQL APIs available for Java, C#, C++, Python, and other programming languages. Ignite supports all DML commands, including SELECT, UPDATE, INSERT, and DELETE queries as well as a subset of DDL commands relevant for distributed systems.
Apache Ignite Machine Learning is a set of simple, scalable, and efficient tools that allow building predictive machine learning models without costly data transfers. The rationale for adding machine and deep learning to Apache Ignite is quite simple. Today's data scientists have to deal with two major factors that keep ML from mainstream adoption.
High-performance computing (HPC) is the ability to process data and perform complex calculations at high speeds. Using Apache Ignite as a high-performance compute cluster, you can turn a group of commodity machines or a cloud environment into a distributed supercomputer of interconnected Ignite nodes. Ignite enables speed and scale by processing records in memory and reducing network utilization with APIs for data and compute-intensive calculations. Those APIs implement the MapReduce paradigm and allow you to run arbitrary tasks across the cluster of nodes.