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2-phase commit

An atomic commitment protocol for distributed systems. It consists of two phases: commit-request and commit. In commit-request phase, transaction manager coordinates all the transaction resources to commit or abort. In commit-phase, transaction manager decides to finalize operation by committing or aborting according to the votes of the each transaction resource.


A set of properties (Atomicity, Consistency, Isolation, Durability) guaranteeing that transactions are processed reliably. Atomicity requires that each transaction be all or nothing, i.e., if one part of the transaction fails, the entire transaction fails). Consistency ensures that only valid data following all rules and constraints is written. Isolation ensures that transactions are securely and independently processed at the same time without interference (and without transaction ordering). Durability means that once a transaction has been committed, it will remain so, no matter if there is a power loss, crash, or error.


A high-speed access area that can be either a reserved section of main memory or a storage device.

Change data capture (CDC)

A data pipeline pattern for observing changes made to a database and extracting them in a form usable by other systems, for the purposes of replication, analysis and more.

Client/server topology

Hazelcast topology where members run outside the user application and are connected to clients using client libraries. The client library is installed in the user application.

Data pipeline

A series of actions that ingest data from one or more sources and move it to a destination for storage and analysis.

Embedded topology

Hazelcast topology where the members are in-process with the user application and act as both client and server.

Extract transform load (ETL)

A data pipeline pattern for collecting data from various sources, transforming (changing) it to conform to some rules, and loading it into a sink.

Garbage collection

The recovery of storage that is being used by an application when that application no longer needs the storage. This frees the storage for use by other applications (or processes within an application). It also ensures that an application using increasing amounts of storage does not reach its quota. Programming languages that use garbage collection are often interpreted within virtual machines like the JVM. The environment that runs the code is also responsible for garbage collection.

Hazelcast cluster

A virtual environment formed by Hazelcast members communicating with each other in the cluster.

Hazelcast partition

Memory segments containing the data. Hazelcast is built-on the partition concept, it uses partitions to store and process data. Each partition can have hundreds or thousands of data entries depending on your memory capacity. You can think of a partition as a block of data. In general and optimally, a partition should have a maximum size of 50-100 Megabytes.


An in-memory data grid (IMDG) is a data structure that resides entirely in memory and is distributed among many members in a single location or across multiple locations. IMDGs can support thousands of in-memory data updates per second and they can be clustered and scaled in ways that support large quantities of data.

Java heap

Java heap is the space that Java can reserve and use in memory for dynamic memory allocation. All runtime objects created by a Java application are stored in heap. By default, the heap size is 128 MB, but this limit is reached easily for business applications. Once the heap is full, new objects cannot be created and the Java application shows errors.


A data pipeline that’s packaged and submitted to a cluster member to run.

Least recently used (LRU)

A cache eviction algorithm where entries are eligible for eviction due to lack of interest by applications.

Least frequently used (LFU)

A cache eviction algorithm where entries are eligible for eviction due to having the lowest usage frequency.

Lite member

A member that does not store data and has no partitions. These members are often used to execute tasks and register listeners.


A Hazelcast instance. Depending on your Hazelcast topology, it can refer to a server or a Java virtual machine (JVM). Members belong to a Hazelcast cluster. Members may also be referred as member nodes, cluster members, Hazelcast members, or data members.


A type of communication where data is addressed to a group of destination members simultaneously.

Near cache

A caching model where an object retrieved from a remote member is put into the local cache and the future requests made to this object will be handled by this local member.


"Not Only SQL". A database model that provides a mechanism for storage and retrieval of data that is tailored in means other than the tabular relations used in relational databases. It is a type of database which does not adhering to the traditional relational database management system (RDMS) structure. It is not built on tables and does not employ SQL to manipulate data. It also may not provide full ACID guarantees, but still has a distributed and fault tolerant architecture.


Formerly known as the Open Services Gateway initiative, it describes a modular system and a service platform for the Java programming language that implements a complete and dynamic component model.

Partition table

Table containing all members in the cluster, mappings of partitions to members and further metadata.

Race condition

This condition occurs when two or more threads can access shared data and they try to change it at the same time.


An algorithm developed by Rivest, Shamir and Adleman to generate, encrypt and decrypt keys for secure data transmissions.


Process of converting an object into a stream of bytes in order to store the object or transmit it to memory, a database, or a file. Its main purpose is to save the state of an object in order to be able to recreate it when needed. The reverse process is called deserialization.


A distributed map that contains the saved state of a job’s computations.


A state in which a cluster of members gets divided (or partitioned) into smaller clusters of members, each of which believes it is the only active cluster.


A sequence of information exchange and related work (such as data store updating) that is treated as a unit for the purposes of satisfying a request and for ensuring data store integrity.