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Term Definition


The act of building up an intermediate result inside a mutable object (called the accumulator) as a part of performing an aggregate operation. After all accumulation is done, a finishing function is applied to the object to produce the result of the operation.

Aggregate Operation

A set of functional primitives that instructs Jet how to calculate some aggregate function over one or more data sets. Used in the group-by, co-group and windowing transforms.


The act of applying an aggregate function to a stream of items. The result of the function can be simple, like a sum or average, or complex, like a collection of all aggregated items.

At-Least-Once Processing Guarantee

The system guarantees to process each item of the input stream(s), but doesn't guarantee it will process it just once.

Batch Processing

The act of processing a finite dataset, such as one stored in Hazelcast IMDG or HDFS.

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.


An operation that is a mix of an SQL JOIN and GROUP BY with specific restrictions. Sometimes called a "stream join". Each item of each stream that is being joined must be mapped to its grouping key. All items with the same grouping key (from all streams) are aggregated together into a single result. However, the result can be structured and preserve all input items separated by their stream of origin. In that form the operation effectively becomes a pure JOIN with no aggregation.


Directed Acyclic Graph which Hazelcast Jet uses to model the relationships between individual steps of the data processing.


A DAG element which holds the logic on how to route the data from one vertex's processing units to the next one's.

Embedded Topology

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

Event time

A data item in an infinite stream typically contains a timestamp data field. This is its event time. As the stream items go by, the event time passes as the items' timestamps increase. A typical distributed stream has a certain amount of event time disorder (items aren't strictly ordered by their timestamp) so the "passage of event time" is a somewhat fuzzy concept. Jet uses the watermark to superimpose order over the disordered stream.

Exactly-Once Processing Guarantee

The system guarantees that it will process each item of the input stream(s) and will never process an item more than once.

Fault Tolerance

The property of a distributed computation system that gives it resilience to changes in the topology of the cluster running the computation. If a member leaves the cluster, the system adapts to the change and resumes the computation without loss.


A special-purpose stream join optimized for the use case of data enrichment. Each item of the primary stream is joined with one item from each of the enriching streams. Items are matched by the join key. The name "hash-join" stems from the fact that the contents of the enriching streams are held in hashtables for fast lookup. Hashtables are replicated on each cluster member, which is why this operation is also known as a "replicated join".

Hazelcast IMDG

An In-Memory Data grid (IMDG) is a data structure that resides entirely in memory, and is distributed among many machines in a single location (and possibly replicated across different locations). IMDGs can support millions of in-memory data updates per second, and they can be clustered and scaled in ways that support large quantities of data. Hazelcast IMDG is the in-memory data grid offered by Hazelcast.


Hadoop Distributed File System. Hazelcast Jet can use it both as a data source and a sink.

Jet Job

A unit of distributed computation that Jet executes. One job has one DAG specifying what to do. A distributed array of Jet processors performs the computation.


Apache Kafka is a product that offers a distributed publish-subscribe message queue with guarantees of delivery and message persistence. The most commonly used component over which heterogenous distributed systems exchange data.


The time that passes from the occurrence of an event that triggers some response to the occurrence of the response. In the case of Hazelcast Jet's stream processing, latency refers to the time that passes from the point in time the last item that belongs to a window enters the system to the point where the result for that window appears in the output.


A Hazelcast Jet instance (node) that is a member of a cluster. A single JVM can host one or more Jet members, but in production there should be one member per physical machine.

Partition (Data)

To guarantee that all items with the same grouping key are processed by the same processor, Hazelcast Jet uses a total surjective function to map each data item to the ID of its partition and assigns to each processor its unique subset of all partition IDs. A partitioned edge then routes all items with the same partition ID to the same processor.

Partition (Network)

A malfunction in network connectivity that splits the cluster into two or more parts that are mutually unreachable, but the connections among nodes within each part remain intact. May cause each of the parts to behave as if it was "the" cluster that lost the other members. Also known as "split brain".


Hazelcast Jet's name for the high-level description of a computation job constructed using the Pipeline API. Topologically it is a DAG, but the vertices have different semantics than the Core API vertices and are called pipeline stages. Edges are implicit and not expressed in the API. Each stage (except for source/sink stages) has an associated transform that it performs on its input data.


The unit which contains the code of the computation to be performed by a vertex. Each vertex’s computation is implemented by a processor. On each Jet cluster member there are one or more instances of the processor running in parallel for a single vertex.

Session Window

A window that groups an infinite stream's items by their timestamp. It groups together bursts of events closely spaced in time (by less than the configured session timeout).

Sliding Window

A window that groups an infinite stream's items by their timestamp. It groups together events that belong to a segment of fixed size on the timeline. As the time passes, the segment slides along, always extending from the present into the recent past. In Jet, the window doesn't literally slide, but hops in steps of user-defined size. ("Time" here refers to the stream's own notion of time, i.e., event time.)


A resource present in a Jet job's environment that delivers a data stream to it. Hazelcast Jet uses a source connector to access the resource. Alternatively, source may refer to the DAG vertex that hosts the connector.


A resource present in a Jet job's environment that accepts its output data. Hazelcast Jet uses a sink connector to access the resource. Alternatively, sink may refer to the vertex that hosts the connector.


A generalization of the term "clock skew" applied to distributed stream processing. In this context it refers to the deviation in event time as opposed to wall-clock time in the classical usage. Several substreams of a distributed stream may at the same time emit events with timestamps differing by some delta, due to various lags that accumulate in the delivery pipeline for each substream. This is called stream skew. Event skew refers to the disorder within a substream, where data items appear out of order with respect to their timestamps.

Split Brain

A popular name for a network partition, which see above.

Stream Processing

The act of processing an infinite stream of data, typically implying that the data is processed as soon as it appears. Such a processing job must explicitly deal with the notion of time in order to make sense of the data. It achieves this with the concept of windowing.


A measure for the volume of data a system is capable of processing per unit of time. Typical ways to express it for Hazelcast Jet are in terms of events per second and megabytes per second.

Tumbling Window

A window that groups an infinite stream's items by their timestamp. It groups together events that belong to a segment of fixed size on the timeline. As the time passes, the segment "tumbles" along, never covering the same point in time twice. This means that each event belongs to just one tumbling window position. ("Time" here refers to the stream's own notion of time, i.e., event time.)


The DAG element that performs a step in the overall computation. It receives data from its inbound edges and sends the results of its computation to its outbound edges. There are three kinds of vertices: source (has only outbound edges), sink (has only inbound edges) and computational (has both kinds of edges).


A concept that superimposes order over a disordered underlying data stream. An infinite data stream's items represent timestamped events, but they don't occur in the stream ordered by the timestamp. The value of the watermark at a certain location in the processing pipeline denotes the lowest value of the timestamp that is expected to occur in the upcoming items. Items that don't meet this criterion are discarded because they arrived too late to be processed.


The act of splitting an infinite stream's data into windows according to some rule, most typically one that involves the item's timestamps. Each window becomes the target of an aggregate function, which outputs one data item per window (and per grouping key).