Microsoft Technology Licensing, LLC

Boris Shulman

Alexander Alperovich

Lev Novik

Zhong Chen

Clemens Alden Szyperski

Kanstantsyn Zoryn

Xindi Zhang

Patrick Chung

Todd Robert Porter

Ke Liu

Shoupei Li

Described herein is a system and method of journaling of a streaming anchor resource. An input node can store a value of a property associated with the streaming data in a persistent indexed data structure. The input node can generate an anchor that describes a particular point in time in a data stream. The anchor can include an index into the persistent indexed data structure of the stored value of the property associated with the streaming data. The generated anchor and streaming data can be provided to the downstream node. During recovery of a downstream node, the input node can utilize a received anchor to retrieve a value of a property associated with the streaming data from the persistent indexed data structure, and, provide a batch of data based upon the received anchor and the retrieved property value.

Journaling of streaming anchor resource(s)

A method for joining an event stream with reference data includes loading a plurality of reference data snapshots from a reference data source into a cache. Punctuation events are supplied that indicate temporal validity for the plurality of reference data snapshots in the cache. A logical barrier is provided that restricts a flow of data events in the event stream to a cache lookup operation based on the punctuation events. The cache lookup operation is performed with respect to the data events in the event stream that are permitted to cross the logical barrier.

Punctuation controlled temporal reference data lookup

Described herein is a system and method for dynamically scaling a stream processing system (e.g., 'exactly once' data stream processing system). Various parameter(s) (e.g., user-configurable capacity, real-time load metrics, and/or performance counters) can be used to dynamically scale in and/or scale out the 'exactly once' stream processing system without system restart. Delay introduced by this scaling operation can be minimized by utilizing a combination of mutable process topology (which can dynamically assign certain parts of the system to a new host machine) and controllable streaming processor movement with checkpoints and the streaming protocol controlled recovery which still enforces the &#x201c;exactly once&#x201d; delivery metric.

Dynamic scaling for data processing streaming system

Described herein is a system and method for startup and/or recovery for stream processing. During a startup phase: start anchor request(s), each identifying a particular time, are accumulated until request(s) are pending from downstream nodes. A minimum time of the accumulated start anchor request(s) is determined. If the processing system is an input node, an anchor associated with the determined minimum time is generated. Otherwise, a start anchor request is provided to an upstream node identifying the determined minimum time. Once the anchor associated with the determined minimum time is received (or generated), the anchor is provided in response to a polled start anchor request for the determined minimum time from a downstream node. Asynchronous requests for batches of data bounded by two specific anchors are performed in accordance with information stored in an ordered collection of anchors during a recovery phase.

Enhanced anchor protocol for event stream processing

Methods, systems, apparatuses, and computer program products are provided for processing a stream of data. A maximum temporal divergence is established for data flushed to a data store from a plurality of upstream partitions. Each of a plurality of data flushers, each corresponding to an upstream partition, may obtain an item of data from a data producer. Each data flusher may determine whether flushing the data to the data store would exceed the maximum temporal divergence. Based at least on determining that flushing the data to the data store would not exceed the maximum temporal divergence, the data may be flushed to the data store for ingestion by a downstream partition and a data structure (e.g., a ledger) may be updated to indicate a time associated with the most recent item of data flushed to the data store.

Efficient out of process reshuffle of streaming data

A method for facilitating anchor shortening across streaming nodes in an event stream processing system may include receiving a full anchor at an upstream marshaller. The full anchor may be associated with a data batch that corresponds to one or more event streams. The full anchor may include an indication of an input point for the one or more event streams. The full anchor may be received from an upstream compute processor. The method may also include mapping the full anchor to an index anchor and passing the index anchor to a downstream marshaller.

Anchor shortening across streaming nodes

Described herein is a system and method for a static streaming job startup sequence. During compilation of a streaming job, a graph of computing nodes of the streaming job is traversed to determine a minimum start time of computing node(s) downstream of each input computing node of the streaming job. Also, during compilation, a start time is assigned to each input computing node in accordance with the determined minimum start time. During execution of the streaming job, responsive to receipt of the trigger anchor by a particular input node, processing of the particular input computing node using the determined minimum start time is commenced. The input computing node further generates an anchor. Input data is received, and, a batch of data provided in accordance with the received input data and generated anchor.

Static streaming job startup sequence

Stream processing can be performed using a pull-based, anchor-based methodology that guarantees once and only once processing and repeatability of the creation of output with no additional communication overhead during normal processing. Each node (computing device) in the graph (representing interconnected computing devices) establishes a system of anchors. An anchor describes a point in the output stream of the node, so that every event in the stream is either before or after any given anchor.

Using anchors for reliable stream processing

Processing streaming data in accordance with policies that group data by source, enforce a maximum permissible late arrival value for streaming data, a maximum permissible early arrival for data and/or a maximum degree to which data can be out of order and still be compliant with the out of order policy is described. The correct starting point for reading a data stream so as to produce correct output from a given output start time can be enabled using the early arrival policy. Using combinations of policies, output can be generated promptly (with low latency). When input from a given source is not disrupted, output can be generated with low latency. Output can be generated even when the input stops by applying a late arrival policy.

Handling out of order events

Large amounts of memory can be consumed in streaming joins because events from one stream are held in memory while waiting for matching events from a second stream. Memory needs can be reduced by analyzing the join condition to determine the bounds on the time discrepancy between events in the two streams. When it is determined that an event from one stream must occur prior to the matching event from the other stream, the later-arriving stream data can be ingested with an intentional delay. When it is determined that regardless of input received from a first stream, no output will be produced when there is no input from the second stream, pulling data from the first stream can cease. A multi-stage join plan can be employed so that a less busy stream can be scanned with increasing amounts of intentional delay. Only unmatched data is stored.

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Sammamish, WA, United States of America

Boris Shulman