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SNB Driver - Part 1

  • Posted on: 27 November 2014
  • By: Alex Averbuch

In this multi-part blog we consider the challenge of running the LDBC Social Network Interactive Benchmark (LDBC SNB) workload in parallel, i.e. the design of the workload driver that will issue the queries against the System Under Test (SUT). We go through design principles that were implemented for the LDBC SNB workload generator/load tester (simply referred to as driver). Software and documentation for this driver is available here: https://github.com/ldbc/ldbc_driver/Multiple reference implementations by two vendors are available here: https://github.com/ldbc/ldbc_snb_implementationsand discussion of the schema, data properties, and related content is available here: https://github.com/ldbc/ldbc_snb_docs.

The following will concentrate on key decisions and techniques that were developed to support scalable, repeatable, distributed workload execution.

 

Problem Description 

 

The driver generates a stream of operations (e.g. create user, create post, create comment, retrieve person's posts etc.) and then executes them using the provided database connector. To be capable of generating heavier loads, it executes the operations from this stream in parallel. If there were no dependencies between operations (e.g., reads that depend on the completion of writes) this would be trivial. This is the case, for example, for the classical TPC-C benchmark, where splitting transaction stream into parallel clients (terminals) is trivial. However, for LDBC SNB Interactive Workload this is not the case: some operations within the stream do depend on others, others are depended on, some both depend on others and are depended on, and some neither depend on others nor are they depended on.

Consider, for example, a Social Network Benchmark scenario, where the data generator outputs a sequence of events such as User A posted a picture, User B left a comment to the picture of User A, etc. The second event depends on the first one in a sense that there is a causal ordering between them: User B can only leave a comment on the picture once it has been posted. The generated events are already ordered by their time stamp, so in case of the single-threaded execution this ordering is observed by default: the driver issues a request to the SUT with the first event (i.e., User A posts a picture), after its completion it issues the second event (create a comment). However, if events are executed in parallel, these two events may end up in different parallel sequences of events. Therefore, a driver needs a mechanism to ensure the dependency is observed even when the dependent events are in different parallel update streams.

The next blog entries in this series will discuss the approaches used in the driver to deal with these challenges.

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