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Trino turns 10: Starburst celebrates ten years of its open source query engine

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Starburst, provider of enterprise platform offerings for optimizing the Trino distributed SQL query engine, recently marked a milestone anniversary of the initial open-source code family that the engines development stems. Trino is really a highly parallel, open-source distributed SQL query engine made to perform interactive analytics on large volumes of data. VentureBeat spoke with co-creator Dain Sundstrom concerning the projects growth and its own future.

Open Source project lineage

A decade ago, the initial Presto/Trino open-source code family was started by Sundstrom and co-creators Martin Traverso, David Phillips and Eric Hwang, at Facebook, to resolve the issue of analytics and querying at speed over Facebooks large datasets. In 2018, the creators parted with Facebook and the initial code family was put into two lineages, the main one remaining under Facebook being called PrestoDB, and the main one being centered on by the creators differentiated by the name PrestoSQL. In December, 2020, the PrestoSQL lineage of the code was rebranded to Trino, under which name this lineage of the code is still developed today.

Continued refinements

The engine was originally intended to perform querying at speed over massive datasets, and contains grown and been refined greatly since its start. Features such as for example security, that hardly existed in the initial few releases, are actually core to the project. The ecosystem of tools and integrations supported has expanded, as gets the amount of data connectors. Included in these are connectors to relational data sources such as for example PostgreSQL, Oracle and SQL Server, and also non-traditional sources such as for example Elasticsearch, OpenSearch, MongoDB and Apache Kafka. Sundstrom described additional refinements currently in the works as including redesigning the event language for improved extensibility, improving support for ETL workloads and causeing this to be functionality are better, out-of-the box, to boost productivity for non-experts.

Sundstrom says the creators made a decision to open-source the project in line with the shared open-source background included in this. Some challenges they faced and overcame included growing and scaling the machine out not only the software, that is a difficult enough problem in and of itself, but additionally the city: helping start communication between different members of the city to operate a vehicle collaboration around solving a standard problem, instead of solutions being developed to exactly the same problem in parallel.

Trino use cases

Trino can be used by many companies, including Netflix and LinkedIn, for internal analytics, plus some of the companies also donate to the open-source project, such as for example Bloomberg and Comcast. Sundstrom discussed how Trino is particularly favored by real-time, internet dispatch/taxi-like services and food delivery services, including Lyft and DoorDash, since it can perform very quickly low-latency queries over large datasets. Sundstrom mentioned that in addition, it performs very well on geo-spatial data, that is becoming ever-more common, and will be difficult to investigate.

Future view of Trino

Seeking to the near future, Sundstrom said he could be worked up about Trino and its own future, because the pace of innovation continues to accelerate and the utilization cases can cover expanded workloads and data types. He anticipates bigger growth in the issues Trino can approach for instance, adding the ability to process geospatial data implies that mapping companies, cellular providers, and food delivery companies can derive added value from analyzing customer data.

The Trino community has recently shown itself very with the capacity of finding innovative answers to its users problems. Its hard to fathom that the Presto/Trino platforms are actually 10 yrs . old, but its an easy task to imagine Trino can be applicable to more use cases and user requirements as time passes.

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