It’s amazing how far we have come! Our massively-parallel processing SQL query
engine, Trino, has really grown up. We have moved beyond just querying object
stores using Hive, beyond just one company using the project, beyond usage in
Silicon Valley, beyond simple SQL
SELECT statements, and definitely also
beyond our expectations. Let’s have a look at some of the great technical and
architectural changes the project underwent, and how we all benefit from the
commitment to quality, openness and collaboration.
Runtime and deployment #
Starting with how you even run Trino and install it, numerous changes came about in the last decade. We moved from Java 7 to Java 8, then to Java 11, and only recently to the latest supported Java LTS release - Java 17. Each time we benefited from the innovations in the runtime performance as well as the improved Java language features. With Java 17, we are just about to start a lot of these improvements.
When it comes to actually running and deploying Trino, the tarball is still a good choice for simple installation and as a base for other packages. Over time we added RPM archive support, which is being replaced more and more by Docker containers. The container images also enable modern deployment on Kubernetes with our Helm chart.
And let us add one last note about deployments. Trino was always designed to work on large servers. However the actual growth in a decade in the real world has amazing to see. Machine sizes keep growing to hundreds of CPU cores and closer to a terabyte of memory, and these truly large machines are now running as clusters with many workers of that size. And more and more of these deployments take advantage of our added support for the ARM processor architecture and the increasing availability of suitable servers from the cloud providers.
What is security, authentication, authorization? In the beginning none of this
existed in the first releases of Trino. Two years after launch we added first
simple authentication and authorization support. Today the days when Kerberos
was critical, and you needed to use the Java KeyStore in most deployments are
long gone. The wide adoption of Trino led to improvements such as support for
automatic certificate creation and TLS for internal
secret injection from environment
variables, and the many
starting with LDAP and password file, to the modern OAuth2.0 and SSO systems.
Trino supports fine-grained access control and security management SQL commands
You can secure connections from client tools, and use numerous methods to ensure
secured access to your data sources.
Client tools and integrations #
In the very beginning all you could do is submit a query to the client REST API. Very quickly we added the Trino CLI and the JDBC driver. And while it has continued to be widely used in the community, and gathered great features such as command-completion and history, different output formats, and much more, the Trino CLI is not the only tool anymore. The JDBC driver, the Python client, the Go client, and the ODBC driver from Starburst, all expanded the support for different client tools. You can query Trino in your Java-based IDE, such as IntelliJ IDEA, or database tool, such as DBeaver or Metabase. You can take advantage of visualizations in Apache Superset, or automate with Apache Airflow, dbt, or Apache Flink. And many commercial tools such as Tableau, Looker, PowerBI, or ThoughtSpot also proudly support Trino users.
All the client tools and integrations rely on the rich SQL support of Trino,
which has grown tremendously. Purely analytics-related support for
all its complexities was not enough. Trino gained support for data management to
create schema and tables, but also views and materialized views. And with that
write support we needed
That’s all done and
MERGE is next. But the core language features were not
able to satisfy the needs of our users. We added functions for a large variety
of topics ranging from simple string and date
functions to JSON
functions, and many
From the core language perspective we added newer SQL functionality, such as
window functions and
MATCH_RECOGNIZE support. Currently we are on a journey to implement
support for table functions, including polymorphic table functions.
Connectors and data sources #
When it comes to the new SQL language features, there are two categories. There
are generic functions and statements that build on top of commonly used
SELECT. These typically work with any connector and therefore
any data sources. And then there are SQL language features that need support in
a connector. After all, inserting data in PostgreSQL and an object storage
system are very different. Our community has been hard at work however, and
numerous connectors have gone way beyond simple read-only access.
Looking at the number of available connectors, innovation has been tremendous. The original Hive connector with support for HDFS and a Hive Metastore Service, became a powerhouse of features. Support for object storage systems including Amazon S3 and compatible systems, Azure Data Lake Storage, and Google Cloud Storage, was supplemented by support for Amazon Glue as metastore. We also constantly added support for different file formats in these systems, and improved performance for ORC, Parquet, Avro, and others.
The initial idea to support other data sources led to connectors for over a dozen other databases, including relational systems such PostgreSQL, Oracle, SQL Server, and many others. We also gained support for Elasticsearch and OpenSearch, MongoDB, Apache Kafka, and other systems that traditionally are not available to query with SQL. Trino unlocks completely new use cases for these systems.
The wide range of supported systems includes traditional data lakes and data warehouses. With the emerging new table formats and the related Trino connectors, our project is a powerful tool to run your lakehouse system. Delta Lake and Apache Iceberg connectors are already capable of full read and write operations and include numerous other features. An Apache Hudi connector is in the works and coming soon.
Query processing and performance #
Last but not least, these queries also need to be processed. From the start high efficiency and low latency were a core design goal, and with features like native compilation the resulting performance surpassed other systems. Over the years our query analyzer and planner was supplemented by more and more sophisticated algorithms and features. Connectors learned to retrieve and manage table statistics, the optimizer was created and morphed into a cost-based optimizer, and we added further improvements that benefit query processing performance. We added dynamic filtering, dynamic partition pruning, predicate pushdown, join pushdown, aggregate function pushdown and numerous others. Each of these improvements was also finely tuned, and runs in production with huge workloads providing us more data on how to improve next.
One large pivot we recently added was the addition of fault-tolerant query execution mode. Queries execution can survive cluster node failures when this feature is enabled. Parts of the execution can be retried and query processing can proceed. Trino is moving on from the best analytics engine to be the best query engine for many more use case!
Looking forward #
As you can see there is a lot to look back to and celebrate. But while we are definitely proud of our successes working with the community, we see no time to rest. There are many more improvements we are working on. Just to tease you a bit, let us just mention that there will be more polymorphic table functions, new lakehouse connectors and features, more client tools, and maybe even dynamic configuration of the cluster.
What would you like to add? Join us to celebrate and innovate towards your favorite features. And who knows, we might see you in the Trino Summit in November, or in a future episode of the Trino Community Broadcast.