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Trino Community Broadcast

6: Query Planning, Remove duplicate predicates, and Memory settings




Video sections

Show notes

Presto nation, We want to hear from you! If you have a question or pull request that you would like us to feature on the show please join the Trino community chat and go to the #trino-community-broadcast channel and let us know there. Otherwise, you can message Manfred Moser or Brian Olsen directly. Also, feel free to reach out to us on our Twitter channels Brian @bitsondatadev and Manfred @simpligility.

Release 347

We discuss the Trino 347 release notes:

Official release announcement from Martin Traverso:

We’re happy to announce the release of Presto 347! This version includes:

  • Support for EXCEPT ALL and INTERSECT ALL
  • New syntax for changing the owner of a view
  • Performance improvements when inserting data into Hive tables

Notes from Manfred:

  • contains_sequence function for arrays.
  • CentOS 8 on docker image.
  • Kudu get dynamic filtering.

Concept of the week: Query planning

  • All happening on coordinator in cluster.
  • Before a query can be planned, the coordinator receives a SQL query and passes it to a parser.


  • The Parser parses the sql query into an AST (abstract syntax tree).
  • Then the analyzer checks for valid SQL including functions and such.


  • Request metadata about structure from catalogs.
    • Do the tables and columns exist?
    • What data types are used?
  • Request metadata about content (table stats, data location).
  • Create logical plan
    • Are function parameters using right data types?
    • What catalogs/schema/tables/columns need to be accessed?
    • Are joins using compatible field data types?
    • Optimize
      • Eliminate redundant conditions.
      • Figure best order of operations.
      • Decide on filtering early.
  • Create distributed plan (More on this in the next episode!)
    • Break logical plan up.
    • Adapt to parallel access by multiple workers to data source.
    • Break up operations so workers aggregate and process data from other workers.

Use EXPLAIN to learn what is planned. Also refer to chapter 4 in Presto: The Definitive Guide pg. 50.

PR of the week: PR 730 Remove duplicate predicates

In this week’s pull request, came from one of the co-creators Martin Traverso. This pull request removes duplicate predicates in logical binary expressions (AND, OR) and canonicalizes commutative arithmetic expressions and comparisons to handle a larger number of variants. Canonicalize is a big word but all it is saying is that if there are multiple representations of the same logic or data, then simplify it to a simpler or agreed upon normal form.

For example the statement COALESCE(a * (2 * 3), 1 - 1) is equivalent to COALESCE(6 * a, 0) as the expression 2 * 3 can be simplified to static integer.

This is an example of a logical plan because we are talking about the query syntax by optimizing the SQL. It differs from the distributed plan as we are not determining how the plan will be distributed, where this plan will run and it does not run further optimizations that are handled by the cost based optimizer such as pushdown predicates. We’ll talk about this step more in the next episode. For now let’s cover a few examples

Demo: PR 730 Remove duplicate predicates

The format of the EXPLAIN used is graphviz. The online tool used during the show is Viz.js. You can paste the output of your EXPLAIN queries to visualize the query in a tree form.

SELECT * FROM (VALUES 1) t(a) WHERE a = 1 OR 1 = a OR a = 1;

SELECT * FROM (VALUES 1) t(a) WHERE a = 2 OR 1 = a OR a = 3; 

SELECT * FROM tpch.tiny.orders
WHERE custkey > 100 and custkey > 50 and custkey > 50 and custkey > 50 and custkey > 50;  

FROM tpch.tiny.orders o 
  JOIN tpch.tiny.customer c 
  ON o.custkey = c.custkey AND o.custkey > 50 
WHERE c.custkey > 100 AND c.custkey > 50 LIMIT 10;

Question of the week: How should I allocate memory properties?

In this week’s question, we answer:

How should I allocate memory properties? CPU : 16Core MEM:64GB

Before answering this, we should make sure a few things about memory are clear.

User memory

Space needed that the user is capable of reasoning about:

  • Input Data
  • Hash tables execution
  • Sorting


  • query.max-memory-per-node - maximum amount of user memory that a query is allowed to use on a given worker.
  • query.max-memory (without the -per-node at the end) - This config caps the amount of user memory used by a single query over all worker nodes in your cluster.

System memory

Memory needed to facilitate internal usage

  • Shuffle buffers

NOTE: There are no settings for this memory as it is implicitly set by the user and total memory settings. Use this to calculate system memory:

  • max system memroy per node = query.max-total-memory-per-node - query.max-memory-per-node
  • max system memory = query.max-total-memory - query.max-memory

Total memory

Total Memory = System + User, but there are only properties for total and user memory.


  • query.max-total-memory-per-node - maximum amount of total memory that a query is allowed to use on a given worker.
  • query.max-total-memory(without the -per-node at the end) - This config caps the total memory used by a single query over all worker nodes in your cluster.

Heap headroom

The final setting I would like to cover is the memory.heap-headroom-per-node. This config sets aside memory for the JVM heap for allocations that are not tracked by Presto. You can typically go with the default on this setting which is 30% of the JVM’s max heap size (-Xmx setting).

JVM heap memory (-Xmx setting)

Now knowing that Presto is a java application means it runs on the JVM. None of these memory settings mean anything until we actually have the JVM that Presto is running on set aside sufficient memory. So how do I know I am setting sufficient memory based on my settings?

query.max-total-memory-per-node + memory.heap-headroom-per-node < -Xmx setting (Java heap)

Dain really covers the proportions well in detail on the recent training videos. Here’s a snippet of what he recommends.

All in all, try to estimate the amount of memory needed by your max anticipated query load, and if possible try to get even more than your estimate. Once Presto is discovered by users, they will start to use it even more and demands on the system will grow.


Upcoming events

Latest training from David, Dain, and Martin(Now with timestamps!):

Presto Summit Series - Real world usage


If you want to learn more about Presto yourself, you should check out the O’Reilly Presto Definitive guide. You can download the free PDF or buy the book online.

Music for the show is from the Megaman 6 Game Play album by Krzysztof Słowikowski.