Like in any standard relational database, you can use indexes to optimize query performance in Materialize. Improvements can be significant, reducing some query times down to single-digit milliseconds.

Building an efficient index depends on the clauses used in your queries, as well as your expected access patterns. Use the following as a guide:

GROUP BY, ORDER BY and LIMIT clauses currently do not benefit from an index.


Speed up a query involving a WHERE clause with equality comparisons to literals (e.g., 42, or 'foo'):

Clause Index
WHERE x = 42 CREATE INDEX ON obj_name (x);
WHERE x IN (1, 2, 3) CREATE INDEX ON obj_name (x);
WHERE (x, y) IN ((1, 'a'), (7, 'b'), (8, 'c')) CREATE INDEX ON obj_name (x, y);
WHERE x = 1 AND y = 'abc' CREATE INDEX ON obj_name (x, y);
WHERE (x = 5 AND y = 'a') OR (x = 7 AND y = '') CREATE INDEX ON obj_name (x, y);
WHERE 2 * x = 64 CREATE INDEX ON obj_name (2 * x);
WHERE upper(y) = 'HELLO' CREATE INDEX ON obj_name (upper(y));

You can verify that Materialize is accessing the input by an index lookup using EXPLAIN. Check for lookup_value after the index name to confirm that an index lookup is happening, i.e., that Materialize is only reading the matching elements of the index instead of scanning the entire index:

EXPLAIN SELECT * FROM foo WHERE x = 42 AND y = 'hello';
                               Optimized Plan
 Explained Query (fast path):                                               +
   Project (#0, #1)                                                         +
     ReadExistingIndex materialize.public.foo_x_y lookup_value=(42, "hello")+
 Used Indexes:                                                              +
   - materialize.public.foo_x_y                                             +

Matching multi-column indexes to multi-column WHERE clauses

In general, your index key should exactly match the columns that are constrained in the WHERE clause. In more detail:

  • If the WHERE clause constrains fewer fields than your index key includes, then the index will not be used. For example, an index on (x, y) cannot be used to speed up WHERE x = 7.
  • If the WHERE clause constrains more fields than your index key includes, then the index might still provide some speedup, but it won’t necessarily be optimal: In this case, the index lookup is performed using only those constraints that are included in the index key, and the rest of the constraints will be used to subsequently filter the result of the index lookup.
  • If OR is used and not all arguments constrain the same fields, create an index for the intersection of the constrained fields. For example, if you have WHERE (x = 51 AND y = 'bbb') OR (x = 76 AND z = 9), create an index just on x.
  • If OR is used and its arguments constrain completely disjoint sets of fields (e.g. WHERE x = 5 OR y = 'aaa'), try to rewrite your query using a UNION (or UNION ALL), where each argument of the UNION has one of the original OR arguments.


In general, you can improve the performance of your joins by creating indexes on the columns being joined. This comes at the cost of additional memory usage. Fortunately, Materialize’s in-memory arrangements allow the system to share indexes across queries, which means for multiple queries, an index is a fixed upfront cost with memory savings for each new query that uses it.

Let’s create a few tables to work through examples.

CREATE TABLE teachers (id INT, name TEXT);
CREATE TABLE sections (id INT, teacher_id INT, course_id INT, schedule TEXT);
CREATE TABLE courses (id INT, name TEXT);

Multiple Queries Join On the Same Collection

Let’s consider two queries that join on a common collection. The idea is to create an index that can be shared across the two queries to save memory.

Here is a query where we join a collection teachers to a collection sections to see the name of the teacher, schedule, and course ID for a specific section of a course.

FROM teachers t
INNER JOIN sections s ON t.id = s.teacher_id;

Here is another query that also joins on teachers.id. This one counts the number of sections each teacher teaches.

FROM teachers t
INNER JOIN sections s ON t.id = s.teacher_id
GROUP BY t.id;

We can eliminate redundant memory usage for these two queries by creating an index on the common column being joined, teachers.id.

CREATE INDEX pk_teachers ON teachers (id);

Joins with Filters

If your query filters one or more of the join inputs by a literal equality (e.g., WHERE t.name = 'Escalante'), place one of those input collections first in the FROM clause. In particular, this can speed up ad hoc SELECT queries by accessing collections using index lookups rather than full scans.

Optimize Multi-Way Joins with Delta Joins

Materialize has access to a join execution strategy we call DeltaQuery, a.k.a. delta joins, that aggressively re-uses indexes and maintains no intermediate results. Materialize considers this plan only if all the necessary indexes already exist, in which case the additional memory cost of the join is zero This is typically possible when you index all the join keys.

From the previous example, add the name of the course rather than just the course ID.

CREATE VIEW course_schedule AS
      t.name AS teacher_name,
      c.name AS course_name
  FROM teachers t
  INNER JOIN sections s ON t.id = s.teacher_id
  INNER JOIN courses c ON c.id = s.course_id;

In this case, we create indexes on the join keys to optimize the query:

CREATE INDEX pk_teachers ON teachers (id);
CREATE INDEX sections_fk_teachers ON sections (teacher_id);
CREATE INDEX pk_courses ON courses (id);
CREATE INDEX sections_fk_courses ON sections (course_id);
EXPLAIN VIEW course_schedule;
                  Optimized Plan
 Explained Query:                                +
   Project (#1, #5, #7)                          +
     Filter (#0) IS NOT NULL                     +
       Join on=(#0 = #3 AND #4 = #6) type=delta  + <--- using delta join
         ArrangeBy keys=[[#0]]                   +
           Get materialize.public.teachers       +
         ArrangeBy keys=[[#1], [#2]]             +
           Get materialize.public.sections       +
         ArrangeBy keys=[[#0]]                   +
           Filter (#0) IS NOT NULL               +
             Get materialize.public.courses      +
 Source materialize.public.courses               +
   filter=((#0) IS NOT NULL)                     +
 Used Indexes:                                   +
   - materialize.public.pk_teachers              +
   - materialize.public.sections_fk_teachers     +
   - materialize.public.sections_fk_courses      +
   - materialize.public.pk_courses               +

Further Optimize with Late Materialization

Materialize can further optimize memory usage when joining collections with primary and foreign key constraints using a pattern known as late materialization.

To understand late materialization, you need to know about primary and foreign keys. In our example, the teachers.id column uniquely identifies all teachers. When a column or set of columns uniquely identifies each record, it is called a primary key. We also have sections.teacher_id, which is not the primary key of sections, but it does correspond to the primary key of teachers. Whenever we have a column that is a primary key of another collection, it is called a foreign key.

In many relational databases, indexes don’t replicate the entire collection of data. Rather, they maintain just a mapping from the indexed columns back to a primary key. These few columns can take substantially less space than the whole collection, and may also change less as various unrelated attributes are updated. This is called late materialization, and it is possible to achieve in Materialize as well. Here are the steps to implementing late materialization along with examples.

  1. Create indexes on the primary key column(s) for your input collections.

    CREATE INDEX pk_teachers ON teachers (id);
    CREATE INDEX pk_sections ON sections (id);
    CREATE INDEX pk_courses ON courses (id);
  2. For each foreign key in the join, create a “narrow” view with just two columns: foreign key and primary key. Then create two indexes: one for the foreign key and one for the primary key. In our example, the two foreign keys are sections.teacher_id and sections.course_id, so we do the following:

    -- Create a "narrow" view containing primary key sections.id
    -- and foreign key sections.teacher_id
    CREATE VIEW sections_narrow_teachers AS SELECT id, teacher_id FROM sections;
    -- Create indexes on those columns
    CREATE INDEX sections_narrow_teachers_0 ON sections_narrow_teachers (id);
    CREATE INDEX sections_narrow_teachers_1 ON sections_narrow_teachers (teacher_id);
    -- Create a "narrow" view containing primary key sections.id
    -- and foreign key sections.course_id
    CREATE VIEW sections_narrow_courses AS SELECT id, course_id FROM sections;
    -- Create indexes on those columns
    CREATE INDEX sections_narrow_courses_0 ON sections_narrow_courses (id);
    CREATE INDEX sections_narrow_courses_1 ON sections_narrow_courses (course_id);
    NOTE: In this case, because both foreign keys are in sections, we could have gotten away with one narrow collection sections_narrow_teachers_and_courses with indexes on id, teacher_id, and course_id. In general, we won’t be so lucky to have all the foreign keys in the same collection, so we’ve shown the more general pattern of creating a narrow view and two indexes for each foreign key.
  3. Rewrite your query to use your narrow collections in the join conditions. Example:

      t.name AS teacher_name,
      c.name AS course_name
    FROM sections_narrow_teachers s_t
    INNER JOIN sections s ON s_t.id = s.id
    INNER JOIN teachers t ON s_t.teacher_id = t.id
    INNER JOIN sections_narrow_courses s_c ON s_c.id = s.id
    INNER JOIN courses c ON s_c.course_id = c.id;


Create a default index when there is no particular WHERE or JOIN clause that would fit the above cases. This can still speed up your query by reading the input from memory.

Clause Index

Learn more

Check out the blog post Delta Joins and Late Materialization to go deeper on join optimization in Materialize.

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