The mechanisms that maintain materialized views for Materialize dataflows are called arrangements. Understanding arrangements better can help you make decisions that will reduce memory usage while maintaining performance.
Before we talk about the arrangements that maintain materialized views, let’s review what materialized views are, how they work in traditional databases, and how they work in Materialize.
A view is simply a query saved under a name for convenience; the query is executed each time the view is referenced, without any savings in performance or speed. But some databases also support something more powerful: materialized views, which save the results of the query for quicker access.
Traditional databases typically only have limited support for materialized views in two ways: first, the updates to the views generally occur at set intervals, so views are not updated in real time, and second, only a limited subset of SQL syntax is supported. In cases where a traditional database does support refreshes for each data update, it tends to be very slow. These limitations stem from limited support for incremental updates; most databases are not designed to maintain long-running incremental queries, but instead are optimized for queries that are executed once and then wound down. This means that when the data changes, the materialized view must be recomputed from scratch in all but a few simple cases.
Our raison d’être at Materialize is to manage materialized views better than this. Materialize stores materialized views in memory to make them faster to access, and incrementally updates the views as the data changes to maintain correctness.
Materialize can make incremental updates efficiently because it’s built on an incremental data-parallel compute engine, Differential Dataflow, which in turn is built on a distributed processing framework called Timely Dataflow.
When you create a materialized view and issue a query, Materialize creates a dataflow. A dataflow consists of instructions on how to respond to data input and to changes to that data. Once executed, the dataflow computes and stores the result of the SQL query in memory, polls the source for updates, and then incrementally updates the query results when new data arrives.
Materialize dataflows act on collections of data, multisets that store each event in an update stream as a triple of
(data, time, diff).
|data||The record update.|
|time||The logical timestamp of the update.|
|diff||The change in the number of copies of the record (typically
A collection provides a data stream of updates as they happen. To provide fast access to the changes to individual records, the collection can be represented in an alternate form, indexed on
data to present the sequence of changes (
time, diff) the collection has undergone. This indexed representation is called an arrangement.
Materialize builds and maintains indexes on both the input and output collections as well as for many intermediate collections created when processing a query. Because queries can overlap, Materialize might need to build the exact same indexes for multiple queries. Instead of performing redundant work, Materialize builds the index once and maintains it in memory, sharing the required resources across all queries that use the indexed data. The index is then effectively a sunk cost, and the cost of each query is determined only by the new work it introduces.
You can find a more detailed analysis of the arrangements built for different types of queries in our blog post on Joins in Materialize.
The size of an arrangement, or amount of memory it requires, is roughly proportional to its number of distinct
(data, time) pairs, which can be small even if the number of records is large. As an illustration, consider a histogram of taxi rides grouped by the number of riders and the fare amount. The number of distinct
(rider, fare) pairs will be much smaller than the number of total rides that take place.
The amount of memory that the arrangement requires is then further reduced by background compaction of historical data.
Materialize provides various tools that allow you to analyze arrangements, although they are post hoc tools best used for debugging, rather than planning tools to be used before creating indexes or views. See Diagnosing Using SQL, Monitoring, and
EXPLAIN for more details.
Reducing memory usage
Creating indexes manually
When creating an arrangement for a join where the key is not clear, Materialize attempts to choose a key that will ensure that data is well distributed. If there is a primary key, that will be used; if there are source fields not required by the query, they are not included. Often Materialize can pull primary key info from a Confluent schema.
If Materialize cannot detect a primary key, the default key is the full set of columns, in order to ensure good data distribution. Creating an unmaterialized view and then specifying a custom index makes the key smaller.
For more examples of cases where you might want to create an index manually, see Joins in Materialize.
Casting the data type
Currently, Materialize handles implicit casts in a very memory-intensive way. Until this issue is resolved, you can reduce memory usage by building an index on the view with the type changed for any queries which include implicit casts, for example, when you combine 32-bit and 64-bit numbers.