Materialize is a streaming database with a SQL API. However, despite the fact that Materialize uses SQL idioms and can process data from databases, it actually has very little in common with “databases” as most users think of them.
In this document, we’ll sketch a conceptual framework expressed by Materialize’s API components, which might help you develop a mental model of how to work with Materialize and how its components differ from traditional databases.
Materialize offers the following components through its SQL API:
|Sources||Sources represent streams (e.g. Kafka) or files that provide data to Materialize.|
|Views||Views represent queries of sources and other views that you want to save for repeated execution.|
|Indexes||Indexes represent query results stored in memory.|
|Sinks||Sinks represent output streams or files that Materialize sends data to.|
Sources represent a connection to the data you want Materialize to process, as well as details about the structure of that data. A simplistic way to think of this is that sources represent streams and their schemas; this isn’t entirely accurate, but provides an illustrative mental model.
In terms of SQL, sources are similar to a combination of both tables and clients.
- Like a table, sources are a structured component that users can read from.
- Like a client, sources are responsible for writing data. There is no
INSERTstatement in Materialize, which is correlated with the fact that external sources provide all of the underlying data to process.
By looking at what comprises a source, we can develop a sense for how this combination works.
Sources are composed of the following components:
|Connector||Provides actual bytes of data to Materialize||Kafka|
|Format||Structures of the external source’s bytes, i.e. its schema||Avro|
|Envelope||Expresses how Materialize should handle the incoming data + any additional formatting information||Append-only|
Materialize can connect to the following types of sources:
- Streaming sources like Kafka
- File sources like
.csvor unstructured log files
Materialize can decode incoming bytes of data from several formats:
- Plain text
- Raw bytes
What Materialize actually does with the data it receives depends on the “envelope” your data provides:
|Append-only||Inserts all received data; does not support updates or deletes.|
|Debezium||Treats data as wrapped in a “diff envelope” which indicates whether the record is an insertion, deletion, or update. The Debezium envelope is only supported by sources published to Kafka by Debezium.
For more information, see
|Upsert||Treats data as having a key and a value. New records with non-null value that have the same key as a preexisting record in the dataflow will replace the preexisting record. New records with null value that have the same key as preexisting record will cause the preexisting record to be deleted.
For more information, see
In SQL, views represent a query that you save with some given name. These are
used primarily as shorthand for some lengthy, complicated
Materialize uses the idiom of views similarly, but the implication of views is
Materialize offers the following types of views:
|Materialized views||Incrementally updated views whose results are maintained in memory|
|Non-materialized views||Similar to the traditional SQL view|
All views in Materialize are built by reading data from sources and other views.
Materialized views embed a query like a traditional SQL view, but—unlike a SQL view—compute and incrementally update the results of the embedded query. This lets users read from materialized views and receive fresh answers with incredibly low latencies.
Materialize accomplishes incremental updates by creating a set of persistent transformations—known as a “dataflow”—that represent the query’s output. As new data comes in from sources, it’s fed into materialized views that query the source. Materialize then incrementally updates the materialized view’s output by understanding what has changed in the data, based on the source’s envelope. Any changes to a view’s output is then propagated to materialized views that query it, and the process repeats.
When reading from a materialized view, Materialize simply returns the dataflow’s current result set.
You can find more information about how materialized views work in the Indexes section below.
Non-materialized views simply stores a verbatim query, and provides a shorthand for performing the query.
Unlike materialized views, non-materialized views do not store the results of their embedded queries. This means they take up very little memory, but also provide very little benefit in terms of reducing the latency and computation needed to answer queries.
If you plan on repeatedly reading from a view, we recommend using materialized views instead.
Indexes are the component that actually “materializes” a view by storing its results in memory, though more generally, indexes can simply store any subset of a query’s data.
Each materialized view contains at least one index, which both lets it maintain the result set as new data streams in, as well as provide low-latency reads.
If you were to add an index to a non-materialized view, it would become a materialized view, and would start incrementally updating its embedded query’s results.
Interaction with materialized views
As we just mentioned, each materialized view has at least one index that
maintains the embedded query’s result in memory; these are known as
“arrangements” within Materialize’s dataflows. In the simplest case, it’s the
last operator, and simply stores the query’s output in memory. In more complex
cases, arrangements let Materialize perform more sophisticated aggregations more
Creating additional indexes on materialized views lets you store some subset of a query’s data in memory using a different structure, which can be useful if you want to perform a join over a view’s data using non-primary keys (e.g. foreign keys).
Sinks are the inverse of sources and represent a connection to an external stream where Materialize outputs data. When a user defines a sink over a materialized view or source, Materialize automatically generates the required schema and writes down the stream of changes to that view or source. In effect, Materialize sinks act as change data capture (CDC) producers for the given source or view.
Currently, Materialize only supports sending sink data to Kafka or Avro OCFs, encoded in Avro with the Debezium diff envelope.