Materialize is a cloud-native data warehouse purpose-built for operational workloads where an analytical data warehouse would be too slow, and a stream processor would be too complicated.
Using SQL and common tools in the wider data ecosystem, Materialize allows you to build real-time automation, engaging customer experiences, and interactive data products that drive value for your business while reducing the cost of data freshness.
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Materialize is designed to help you interactively explore your streaming data, perform analytics against live relational data, or increase data freshness while reducing the load of your dashboard and monitoring tasks. The moment you need a refreshed answer, you can get it in milliseconds.
It focuses on providing correct and consistent answers with minimal latency, and does not ask you to accept either approximate answers or eventual consistency. Whenever Materialize answers a query, that answer is the correct result on some specific (and recent) version of your data. Materialize does all of this by recasting your SQL queries as dataflows, which can react efficiently to changes in your data as they happen.
Our fully managed service is cloud native, featuring high availability, via multi-active replication, horizontal scalability, by seamlessly scaling dataflows across multiple machines, and near infinite storage, by leveraging cloud object storage (e.g., Amazon S3).
We support a large fraction of PostgreSQL, and are actively working on supporting more built-in PostgreSQL functions. Please file an issue if something doesn't work as expected!
Materialize can read data from Kafka (and other Kafka API-compatible systems like Redpanda), directly from a PostgreSQL or MySQL replication stream, or from SaaS applications via webhooks. It also supports regular database tables to which you can insert, update, and delete rows.
Once you've got the data in, define views and perform reads via the PostgreSQL protocol. Use your favorite SQL client, including the psql you probably already have on your system.
Materialize supports a comprehensive variety of SQL features, all using the PostgreSQL dialect and protocol:
- Joins, joins, joins! Materialize supports multi-column join conditions, multi-way joins, self-joins, cross-joins, inner joins, outer joins, etc.
- Delta-joins avoid intermediate state blowup compared to systems that can only plan nested binary joins - tested on joins of up to 64 relations.
- Support for subqueries. Materialize's SQL optimizer performs subquery decorrelation out-of-the-box, avoiding the need to manually rewrite subqueries into joins.
- Materialize can incrementally maintain views in the presence of arbitrary inserts, updates, and deletes. No asterisks.
- All the aggregations:
min,max,count,sum,stddev, etc. HAVINGORDER BYLIMITDISTINCT- JSON support in the PostgreSQL dialect including operators and functions like
->,->>,@>,?,jsonb_array_element,jsonb_each. Materialize automatically plans lateral joins for efficientjsonb_eachsupport. - Nest views on views on views!
- Multiple views that have overlapping subplans can share underlying indices for space and compute efficiency, so just declaratively define what you want, and we'll worry about how to efficiently maintain them.
Here's an example join query that works fine in Materialize, TPC-H query 15:
CREATE SOURCE tpch
FROM LOAD GENERATOR TPCH (SCALE FACTOR 1)
FOR ALL TABLES;
-- Views define commonly reused subqueries.
CREATE VIEW revenue (supplier_no, total_revenue) AS
SELECT
l_suppkey,
SUM(l_extendedprice * (1 - l_discount))
FROM
lineitem
WHERE
l_shipdate >= DATE '1996-01-01'
AND l_shipdate < DATE '1996-01-01' + INTERVAL '3' month
GROUP BY
l_suppkey;
-- The MATERIALIZED keyword is the trigger to begin
-- eagerly, consistently, and incrementally maintaining
-- results that are stored directly in durable storage.
CREATE MATERIALIZED VIEW tpch_q15 AS
SELECT
s_suppkey,
s_name,
s_address,
s_phone,
total_revenue
FROM
supplier,
revenue
WHERE
s_suppkey = supplier_no
AND total_revenue = (
SELECT
max(total_revenue)
FROM
revenue
)
ORDER BY
s_suppkey;
-- Creating an index keeps results always up to date and in memory.
-- In this example, the index will allow for fast point lookups of
-- individual supply keys.
CREATE INDEX tpch_q15_idx ON tpch_q15 (s_suppkey);Stream inserts, updates, and deletes on the underlying tables (lineitem and supplier), and Materialize keeps the materialized view incrementally updated. You can type SELECT * FROM tpch_q15 and expect to see the current results immediately!
Pull based: Use any PostgreSQL-compatible driver in any language/environment to make SELECT queries against your views. Tell them they're talking to a PostgreSQL database, they don't ever need to know otherwise.
Push based: Listen to changes directly using SUBSCRIBE or configure Materialize to stream results to a Kafka topic as soon as the views change.
Check out our documentation.
Materialize is provided primarily as a fully managed cloud service with credit-based pricing. Included in the price are proprietary cloud-native features like horizontal scalability, high availability, and a web management console.
However, we're big believers in advancing the frontier of human knowledge. To that end, the source code of the standalone database engine is publicly available, in this repository, and licensed under the BSL 1.1, converting to the open-source Apache 2.0 license after 4 years. As stated in the BSL, use of the standalone database engine on a single node is free forever. Please be warned that this deployment model is not suitable for production use and we cannot offer support for it.
Materialize depends upon many open source Rust crates. We maintain a list of these crates and their licenses, including links to their source repositories.
Materialize is primarily written in Rust.
Developers can find docs at doc/developer, and Rust API documentation is hosted at https://dev.materialize.com/api/rust/. The Materialize development roadmap is divided up into roughly month-long milestones, and managed in GitHub.
Contributions are welcome. Prospective code contributors might find the D-good for external contributors label useful. See CONTRIBUTING.md for additional guidance.
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