Materialize lets you ask questions of your live data, which it answers and then maintains for you as your data continue to change. The moment you need a refreshed answer, you can get it in milliseconds. Materialize is designed to help you interactively explore your streaming data, perform data warehousing analytics against live relational data, or just increase the freshness and reduce the load of your dashboard and monitoring tasks.
Materialize focuses on providing correct and consistent answers with minimal latency. It 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 SQL92 queries as dataflows, which can react efficiently to changes in your data as they happen. Materialize is powered by timely dataflow, which connects the times at which your inputs change with the times of answers reported back to you.
We support a large fraction of PostgreSQL, and are actively working on supporting more builtin PostgreSQL functions.
Example:
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;
-- Materialized views are maintained automatically, and can depend on non-materialized views.
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
https://github.com/MaterializeInc/materialize
