Temporal Table Function
A Temporal table function provides access to the version of a temporal table at a specific point in time. In order to access the data in a temporal table, one must pass a time attribute that determines the version of the table that will be returned. Flink uses the SQL syntax of table functions to provide a way to express it.
Unlike a versioned table, temporal table functions can only be defined on top of append-only streams — it does not support changelog inputs. Additionally, a temporal table function cannot be defined in pure SQL DDL.
Defining a Temporal Table Function
Temporal table functions can be defined on top of append-only streams using the Table API. The table is registered with one or more key columns, and a time attribute used for versioning.
Suppose we have an append-only table of currency rates that we would like to register as a temporal table function.
SELECT * FROM currency_rates;
update_time currency rate
============= ========= ====
09:00:00 Yen 102
09:00:00 Euro 114
09:00:00 USD 1
11:15:00 Euro 119
11:49:00 Pounds 108
Using the Table API, we can register this stream using currency
for the key and update_time
as the versioning time attribute.
Java
TemporalTableFunction rates = tEnv
.from("currency_rates")
.createTemporalTableFunction("update_time", "currency");
tEnv.registerFunction("rates", rates);
Scala
rates = tEnv
.from("currency_rates")
.createTemporalTableFunction("update_time", "currency")
tEnv.registerFunction("rates", rates)
Python
Still not supported in Python Table API.
Temporal Table Function Join
Once defined, a temporal table function is used as a standard table function. Append-only tables (left input/probe side) can join with a temporal table (right input/build side), i.e., a table that changes over time and tracks its changes, to retrieve the value for a key as it was at a particular point in time.
Consider an append-only table orders
that tracks customers’ orders in different currencies.
SELECT * FROM orders;
order_time amount currency
========== ====== =========
10:15 2 Euro
10:30 1 USD
10:32 50 Yen
10:52 3 Euro
11:04 5 USD
Given these tables, we would like to convert orders to a common currency — USD.
SQL
SELECT
SUM(amount * rate) AS amount
FROM
orders,
LATERAL TABLE (rates(order_time))
WHERE
rates.currency = orders.currency
Java
Table result = orders
.joinLateral($("rates(order_time)"), $("orders.currency = rates.currency"))
.select($("(o_amount * r_rate).sum as amount"));
Scala
val result = orders
.joinLateral($"rates(order_time)", $"orders.currency = rates.currency")
.select($"(o_amount * r_rate).sum as amount"))
Python
Still not supported in Python API.