This documentation is for an unreleased version of Apache Flink. We recommend you use the latest stable version.
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
the versioning time attribute.
TemporalTableFunction rates = tEnv .from("currency_rates") .createTemporalTableFunction("update_time", "currency"); tEnv.registerFunction("rates", rates);
rates = tEnv .from("currency_rates") .createTemporalTableFunction("update_time", "currency") tEnv.registerFunction("rates", rates)
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.
SELECT SUM(amount * rate) AS amount FROM orders, LATERAL TABLE (rates(order_time)) WHERE rates.currency = orders.currency
Table result = orders .joinLateral($("rates(order_time)"), $("orders.currency = rates.currency")) .select($("(o_amount * r_rate).sum as amount"));
val result = orders .joinLateral($"rates(order_time)", $"orders.currency = rates.currency") .select($"(o_amount * r_rate).sum as amount"))
Still not supported in Python API.