Draft: ✨ add a first working draft of auto_aggregate selection

README.md

@@ -25,6 +25,8 @@ to just include the views that you're interested in.
 `migrate.sql` ([documentation](docs/migrate.md))
 : Incrementally copy existing time-series data from large tables into a new hypertable.
 
+- [`ts_autoagg`](/docs/auto_aggregate_selection.md): Helper function to downsample timeseries data from an automatically selected continuous aggregate table.
+
 ## Documentation and Help
 
 - [Why use TimescaleDB?](https://tsdb.co/GitHubTimescaleIntro)

docs/auto_aggregate_selection.md

@@ -0,0 +1,100 @@
+# Auto Aggregate Selection
+
+The file [utils/aggregate_selector.sql](/utils/aggregate_selector.sql) implements an automatic continuous aggregate selection system for TimescaleDB databases, which is meant to optimize the plotting of time series data in tools such as Grafana, by easing and automating the use of continuous aggregates.
+
+Its general usage as well as a few examples are documented here.
+
+## Quickstart:
+
+Execute the [utils/aggregate_selector.sql](/utils/aggregate_selector.sql) file by logging in to the database via `psql` and then running `\i aggregate_selector.sql`. *Always* verify the contents of the file when doing so. I encourage you to read through the SQL source, as it's not very long!
+
+Create an arbitrary number of continuous aggregates on top of your existing hypertables. Make sure to name their columns reasonably, preferably with the type of downsampling in the column name, such as `value_avg`. Exact specifics don't matter, as long as all columns of the same name hold the same type of data.
+
+The aggregation function is defined as follows:
+```SQL
+ts_autoagg(hypertable TEXT, 
+	data_interval INTERVAL,
+	aggregate_choices JSONB, 
+	groupby_clause TEXT,
+	filter_query TEXT,
+	hypertable_schema TEXT DEFAULT 'public',
+	time_column TEXT DEFAULT 'time')
+```
+
+And a fairly standard aggregation selection looks as follows:
+```SQL
+SELECT *
+FROM ts_autoagg( -- Root hypertable name
+				'cpu', 
+				-- Requested downsampling interval
+				INTERVAL '10m',
+				-- JSONB Array of Objects. Each object represents one downsampling option, and
+				-- should at least have a "with_columns" field and an "aggregate" field. May
+				-- also have a "priority" field.
+				$aggregate_options$
+					[
+						{"with_columns": ["values"],      "aggregate":"avg(value) AS value"},
+						{"with_columns": ["value_avg"],   "aggregate":"avg(value_avg) AS value"},
+						{"with_columns": ["value_stats"], "aggregate":"average(rollup(value_stats))", "priority":10}
+					]
+				$aggregate_options$,
+				-- List of fields to use as GROUP BY keys. This string is inserted into the SELECT
+				-- clause as well as after the GROUP BY time_bucket() key, so it *can not be empty*.
+				'tags', 
+				-- Additional JOIN and WHERE clause(s). This string will be inserted into the query
+				-- after the FROM statement. It *really should* include a time-column constraint to
+				-- let TimescaleDB do chunk exclusion here.
+				$where_clause$
+								WHERE time BETWEEN NOW()-INTERVAL'30d' AND NOW()-INTERVAL'10d'
+									AND tags@>'{"metric":"usage_idle","cpu":"cpu-total"}'
+				$where_clause$)
+				-- The user MUST define the return columns of this function, it's a PostgreSQL quirk.
+				AS (time TIMESTAMP, tags JSONB, value DOUBLE PRECISION);
+```
+
+## Detailed explanation
+
+The aggregate selection and auto-downsampling is performed via the "ts_autoagg" function. 
+The function will, internally, search through all Continuous Aggregates built on top of the given root hypertable. It will then select one aggregate (or the hypertable itself) that matches the given input values for downsampling_interval and aggregation options. The data is then filtered with the `filter_query` parameter, and a `GROUP BY` is performed using both `time_bucket(<time_column>, <data_interval>) AS time` and the list of `groupby_clause`s. The result of this query is then returned.
+
+The function takes in the following arguments:
+
+```SQL
+ts_autoagg(hypertable TEXT, 
+	data_interval INTERVAL,
+	aggregate_choices JSONB, 
+	groupby_clause TEXT,
+	filter_query TEXT,
+	hypertable_schema TEXT DEFAULT 'public',
+	time_column TEXT DEFAULT 'time')
+```
+
+The function will try and find a matching hypertable as follows:
+- It first looks up all known Continuous Aggregates built on the root hypertable, and fetches their
+intervals as well as available columns.
+- It then looks for the largest bucketing interval aggregate table whose bucket size is smaller or equal to
+the requested data, AND which has at least one combination of columns matching a `with_colums` clause from one of the `aggregate_choices` entries.
+The root hypertable is assumed to have an interval of "0", i.e. no downsampling.
+- If no matching table has been found, it will look for the next-largest table with a valid column combination.
+This only happens when the supplied aggregation options do not include a valid combination for the root hypertable,
+and may be used to e.g. force the downsampling to always run off of a continuous aggregate.
+- It will then return the result of a query constructed from the given parameters. If no table was found, it will return an empty table.
+
+
+Please note that it is HIGHLY advised to place the time filtering query inside the query filter argument.
+If it is applied outside the function, the TimescaleDB planner will NOT be able to perform chunk exclusion
+properly, which will lead to major query slowdowns.
+
+### aggregate_choices structure
+
+The aggregate selection JSONB structure is as follows:
+It is an Array of Objects. Each array element represents one possible downsampling method, and one candidate
+will be chosen based on given parameters.
+Each array element can have the following key/value pairs:
+- "with_columns": *Must* be set. Must be an array of strings, each string representing a column that must be
+present in a given table in order for the downsampling method to be deemed compatible with said table.
+- "aggregate": *Must* be set. Must be a single string, defining the clause, or clauses, used in the SELECT
+statement to downsample data. Please note that the function's return type is statically determined by the
+user at call-time, and as such all aggregates must return the same type.
+- "priority": May optionally be set to an integer. Higher numbers mean this query will be preferentially
+selected in case multiple aggregate options are valid.

utils/aggregate_selector.sql

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+-- AUTO AGGREGATE SELECTION
+-- Created by: David Bailey, Jun. 2023
+-- Twitter: @XasinTheSystem
+
+
+-- This file implements the functionality of an automatic continuous aggregate selection system
+-- for TimescaleDB databases, as well as a few needed helper functions needed to perform this.
+--
+-- The aggregate selection and auto-downsampling is performed via the "ts_autoagg" function. This function takes
+-- in a root hypertable name, a downsampling interval that is used for time_bucket grouping, a JSONB structure
+-- defining available downsampling methods, a set of columns used for a GROUP BY clause alongside the time_bucket,
+-- and a string defining further JOIN and WHERE clauses needed in the query.
+--
+-- Please note that it is HIGHLY advised to place the time filtering query inside the query filter argument.
+-- If it is applied outside the function, the TimescaleDB planner will NOT be able to perform chunk exclusion
+-- properly, which will lead to major query slowdowns.
+--
+-- The aggregate selection JSONB structure is as follows:
+-- It is an Array of Objects. Each array element represents one possible downsampling method, and one candidate
+-- will be chosen based on given parameters.
+-- Each array element can have the following key/value pairs:
+--  - "with_columns": *Must* be set. Must be an array of strings, each string representing a column that must be
+--    present in a given table in order for the downsampling method to be deemed compatible with said table.
+--  - "aggregate": *Must* be set. Must be a single string, defining the clause, or clauses, used in the SELECT
+--    statement to downsample data. Please note that the function's return type is statically determined by the
+--    user at call-time, and as such all aggregates must return the same type.
+--  - "priority": May optionally be set to an integer. Higher numbers mean this query will be preferentially
+--    selected in case multiple aggregate options are valid.
+--
+-- The function will try and find a matching hypertable as follows:
+-- - It first looks up all known Continuous Aggregates built on the root hypertable, and fetches their
+--   intervals as well as available columns.
+-- - It then looks for the largest bucketing interval aggregate table whose bucket size is smaller or equal to
+--   the requested data, AND which has at least one combination of columns matching a "with_colums" clause.
+--   The root hypertable is assumed to have an interval of "0", i.e. no downsampling.
+-- - If no matching table has been found, it will look for the next-largest table with a valid column combination.
+--   This only happens when the supplied aggregation options do not include a valid combination for the root hypertable,
+--   and may be used to e.g. force the downsampling to always run off of a continuous aggregate.
+-- - It will then return the result of a query constructed from the given parameters. If no Table was found, it will
+--   return an empty table.
+--
+---- EXAMPLE QUERY:
+-- SELECT *
+-- FROM ts_autoagg('cpu', INTERVAL '10m',
+-- -- JSONB list of aggregate options, made of "with_colum" "aggregate" pairs.
+-- $aggs$
+--	[
+--		{"with_columns":["values"], "aggregate":"avg(value) AS value"},
+--		{"with_columns":["value_avg"], "aggregate":"avg(value_avg) AS value"},
+--		{"with_columns":["value_stats"], "aggregate":"average(rollup(value_stats))"}
+--	]
+-- $aggs$,
+-- 'tags', -- List of fields or other calculations used in the GROUP BY clause. Must be included in return type list.
+-- -- Additional filter clauses. MUST include a filter on the time column.
+-- $$
+--   WHERE time BETWEEN NOW()-INTERVAL'30d' AND NOW()-INTERVAL'10d'
+--   AND tags@>'{"metric":"usage_idle","cpu":"cpu-total"}'
+-- $$)
+-- -- This function needs to have a user-assigned  return type due to the RETURNS SETOF RECORD type.
+-- AS (time TIMESTAMP, tags JSONB, value DOUBLE PRECISION);
+
+
+-- Recursively go through all known VIEWs for a given root table, and return the set of all VIEWs originating
+-- from the given table name.
+--
+-- Credit to Chris Engelbert for suggesting the recursive CTE query!
+CREATE OR REPLACE FUNCTION get_hypertable_caggs(target_table_name TEXT, target_table_schema TEXT DEFAULT 'public')
+RETURNS TABLE (table_name TEXT, table_schema TEXT)
+LANGUAGE SQL
+STABLE
+AS $BODY$
+WITH RECURSIVE cagg_tables as (
+    select vcu1.view_schema, vcu1.view_name
+    from information_schema.view_column_usage vcu1
+    where vcu1.table_schema = target_table_schema
+      and vcu1.table_name = target_table_name
+
+    union all
+
+	select vcu2.view_schema, vcu2.view_name
+    from information_schema.view_column_usage vcu2, cagg_tables t
+    where t.view_schema = vcu2.table_schema
+      and t.view_name = vcu2.table_name
+)
+SELECT DISTINCT view_name AS table_name,
+	view_schema AS table_schema	
+	FROM cagg_tables
+	WHERE view_schema != '_timescaledb_internal'
+$BODY$;
+
+-- For a single given aggregate table, return the bucket_width as Interval
+CREATE OR REPLACE FUNCTION get_cagg_interval(table_name TEXT, table_schema TEXT DEFAULT 'public')
+RETURNS INTERVAL
+LANGUAGE SQL
+STABLE
+AS $BODY$
+	SELECT bucket_width * INTERVAL '1us'
+	FROM _timescaledb_catalog.continuous_agg 
+	WHERE table_schema = user_view_schema AND table_name = user_view_name
+	LIMIT 1
+$BODY$;
+
+-- For a single given table, return a JSONB array containing the list of table columns.
+-- (FYI, JSONB over TEXT[] to be able to easily use the JSONB array includes subset operator later on)
+CREATE OR REPLACE FUNCTION get_jsonb_table_columns(in_table_name TEXT, in_table_schema TEXT DEFAULT 'public')
+RETURNS JSONB
+LANGUAGE SQL
+STABLE
+AS $BODY$
+	SELECT jsonb_agg(column_name)
+	FROM information_schema.columns
+	WHERE table_name = in_table_name AND table_schema = in_table_schema
+$BODY$;
+
+-- Perform the aggregate choice step. Returns a JSONB.
+-- Given the name of a root hypertable, the wanted downsampling interval, and a list of aggregate choices,
+-- this function will pick the largest-interval available compatible hypertable or continuous aggregate
+-- compatible with the downsampling interval and aggregate "with_column" constraints.
+--
+-- The returned JSONB will be the chosen aggregate object joined with extra metadata for the chosen
+-- table_schema, table_name and table_interval.
+CREATE OR REPLACE FUNCTION aggregate_choice(hypertable TEXT, selection_interval INTERVAL, 
+	aggregate_types JSONB, hypertable_schema TEXT DEFAULT 'public')
+RETURNS JSONB
+LANGUAGE SQL
+STABLE
+AS $BODY$
+	WITH available_tables AS (
+		SELECT table_name, table_schema, get_cagg_interval(table_name, table_schema) AS table_interval, get_jsonb_table_columns(table_name, table_schema) AS table_columns
+		FROM get_hypertable_caggs(hypertable, hypertable_schema)
+
+		UNION ALL
+
+		SELECT hypertable AS table_name, hypertable_schema AS table_schema, INTERVAL '0s' AS table_interval, get_jsonb_table_columns(hypertable, hypertable_schema) AS table_columns
+	), available_aggregates AS (
+		SELECT *
+		FROM jsonb_array_elements(aggregate_types) AS j (aggregate_option)
+	)
+	SELECT jsonb_build_object('table_name', table_name,
+		'table_schema', table_schema,
+		'table_interval', table_interval,
+		'interval_matched', table_interval = selection_interval) || aggregate_option
+	FROM available_tables, available_aggregates, (VALUES(1),(-1)) AS swp(interval_swap)
+	WHERE table_columns@>(aggregate_option->'with_columns') AND (table_interval*interval_swap) <= selection_interval
+	ORDER BY (table_interval*interval_swap) DESC, coalesce((aggregate_option->>'priority')::int) DESC
+	LIMIT 1
+$BODY$;
+
+
+-- This function will perform automatic downsampling of data
+-- from a given hypertable OR compatible continuous aggregate of the table.
+-- See top of the file for more extensive documentation.
+--
+-- The parameters are as follows:
+-- - hypertable: Name of the root hypertable to use.
+-- - data_interval: Interval that will be downsampled to using time_bucket
+-- - aggregate_choices: JSONB array of Objects of configurations for possible downsampling methods
+-- - groupby_clause: Clauses that will be inserted to the SELECT and GROUP BY query. Note that
+--   this may not be left empty. Instead, a dummy "true" or "0" can be inserted to ignore grouping.
+-- - filter_query: A clause inserted to the SELECT query after the FROM clause. Used to perform
+--   optional JOINs as well as the WHERE clause. MUST include a WHERE filter clause for the time column!
+-- - hypertable_schema: Schema of the hypertable, default 'public'
+-- - time_column: Name of the time column, default 'time'
+CREATE OR REPLACE FUNCTION ts_autoagg(hypertable TEXT, data_interval INTERVAL,
+	aggregate_choices JSONB, 
+	groupby_clause TEXT, filter_query TEXT,
+	hypertable_schema TEXT DEFAULT 'public',
+	time_column TEXT DEFAULT 'time')
+RETURNS SETOF RECORD
+LANGUAGE plpgsql
+STABLE
+AS $BODY$
+DECLARE
+	selected_parameters jsonb;
+
+	aggregator_column TEXT;
+BEGIN
+	SELECT aggregate_choice(hypertable, data_interval, aggregate_choices, hypertable_schema) INTO selected_parameters;
+
+	IF NOT FOUND THEN
+		RAISE EXCEPTION 'No fitting hypertable or aggregate found for given columns and table %!', hypertable;
+		RETURN;
+	END IF;
+
+	RAISE NOTICE 'Using parameter set %', selected_parameters;
+
+	aggregator_column := selected_parameters->>'aggregate';
+
+	IF aggregator_column IS NULL THEN
+		RAISE EXCEPTION 'No aggregator given!' USING HINT = 'Supply a "aggregate" field in the JSON aggregate object'
+		RETURN;
+	END IF;
+
+	RETURN QUERY EXECUTE format($qry$
+		SELECT time_bucket($1, %I) AS time, %s, %s
+		FROM %I.%I
+		%s
+		GROUP BY 1, %s
+		ORDER BY 1
+	$qry$, time_column, groupby_clause, aggregator_column,
+		selected_parameters->>'table_schema', selected_parameters->>'table_name',
+		filter_query,
+		groupby_clause) USING data_interval;
+END;
+$BODY$;
+
+
+