mirror of
https://github.com/timescale/timescaledb.git
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82ab09d8fb
* cagg_watermark_concurrent_update is very dependent on the chunk numbers, and should be ran first. * telemetry_stats should do VACUUM and REINDEX before getting the statistics, to avoid dependency on how the index was build * cagg_migrate_function is missing some orderbys
370 lines
13 KiB
MySQL
370 lines
13 KiB
MySQL
-- This file and its contents are licensed under the Timescale License.
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-- Please see the included NOTICE for copyright information and
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-- LICENSE-TIMESCALE for a copy of the license.
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-- Run test as non-superuser
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\c :TEST_DBNAME :ROLE_DEFAULT_PERM_USER
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-- Prepare a hypertable with date values
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CREATE TABLE temperaturedate (
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time date NOT NULL,
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sensor int,
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value float
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);
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SELECT create_hypertable('temperaturedate', 'time');
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INSERT INTO temperaturedate
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SELECT time, sensor_id, sensor_id * sensor_id + 100
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FROM generate_series('2010-01-01 0:00:00+00'::timestamptz, '2010-01-01 23:59:59+00','1m') g1(time),
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generate_series(1, 2, 1) AS g2(sensor_id);
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-- Prepare a hypertable with timestamp values
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CREATE TABLE temperature (
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time timestamp NOT NULL,
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sensor int,
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value float
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);
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SELECT create_hypertable('temperature', 'time');
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INSERT INTO temperature
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SELECT time, sensor_id, sensor_id * sensor_id + 100
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FROM generate_series('2010-01-01 0:00:00+00'::timestamptz, '2010-01-01 23:59:59+00','1m') g1(time),
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generate_series(1, 2, 1) AS g2(sensor_id);
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-- Prepare a hypertable with timestamptz values
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CREATE TABLE temperaturetz (
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time timestamptz NOT NULL,
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sensor int,
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value float
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);
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SELECT create_hypertable('temperaturetz', 'time');
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INSERT INTO temperaturetz
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SELECT time, sensor_id, sensor_id * sensor_id + 100
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FROM generate_series('2010-01-01 0:00:00+0'::timestamptz, '2010-01-01 23:59:59+0','1m') g1(time),
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generate_series(1, 2, 1) AS g2(sensor_id);
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SET timescaledb.debug_allow_cagg_with_deprecated_funcs = true;
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--- Tests with date
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--
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-- Tested function signatures
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--
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-- timescaledb_experimental | time_bucket_ng | date | bucket_width interval, ts date
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-- timescaledb_experimental | time_bucket_ng | date | bucket_width interval, ts date, origin date
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---
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CREATE MATERIALIZED VIEW cagg_tempdate_ng
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WITH (timescaledb.continuous) AS
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SELECT timescaledb_experimental.time_bucket_ng('7 days', time) as time, sensor, avg(value) AS avg
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FROM temperaturedate
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GROUP BY 1,2
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WITH NO DATA;
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-- Run the migration
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\set CAGG_NAME 'cagg_tempdate_ng'
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\ir include/cagg_run_timebucket_migration.sql
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CREATE MATERIALIZED VIEW cagg_tempdate_ng_origin
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WITH (timescaledb.continuous) AS
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SELECT timescaledb_experimental.time_bucket_ng('7 days', time, '2020-01-04') as time, sensor, avg(value) AS avg
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FROM temperaturedate
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GROUP BY 1,2
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WITH NO DATA;
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-- Run the migration
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\set CAGG_NAME 'cagg_tempdate_ng_origin'
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\ir include/cagg_run_timebucket_migration.sql
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--- Tests with timestamp
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--
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-- Tested function signatures
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-- timescaledb_experimental | time_bucket_ng | timestamp without time zone | bucket_width interval, ts timestamp without time zone
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-- timescaledb_experimental | time_bucket_ng | timestamp without time zone | bucket_width interval, ts timestamp without time zone, origin timestamp without time zone
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---
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CREATE MATERIALIZED VIEW cagg_temp_ng
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WITH (timescaledb.continuous) AS
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SELECT timescaledb_experimental.time_bucket_ng('5 minutes', time) as time, sensor, avg(value) AS avg
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FROM temperature
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GROUP BY 1,2
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WITH NO DATA;
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-- Run the migration
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\set CAGG_NAME 'cagg_temp_ng'
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\ir include/cagg_run_timebucket_migration.sql
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CREATE MATERIALIZED VIEW cagg_temp_ng_origin
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WITH (timescaledb.continuous) AS
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SELECT timescaledb_experimental.time_bucket_ng('5 minutes', time, '2020-01-01 04:00:00') as time, sensor, avg(value) AS avg
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FROM temperature
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GROUP BY 1,2
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WITH NO DATA;
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-- Run the migration
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\set CAGG_NAME 'cagg_temp_ng_origin'
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\ir include/cagg_run_timebucket_migration.sql
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--- Tests with timestamptz
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--
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-- Not tested signatures since they are not supported by CAggs
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-- timescaledb_experimental | time_bucket_ng | timestamp with time zone | bucket_width interval, ts timestamp with time zone
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-- timescaledb_experimental | time_bucket_ng | timestamp with time zone | bucket_width interval, ts timestamp with time zone, origin timestamp with time zone
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--
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-- Tested function signatures
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-- timescaledb_experimental | time_bucket_ng | timestamp with time zone | bucket_width interval, ts timestamp with time zone, origin timestamp with time zone, timezone text
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-- timescaledb_experimental | time_bucket_ng | timestamp with time zone | bucket_width interval, ts timestamp with time zone, timezone text
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---
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-- Create a CAgg using time_bucket_ng
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CREATE MATERIALIZED VIEW cagg_temptz_ng
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WITH (timescaledb.continuous) AS
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SELECT timescaledb_experimental.time_bucket_ng('5 minutes', time, 'Europe/Berlin') as time, sensor, avg(value) AS avg
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FROM temperaturetz
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GROUP BY 1,2
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WITH NO DATA;
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-- Run the migration
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\set CAGG_NAME 'cagg_temptz_ng'
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\ir include/cagg_run_timebucket_migration.sql
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CREATE MATERIALIZED VIEW cagg_temptz_ng_origin
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WITH (timescaledb.continuous) AS
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SELECT timescaledb_experimental.time_bucket_ng('5 minutes', time, '2020-04-01'::timestamptz, 'Europe/Berlin') as time, sensor, avg(value) AS avg
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FROM temperaturetz
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GROUP BY 1,2
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WITH NO DATA;
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-- Run the migration - special case, order of origin and timezone needs to be swapped
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\set CAGG_NAME 'cagg_temptz_ng_origin'
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\ir include/cagg_run_timebucket_migration.sql
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-- Test real-time CAggs as well
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-- Create a real-time CAgg using time_bucket_ng
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CREATE MATERIALIZED VIEW cagg_temptz_ng_rt
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WITH (timescaledb.continuous, timescaledb.materialized_only=false) AS
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SELECT timescaledb_experimental.time_bucket_ng('5 minutes', time, 'Europe/Berlin') as time, sensor, avg(value) AS avg
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FROM temperaturetz
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GROUP BY 1,2
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WITH NO DATA;
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\set CAGG_NAME 'cagg_temptz_ng_rt'
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\ir include/cagg_run_timebucket_migration.sql
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----
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-- Check bucket conversion -- timestamptz without custom origin
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----
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CREATE MATERIALIZED VIEW cagg_temp_ng_1week
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WITH (timescaledb.continuous) AS
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SELECT timescaledb_experimental.time_bucket_ng('1 week', time, 'Europe/Berlin') as time, sensor, avg(value) AS avg
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FROM temperaturetz
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GROUP BY 1,2
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WITH NO DATA;
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CALL refresh_continuous_aggregate('cagg_temp_ng_1week', NULL, NULL);
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-- Ensure time_bucket and time_bucket_ng create buckets with a different origin
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SELECT time_bucket('1 week', time, 'Europe/Berlin'), sensor, avg(value)
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FROM temperaturetz
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GROUP BY 1, 2
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ORDER BY 1, 2;
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SELECT timescaledb_experimental.time_bucket_ng('1 week', time, 'Europe/Berlin'), sensor, avg(value)
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FROM temperaturetz
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GROUP BY 1, 2
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ORDER BY 1, 2;
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-- Test what the CAgg returns
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SELECT * FROM cagg_temp_ng_1week;
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-- Insert new data into the hypertable. 10 years after the data that was used during the CAgg refresh
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INSERT INTO temperaturetz
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SELECT time, sensor_id, sensor_id * sensor_id + 100
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FROM generate_series('2020-01-01 0:00:00+0'::timestamptz, '2020-01-01 23:59:59+0','1m') g1(time),
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generate_series(1, 2, 1) AS g2(sensor_id);
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-- Migrate and update CAgg
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CALL _timescaledb_functions.cagg_migrate_to_time_bucket('cagg_temp_ng_1week');
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CALL refresh_continuous_aggregate('cagg_temp_ng_1week', NULL, NULL);
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-- Get output of the time_bucket_ng
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SELECT timescaledb_experimental.time_bucket_ng('1 week', time, 'Europe/Berlin'), sensor, avg(value)
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FROM temperaturetz
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GROUP BY 1, 2
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ORDER BY 1, 2;
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-- Ensure time function with added origin during migration produces the same results
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SELECT time_bucket('1 week', time, origin=>'2000-01-01 00:00:00+01'), sensor, avg(value)
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FROM temperaturetz
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GROUP BY 1, 2
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ORDER BY 1, 2;
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-- Even the CAgg now uses time_bucket, we should see buckets of the same alignment
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SELECT * FROM cagg_temp_ng_1week ORDER BY time, sensor;
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----
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-- Check bucket conversion -- timestamptz with custom origin
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----
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TRUNCATE temperaturetz;
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INSERT INTO temperaturetz
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SELECT time, sensor_id, sensor_id * sensor_id + 100
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FROM generate_series('2010-01-01 0:00:00+0'::timestamptz, '2010-01-01 23:59:59+0','1m') g1(time),
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generate_series(1, 2, 1) AS g2(sensor_id);
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CREATE MATERIALIZED VIEW cagg_temp_ng_1week_corgin
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WITH (timescaledb.continuous) AS
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SELECT timescaledb_experimental.time_bucket_ng('1 week', time, '2005-01-01 15:00:00'::timestamptz, 'Europe/Berlin') as time, sensor, avg(value) AS avg
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FROM temperaturetz
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GROUP BY 1,2
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WITH NO DATA;
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CALL refresh_continuous_aggregate('cagg_temp_ng_1week_corgin', NULL, NULL);
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SELECT * FROM cagg_temp_ng_1week_corgin;
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-- Insert new data into the hypertable. 10 years after the data that was used during the CAgg refresh
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INSERT INTO temperaturetz
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SELECT time, sensor_id, sensor_id * sensor_id + 100
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FROM generate_series('2020-01-01 0:00:00+0'::timestamptz, '2020-01-01 23:59:59+0','1m') g1(time),
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generate_series(1, 2, 1) AS g2(sensor_id);
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-- Migrate and update CAgg
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CALL _timescaledb_functions.cagg_migrate_to_time_bucket('cagg_temp_ng_1week_corgin');
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CALL refresh_continuous_aggregate('cagg_temp_ng_1week_corgin', NULL, NULL);
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-- Get output of the time_bucket_ng
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SELECT timescaledb_experimental.time_bucket_ng('1 week', time, '2005-01-01 15:00:00'::timestamptz, 'Europe/Berlin'), sensor, avg(value)
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FROM temperaturetz
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GROUP BY 1, 2
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ORDER BY 1, 2;
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-- Even the CAgg now uses time_bucket, we should see buckets of the same alignment
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SELECT * FROM cagg_temp_ng_1week_corgin ORDER BY time, sensor;
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----
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-- Check bucket conversion -- timestamp without custom origin
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----
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CREATE MATERIALIZED VIEW cagg_temp_ng_1week_timestamp
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WITH (timescaledb.continuous) AS
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SELECT timescaledb_experimental.time_bucket_ng('1 week', time) as time, sensor, avg(value) AS avg
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FROM temperature
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GROUP BY 1,2
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WITH NO DATA;
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CALL refresh_continuous_aggregate('cagg_temp_ng_1week_timestamp', NULL, NULL);
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-- Ensure time_bucket and time_bucket_ng create buckets with a different origin
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SELECT time_bucket('1 week', time), sensor, avg(value)
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FROM temperature
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GROUP BY 1, 2
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ORDER BY 1, 2;
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SELECT timescaledb_experimental.time_bucket_ng('1 week', time), sensor, avg(value)
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FROM temperature
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GROUP BY 1, 2
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ORDER BY 1, 2;
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-- Test what the CAgg returns
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SELECT * FROM cagg_temp_ng_1week_timestamp;
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-- Insert new data into the hypertable. 10 years after the data that was used during the CAgg refresh
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INSERT INTO temperature
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SELECT time, sensor_id, sensor_id * sensor_id + 100
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FROM generate_series('2020-01-01 0:00:00+0'::timestamptz, '2020-01-01 23:59:59+0','1m') g1(time),
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generate_series(1, 2, 1) AS g2(sensor_id);
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-- Migrate and update CAgg
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CALL _timescaledb_functions.cagg_migrate_to_time_bucket('cagg_temp_ng_1week_timestamp');
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CALL refresh_continuous_aggregate('cagg_temp_ng_1week_timestamp', NULL, NULL);
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-- Get output of the time_bucket_ng
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SELECT timescaledb_experimental.time_bucket_ng('1 week', time), sensor, avg(value)
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FROM temperature
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GROUP BY 1, 2
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ORDER BY 1, 2;
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-- Ensure time function with added origin during migration produces the same results
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SELECT time_bucket('1 week', time, origin=>'2000-01-01 00:00:00'), sensor, avg(value)
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FROM temperature
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GROUP BY 1, 2
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ORDER BY 1, 2;
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-- Even the CAgg now uses time_bucket, we should see buckets of the same alignment
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SELECT * FROM cagg_temp_ng_1week_timestamp ORDER BY time, sensor;
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----
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-- Check bucket conversion -- date without custom origin
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----
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CREATE MATERIALIZED VIEW cagg_temp_ng_1week_date
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WITH (timescaledb.continuous) AS
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SELECT timescaledb_experimental.time_bucket_ng('1 week', time) as time, sensor, avg(value) AS avg
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FROM temperaturedate
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GROUP BY 1,2
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WITH NO DATA;
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CALL refresh_continuous_aggregate('cagg_temp_ng_1week_date', NULL, NULL);
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-- Ensure time_bucket and time_bucket_ng create buckets with a different origin
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SELECT time_bucket('1 week', time), sensor, avg(value)
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FROM temperaturedate
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GROUP BY 1, 2
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ORDER BY 1, 2;
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SELECT timescaledb_experimental.time_bucket_ng('1 week', time), sensor, avg(value)
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FROM temperaturedate
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GROUP BY 1, 2
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ORDER BY 1, 2;
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-- Test what the CAgg returns
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SELECT * FROM cagg_temp_ng_1week_date;
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-- Insert new data into the hypertable. 10 years after the data that was used during the CAgg refresh
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INSERT INTO temperaturedate
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SELECT time, sensor_id, sensor_id * sensor_id + 100
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FROM generate_series('2020-01-01 0:00:00+0'::timestamptz, '2020-01-01 23:59:59+0','1m') g1(time),
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generate_series(1, 2, 1) AS g2(sensor_id);
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-- Migrate and update CAgg
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CALL _timescaledb_functions.cagg_migrate_to_time_bucket('cagg_temp_ng_1week_date');
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CALL refresh_continuous_aggregate('cagg_temp_ng_1week_date', NULL, NULL);
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-- Get output of the time_bucket_ng
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SELECT timescaledb_experimental.time_bucket_ng('1 week', time), sensor, avg(value)
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FROM temperaturedate
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GROUP BY 1, 2
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ORDER BY 1, 2;
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-- Ensure time function with added origin during migration produces the same results
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SELECT time_bucket('1 week', time, origin=>'2000-01-01 01:00:00+00'), sensor, avg(value)
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FROM temperaturedate
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GROUP BY 1, 2
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ORDER BY 1, 2;
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-- Even the CAgg now uses time_bucket, we should see buckets of the same alignment
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SELECT * FROM cagg_temp_ng_1week_date ORDER BY time, sensor;
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-- Ensure we error out when the CAgg does not use a deprecated function
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\set ON_ERROR_STOP 0
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CALL _timescaledb_functions.cagg_migrate_to_time_bucket('cagg_temp_ng_1week_date');
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\set ON_ERROR_STOP 1
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----
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-- Test migration as non-owner (should fail)
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----
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CREATE MATERIALIZED VIEW cagg_tempdate_ng_acl
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WITH (timescaledb.continuous) AS
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SELECT timescaledb_experimental.time_bucket_ng('7 days', time) as time, sensor, avg(value) AS avg
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FROM temperaturedate
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GROUP BY 1,2
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WITH NO DATA;
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\c :TEST_DBNAME :ROLE_DEFAULT_PERM_USER_2
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\set ON_ERROR_STOP 0
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CALL _timescaledb_functions.cagg_migrate_to_time_bucket('cagg_tempdate_ng_acl');
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\set ON_ERROR_STOP 1
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