API reference

TimescaleDB API Reference

Command List (A-Z)

• add_dimension
• add_drop_chunks_policy
• add_reorder_policy
• add_compress_chunks_policy
• alter_job_schedule
• alter table (compression)
• alter view (continuous aggregate)
• attach_tablespace
• chunk_relation_size
• chunk_relation_size_pretty
• compress_chunk
• create_hypertable
• create index (transaction per chunk)
• create view (continuous aggregate)
• decompress_chunk
• detach_tablespace
• detach_tablespaces
• drop_chunks
• drop view (continuous aggregate)
• first
• get_telemetry_report
• histogram
• hypertable_approximate_row_count
• hypertable_relation_size
• hypertable_relation_size_pretty
• indexes_relation_size
• indexes_relation_size_pretty
• interpolate
• last
• locf
• move_chunk
• refresh materialized view (continuous aggregate)
• remove_compress_chunks_policy
• remove_drop_chunks_policy
• remove_reorder_policy
• reorder_chunk
• set_chunk_time_interval
• set_integer_now_func
• set_number_partitions
• show_chunks
• show_tablespaces
• time_bucket
• time_bucket_gapfill
• timescaledb_information.hypertable
• timescaledb_information.license
• timescaledb_information.compressed_chunk_stats
• timescaledb_information.compressed_hypertable_stats
• timescaledb_information.continuous_aggregates
• timescaledb_information.continuous_aggregate_stats
• timescaledb_information.drop_chunks_policies
• timescaledb_information.policy_stats
• timescaledb_information.reorder_policies
• timescaledb.license_key
• timescaledb_pre_restore
• timescaledb_post_restore

Hypertable management

add_dimension()

Add an additional partitioning dimension to a TimescaleDB hypertable. The column selected as the dimension can either use interval partitioning (e.g., for a second time partition) or hash partitioning.

WARNING:The add_dimension command can only be executed after a table has been converted to a hypertable (via create_hypertable), but must similarly be run only on an empty hypertable.

Space partitions: The use of additional partitioning is a very specialized use case. Most users will not need to use it.

Space partitions use hashing: Every distinct item is hashed to one of N buckets. Remember that we are already using (flexible) time intervals to manage chunk sizes; the main purpose of space partitioning is to enable parallel I/O to the same time interval.

Parallel I/O can benefit in two scenarios: (a) two or more concurrent queries should be able to read from different disks in parallel, or (b) a single query should be able to use query parallelization to read from multiple disks in parallel.

Thus, users looking for parallel I/O have two options:

1. Use a RAID setup across multiple physical disks, and expose a single logical disk to the hypertable (i.e., via a single tablespace).

2. For each physical disk, add a separate tablespace to the database. TimescaleDB allows you to actually add multiple tablespaces to a single hypertable (although under the covers, a hypertable’s chunks are spread across the tablespaces associated with that hypertable).

We recommend a RAID setup when possible, as it supports both forms of parallelization described above (i.e., separate queries to separate disks, single query to multiple disks in parallel). The multiple tablespace approach only supports the former. With a RAID setup, no spatial partitioning is required.

That said, when using space partitions, we recommend using 1 space partition per disk.

TimescaleDB does not benefit from a very large number of space partitions (such as the number of unique items you expect in partition field). A very large number of such partitions leads both to poorer per-partition load balancing (the mapping of items to partitions using hashing), as well as much increased planning latency for some types of queries.

Required Arguments

Optional Arguments

Returns

When executing this function, either number_partitions or chunk_time_interval must be supplied, which will dictate if the dimension will use hash or interval partitioning.

The chunk_time_interval should be specified as follows:

• If the column to be partitioned is a TIMESTAMP, TIMESTAMPTZ, or DATE, this length should be specified either as an INTERVAL type or an integer value in microseconds.

• If the column is some other integer type, this length should be an integer that reflects the column’s underlying semantics (e.g., the chunk_time_interval should be given in milliseconds if this column is the number of milliseconds since the UNIX epoch).

WARNING:Supporting more than one additional dimension is currently experimental. For any production environments, users are recommended to use at most one “space” dimension (in addition to the required time dimension specified in create_hypertable).

Sample Usage

First convert table conditions to hypertable with just time partitioning on column time, then add an additional partition key on location with four partitions:

SELECT create_hypertable('conditions', 'time');
SELECT add_dimension('conditions', 'location', number_partitions => 4);

Convert table conditions to hypertable with time partitioning on time and space partitioning (2 partitions) on location, then add two additional dimensions.

SELECT create_hypertable('conditions', 'time', 'location', 2);
SELECT add_dimension('conditions', 'time_received', chunk_time_interval => INTERVAL '1 day');
SELECT add_dimension('conditions', 'device_id', number_partitions => 2);
SELECT add_dimension('conditions', 'device_id', number_partitions => 2, if_not_exists => true);

attach_tablespace()

Attach a tablespace to a hypertable and use it to store chunks. A tablespace is a directory on the filesystem that allows control over where individual tables and indexes are stored on the filesystem. A common use case is to create a tablespace for a particular storage disk, allowing tables to be stored there. Please review the standard PostgreSQL documentation for more information on tablespaces.

TimescaleDB can manage a set of tablespaces for each hypertable, automatically spreading chunks across the set of tablespaces attached to a hypertable. If a hypertable is hash partitioned, TimescaleDB will try to place chunks that belong to the same partition in the same tablespace. Changing the set of tablespaces attached to a hypertable may also change the placement behavior. A hypertable with no attached tablespaces will have its chunks placed in the database’s default tablespace.

Required Arguments

Tablespaces need to be created before being attached to a hypertable. Once created, tablespaces can be attached to multiple hypertables simultaneously to share the underlying disk storage. Associating a regular table with a tablespace using the TABLESPACE option to CREATE TABLE, prior to calling create_hypertable, will have the same effect as calling attach_tablespace immediately following create_hypertable.

Optional Arguments

Sample Usage

Attach the tablespace disk1 to the hypertable conditions:

SELECT attach_tablespace('disk1', 'conditions');
SELECT attach_tablespace('disk2', 'conditions', if_not_attached => true);

WARNING:The management of tablespaces on hypertables is currently an experimental feature.

create_hypertable()

Creates a TimescaleDB hypertable from a PostgreSQL table (replacing the latter), partitioned on time and with the option to partition on one or more other columns (i.e., space). All actions, such as ALTER TABLE, SELECT, etc., still work on the resulting hypertable.

Required Arguments

Optional Arguments

Returns

TIP:If you use SELECT * FROM create_hypertable(...) you will get the return value formatted as a table with column headings.

WARNING:The use of the migrate_data argument to convert a non-empty table can lock the table for a significant amount of time, depending on how much data is in the table. It can also run into deadlock if foreign key constraints exist to other tables.

If you would like finer control over index formation and other aspects of your hypertable, follow these migration instructions instead.

When converting a normal SQL table to a hypertable, pay attention to how you handle constraints. A hypertable can contain foreign keys to normal SQL table columns, but the reverse is not allowed. UNIQUE and PRIMARY constraints must include the partitioning key.

The deadlock is likely to happen when concurrent transactions simultaneously try to insert data into tables that are referenced in the foreign key constraints and into the converting table itself. The deadlock can be prevented by manually obtaining SHARE ROW EXCLUSIVE lock on the referenced tables before calling create_hypertable in the same transaction, see PostgreSQL documentation for the syntax.

Units

The ‘time’ column supports the following data types:

TIP:The type flexibility of the ‘time’ column allows the use of non-time-based values as the primary chunk partitioning column, as long as those values can increment.

TIP:For incompatible data types (e.g. jsonb) you can specify a function to the time_partitioning_func argument which can extract a compatible data type

The units of chunk_time_interval should be set as follows:

• For time columns having timestamp or DATE types, the chunk_time_interval should be specified either as an interval type or an integral value in microseconds.

• For integer types, the chunk_time_interval must be set explicitly, as the database does not otherwise understand the semantics of what each integer value represents (a second, millisecond, nanosecond, etc.). So if your time column is the number of milliseconds since the UNIX epoch, and you wish to each chunk to cover 1 day, you should specify chunk_time_interval => 86400000.

In case of hash partitioning (i.e., number_partitions is greater than zero), it is possible to optionally specify a custom partitioning function. If no custom partitioning function is specified, the default partitioning function is used. The default partitioning function calls PostgreSQL’s internal hash function for the given type, if one exists. Thus, a custom partitioning function can be used for value types that do not have a native PostgreSQL hash function. A partitioning function should take a single anyelement type argument and return a positive integer hash value. Note that this hash value is not a partition ID, but rather the inserted value’s position in the dimension’s key space, which is then divided across the partitions.

TIP:The time column in create_hypertable must be defined as NOT NULL. If this is not already specified on table creation, create_hypertable will automatically add this constraint on the table when it is executed.

Sample Usage

Convert table conditions to hypertable with just time partitioning on column time:

SELECT create_hypertable('conditions', 'time');

Convert table conditions to hypertable, setting chunk_time_interval to 24 hours.

SELECT create_hypertable('conditions', 'time', chunk_time_interval => 86400000000);
SELECT create_hypertable('conditions', 'time', chunk_time_interval => INTERVAL '1 day');

Convert table conditions to hypertable with time partitioning on time and space partitioning (4 partitions) on location:

SELECT create_hypertable('conditions', 'time', 'location', 4);

The same as above, but using a custom partitioning function:

SELECT create_hypertable('conditions', 'time', 'location', 4, partitioning_func => 'location_hash');

Convert table conditions to hypertable. Do not raise a warning if conditions is already a hypertable:

SELECT create_hypertable('conditions', 'time', if_not_exists => TRUE);

Time partition table measurements on a composite column type report using a time partitioning function: Requires an immutable function that can convert the column value into a supported column value:

CREATE TYPE report AS (reported timestamp with time zone, contents jsonb);
CREATE FUNCTION report_reported(report)
  RETURNS timestamptz
  LANGUAGE SQL
  IMMUTABLE AS
  'SELECT $1.reported';
SELECT create_hypertable('measurements', 'report', time_partitioning_func => 'report_reported');

Time partition table events, on a column type jsonb (event), which has a top level key (started) containing an ISO 8601 formatted timestamp:

CREATE FUNCTION event_started(jsonb)
  RETURNS timestamptz
  LANGUAGE SQL
  IMMUTABLE AS
  $func$SELECT ($1->>'started')::timestamptz$func$;
SELECT create_hypertable('events', 'event', time_partitioning_func => 'event_started');

Best Practices

One of the most common questions users of TimescaleDB have revolves around configuring chunk_time_interval.

Time intervals: The current release of TimescaleDB enables both the manual and automated adaption of its time intervals. With manually-set intervals, users should specify a chunk_time_interval when creating their hypertable (the default value is 1 week). The interval used for new chunks can be changed by calling set_chunk_time_interval().

The key property of choosing the time interval is that the chunk (including indexes) belonging to the most recent interval (or chunks if using space partitions) fit into memory. As such, we typically recommend setting the interval so that these chunk(s) comprise no more than 25% of main memory.

TIP:Make sure that you are planning for recent chunks from all active hypertables to fit into 25% of main memory, rather than 25% per hypertable.

To determine this, you roughly need to understand your data rate. If you are writing roughly 2GB of data per day and have 64GB of memory, setting the time interval to a week would be good. If you are writing 10GB per day on the same machine, setting the time interval to a day would be appropriate. This interval would also hold if data is loaded more in batches, e.g., you bulk load 70GB of data per week, with data corresponding to records from throughout the week.

While it’s generally safer to make chunks smaller rather than too large, setting intervals too small can lead to many chunks, which corresponds to increased planning latency for some types of queries.

TIP:One caveat is that the total chunk size is actually dependent on both the underlying data size and any indexes, so some care might be taken if you make heavy use of expensive index types (e.g., some PostGIS geospatial indexes). During testing, you might check your total chunk sizes via the chunk_relation_size function.

Space partitions: In most cases, it is advised for users not to use space partitions. The rare cases in which space partitions may be useful are described in the add_dimension section.

CREATE INDEX (Transaction Per Chunk)

CREATE INDEX ... WITH (timescaledb.transaction_per_chunk, ...);

This option extends CREATE INDEX with the ability to use a separate transaction for each chunk it creates an index on, instead of using a single transaction for the entire hypertable. This allows INSERTs, and other operations to to be performed concurrently during most of the duration of the CREATE INDEX command. While the index is being created on an individual chunk, it functions as if a regular CREATE INDEX were called on that chunk, however other chunks are completely un-blocked.

TIP:This version of CREATE INDEX can be used as an alternative to CREATE INDEX CONCURRENTLY, which is not currently supported on hypertables.

WARNING:If the operation fails partway through, indexes may not be created on all hypertable chunks. If this occurs, the index on the root table of the hypertable will be marked as invalid (this can be seen by running \d+ on the hypertable). The index will still work, and will be created on new chunks, but if you wish to ensure all chunks have a copy of the index, drop and recreate it.

Sample Usage

Anonymous index

CREATE INDEX ON conditions(time, device_id) WITH (timescaledb.transaction_per_chunk);

Other index methods

CREATE INDEX ON conditions(time, location) USING brin
  WITH (timescaledb.transaction_per_chunk);

detach_tablespace()

Detach a tablespace from one or more hypertables. This only means that new chunks will not be placed on the detached tablespace. This is useful, for instance, when a tablespace is running low on disk space and one would like to prevent new chunks from being created in the tablespace. The detached tablespace itself and any existing chunks with data on it will remain unchanged and will continue to work as before, including being available for queries. Note that newly inserted data rows may still be inserted into an existing chunk on the detached tablespace since existing data is not cleared from a detached tablespace. A detached tablespace can be reattached if desired to once again be considered for chunk placement.

Required Arguments

When giving only the tablespace name as argument, the given tablespace will be detached from all hypertables that the current role has the appropriate permissions for. Therefore, without proper permissions, the tablespace may still receive new chunks after this command is issued.

Optional Arguments

When specifying a specific hypertable, the tablespace will only be detached from the given hypertable and thus may remain attached to other hypertables.

Sample Usage

Detach the tablespace disk1 from the hypertable conditions:

SELECT detach_tablespace('disk1', 'conditions');
SELECT detach_tablespace('disk2', 'conditions', if_attached => true);

Detach the tablespace disk1 from all hypertables that the current user has permissions for:

SELECT detach_tablespace('disk1');

detach_tablespaces()

Detach all tablespaces from a hypertable. After issuing this command on a hypertable, it will no longer have any tablespaces attached to it. New chunks will instead be placed in the database’s default tablespace.

Required Arguments

Sample Usage

Detach all tablespaces from the hypertable conditions:

SELECT detach_tablespaces('conditions');

drop_chunks()

Removes data chunks whose time range falls completely before (or after) a specified time, operating either across all hypertables or for a specific one. Shows a list of the chunks that were dropped in the same style as the show_chunks function.

Chunks are defined by a certain start and end time. If older_than is specified, a chunk is dropped if its end time is older than the specified timestamp. Alternatively, if newer_than is specified, a chunk is dropped if its start time is newer than the specified timestamp. Note that, because chunks are removed if and only if their time range falls fully before (or after) the specified timestamp, the remaining data may still contain timestamps that are before (or after) the specified one.

Required Arguments

Function requires at least one of the following arguments. These arguments have the same semantics as the show_chunks function.

Optional Arguments

TIP:The table_name argument was optional in previous versions, but this is now deprecated: table_name should always be given.

The older_than and newer_than parameters can be specified in two ways:

• interval type: The cut-off point is computed as now() - older_than and similarly now() - newer_than. An error will be returned if an INTERVAL is supplied and the time column is not one of a TIMESTAMP, TIMESTAMPTZ, or DATE.

• timestamp, date, or integer type: The cut-off point is explicitly given as a TIMESTAMP / TIMESTAMPTZ / DATE or as a SMALLINT / INT / BIGINT. The choice of timestamp or integer must follow the type of the hypertable’s time column.

WARNING:When using just an interval type, the function assumes that you are are removing things in the past. If you want to remove data in the future (i.e., erroneous entries), use a timestamp.

When both arguments are used, the function returns the intersection of the resulting two ranges. For example, specifying newer_than => 4 months and older_than => 3 months will drop all full chunks that are between 3 and 4 months old. Similarly, specifying newer_than => '2017-01-01' and older_than => '2017-02-01' will drop all full chunks between ‘2017-01-01’ and ‘2017-02-01’. Specifying parameters that do not result in an overlapping intersection between two ranges will result in an error.

TIP:By default, calling drop_chunks on a table that has a continuous aggregate will throw an error. This can be resolved by setting cascade_to_materializations to TRUE, which will cause the corresponding aggregated data to also be dropped.

Sample Usage

Drop all chunks older than 3 months ago from hypertable conditions:

SELECT drop_chunks(INTERVAL '3 months', 'conditions');

Example output:

              drop_chunks
----------------------------------------
 _timescaledb_internal._hyper_3_5_chunk
 _timescaledb_internal._hyper_3_6_chunk
 _timescaledb_internal._hyper_3_7_chunk
 _timescaledb_internal._hyper_3_8_chunk
 _timescaledb_internal._hyper_3_9_chunk
(5 rows)

Drop all chunks more than 3 months in the future from hypertable conditions. This is useful for correcting data ingested with incorrect clocks:

SELECT drop_chunks(newer_than => now() + INTERVAL '3 months', table_name => 'conditions');

Drop all chunks from hypertable conditions before 2017:

SELECT drop_chunks(DATE '2017-01-01', 'conditions');

Drop all chunks from hypertable conditions before 2017, where time column is given in milliseconds from the UNIX epoch:

SELECT drop_chunks(1483228800000, 'conditions');

Drop all chunks from hypertable conditions older than 3 months, including dependent objects (e.g., views):

SELECT drop_chunks(INTERVAL '3 months', 'conditions', cascade => TRUE);

Drop all chunks older than 3 months ago and newer than 4 months ago from hypertable conditions:

SELECT drop_chunks(older_than => INTERVAL '3 months', newer_than => INTERVAL '4 months', table_name => 'conditions')

Drop all chunks older than 3 months, and delete this data from any continuous aggregates based on it:

SELECT drop_chunks(INTERVAL '3 months', 'conditions', cascade_to_materializations => true);

set_chunk_time_interval()

Sets the chunk_time_interval on a hypertable. The new interval is used when new chunks are created but the time intervals on existing chunks are not affected.

Required Arguments

Optional Arguments

The valid types for the chunk_time_interval depend on the type of hypertable time column:

• TIMESTAMP, TIMESTAMPTZ, DATE: The specified chunk_time_interval should be given either as an INTERVAL type (INTERVAL '1 day') or as an integer or bigint value (representing some number of microseconds).

• INTEGER: The specified chunk_time_interval should be an integer (smallint, int, bigint) value and represent the underlying semantics of the hypertable’s time column, e.g., given in milliseconds if the time column is expressed in milliseconds (see create_hypertable instructions).

Sample Usage

For a TIMESTAMP column, set chunk_time_interval to 24 hours.

SELECT set_chunk_time_interval('conditions', INTERVAL '24 hours');
SELECT set_chunk_time_interval('conditions', 86400000000);

For a time column expressed as the number of milliseconds since the UNIX epoch, set chunk_time_interval to 24 hours.

SELECT set_chunk_time_interval('conditions', 86400000);

set_number_partitions()

Sets the number of partitions (slices) of a space dimension on a hypertable. The new partitioning only affects new chunks.

Required Arguments

Optional Arguments

The dimension_name needs to be explicitly specified only if the hypertable has more than one space dimension. An error will be thrown otherwise.

Sample Usage

For a table with a single space dimension:

SELECT set_number_partitions('conditions', 2);

For a table with more than one space dimension:

SELECT set_number_partitions('conditions', 2, 'device_id');

set_integer_now_func()

This function is only relevant for hypertables with integer (as opposed to TIMESTAMP/TIMESTAMPTZ/DATE) time values. For such hypertables, it sets a function that returns the now() value (current time) in the units of the time column. This is necessary for running some policies on integer-based tables. In particular, many policies only apply to chunks of a certain age and a function that returns the current time is necessary to determine the age of a chunk.

Required Arguments

Optional Arguments

Sample Usage

To set the integer now function for a hypertable with a time column in unix time (number of seconds since the unix epoch, UTC).

CREATE OR REPLACE FUNCTION unix_now() returns BIGINT LANGUAGE SQL STABLE as $$ SELECT extract(epoch from now())::BIGINT $$;
SELECT set_integer_now_func('test_table_bigint', 'unix_now');

show_chunks()

Get list of chunks associated with hypertables.

Optional Arguments

Function accepts the following arguments. These arguments have the same semantics as the drop_chunks function.

The older_than and newer_than parameters can be specified in two ways:

• interval type: The cut-off point is computed as now() - older_than and similarly now() - newer_than. An error will be returned if an INTERVAL is supplied and the time column is not one of a TIMESTAMP, TIMESTAMPTZ, or DATE.

• timestamp, date, or integer type: The cut-off point is explicitly given as a TIMESTAMP / TIMESTAMPTZ / DATE or as a SMALLINT / INT / BIGINT. The choice of timestamp or integer must follow the type of the hypertable’s time column.

When both arguments are used, the function returns the intersection of the resulting two ranges. For example, specifying newer_than => 4 months and older_than => 3 months will show all full chunks that are between 3 and 4 months old. Similarly, specifying newer_than => '2017-01-01' and older_than => '2017-02-01' will show all full chunks between ‘2017-01-01’ and ‘2017-02-01’. Specifying parameters that do not result in an overlapping intersection between two ranges will result in an error.

Sample Usage

Get list of all chunks. Returns 0 if there are no hypertables:

SELECT show_chunks();

The expected output:

 show_chunks
---------------------------------------
 _timescaledb_internal._hyper_1_10_chunk
 _timescaledb_internal._hyper_1_11_chunk
 _timescaledb_internal._hyper_1_12_chunk
 _timescaledb_internal._hyper_1_13_chunk
 _timescaledb_internal._hyper_1_14_chunk
 _timescaledb_internal._hyper_1_15_chunk
 _timescaledb_internal._hyper_1_16_chunk
 _timescaledb_internal._hyper_1_17_chunk
 _timescaledb_internal._hyper_1_18_chunk

Get list of all chunks associated with a table:

SELECT show_chunks('conditions');

Get all chunks older than 3 months:

SELECT show_chunks(older_than => INTERVAL '3 months');

Get all chunks more than 3 months in the future. This is useful for showing data ingested with incorrect clocks:

SELECT show_chunks(newer_than => now() + INTERVAL '3 months');

Get all chunks from hypertable conditions older than 3 months:

SELECT show_chunks('conditions', older_than => INTERVAL '3 months');

Get all chunks from hypertable conditions before 2017:

SELECT show_chunks('conditions', older_than => DATE '2017-01-01');

Get all chunks newer than 3 months:

SELECT show_chunks(newer_than => INTERVAL '3 months');

Get all chunks older than 3 months and newer than 4 months:

SELECT show_chunks(older_than => INTERVAL '3 months', newer_than => INTERVAL '4 months');

reorder_chunk() Community

Reorder a single chunk’s heap to follow the order of an index. This function acts similarly to the PostgreSQL CLUSTER command , however it uses lower lock levels so that, unlike with the CLUSTER command, the chunk and hypertable are able to be read for most of the process. It does use a bit more disk space during the operation.

This command can be particularly useful when data is often queried in an order different from that in which it was originally inserted. For example, data is commonly inserted into a hypertable in loose time order (e.g., many devices concurrently sending their current state), but one might typically query the hypertable about a specific device. In such cases, reordering a chunk using an index on (device_id, time) can lead to significant performance improvement for these types of queries.

One can call this function directly on individual chunks of a hypertable, but using add_reorder_policy is often much more convenient.

Required Arguments

Optional Arguments

Returns

This function returns void.

Sample Usage

SELECT reorder_chunk('_timescaledb_internal._hyper_1_10_chunk', 'conditions_device_id_time_idx');

runs a reorder on the _timescaledb_internal._hyper_1_10_chunk chunk using the conditions_device_id_time_idx index.

move_chunk() Enterprise

TimescaleDB allows users to move data (and indexes) to alternative tablespaces. This allows the user the ability to move data to more cost effective storage as it ages. This function acts like the combination of the PostgreSQL CLUSTER command and the PostgreSQL ALTER TABLE…SET TABLESPACE command. However, it uses lower lock levels so that, unlike with these PostgreSQL commands, the chunk and hypertable are able to be read for most of the process. It does use a bit more disk space during the operation.

Required Arguments

Optional Arguments

Sample Usage

SELECT move_chunk(
  chunk => '_timescaledb_internal._hyper_1_4_chunk',
  destination_tablespace => 'tablespace_2',
  index_destination_tablespace => 'tablespace_3',
  reorder_index => 'conditions_device_id_time_idx',
  verbose => TRUE
);

Compression Community

We highly recommend reading the blog post and tutorial about compression before trying to set it up for the first time.

Setting up compression on TimescaleDB requires users to first configure the hypertable for compression and then set up a policy for when to compress chunks.

Advanced usage of compression alows users to compress chunks manually, instead of automatically as they age.

WARNING:Compression is not available when using TimescaleDB on PostgreSQL 9.6.

Restrictions

The current version does not support altering or inserting data into compressed chunks. The data can be queried without any modifications, however if you need to backfill or update data in a compressed chunk you will need to decompress the chunk(s) first.

Associated commands

• ALTER TABLE
• add_compress_chunks_policy
• remove_compress_chunks_policy
• compress_chunk
• decompress_chunk

ALTER TABLE (Compression) Community

‘ALTER TABLE’ statement is used to turn on compression and set compression options.

The syntax is:

ALTER TABLE <table_name> SET (timescaledb.compress, timescaledb.compress_orderby = '<column_name> [ASC | DESC] [ NULLS { FIRST | LAST } ] [, ...]',
timescaledb.compress_segmentby = '<column_name> [, ...]'
);

Required Options

Other Options

Parameters

Sample Usage

Configure a hypertable that ingests device data to use compression.

ALTER TABLE metrics SET (timescaledb.compress, timescaledb.compress_orderby = 'time DESC', timescaledb.compress_segmentby = 'device_id');

add_compress_chunks_policy() Community

Allows you to set a policy by which the system will compress a chunk automatically in the background after it reaches a given age.

Required Arguments

The time_interval parameter should be specifified differently depending on the type of the time column of the hypertable:

• For hypertables with TIMESTAMP, TIMESTAMPTZ, and DATE time columns: the time interval should be an INTERVAL type
• For hypertables with integer-based timestamps: the time interval should be an integer type (this requires the integer_now_func to be set).

Sample Usage

Add a policy to compress chunks older than 60 days on the ‘cpu’ hypertable.

SELECT add_compress_chunks_policy('cpu', INTERVAL '60d');

Add a compress chunks policy to a hypertable with an integer-based time column:

SELECT add_compress_chunks_policy('table_with_bigint_time', BIGINT '600000');

remove_compress_chunks_policy() Community

If you need to remove the compression policy. To re-start policy basd compression again you will need to re-add the policy.

Required Arguments

Sample Usage

Remove the compression policy from the ‘cpu’ table:

SELECT remove_compress_chunks_policy('cpu');

compress_chunk() Community

The compress_chunk function is used to compress a specific chunk. This is most often used instead of the add_compress_chunks_policy function, when a user wants more control over the scheduling of compression. For most users, we suggest using the policy framework instead.

TIP:You can get a list of chunks belonging to a hypertable using the show_chunks function.

Required Arguments

Optional Arguments

Sample Usage

Compress a single chunk.

SELECT compress_chunk('_timescaledb_internal._hyper_1_2_chunk');

decompress_chunk() Community

If you need to modify or add data to a chunk that has already been compressed, you will need to decompress the chunk first. This is especially useful for backfilling old data.

TIP:Prior to decompressing chunks for the purpose of data backfill or updating you should first stop any compression policy that is active on the hypertable you plan to perform this operation on. Once the update and/or backfill is complete simply turn the policy back on and the system will recompress your chucks.

Required Arguments

Optional Arguments

Sample Usage

Decompress a single chunk

SELECT decompress_chunk('_timescaledb_internal._hyper_2_2_chunk');

Continuous Aggregates Community

TimescaleDB allows users the ability to automatically recompute aggregates at predefined intervals and materialize the results. This is suitable for frequently used queries. For a more detailed discussion of this capability, please see using TimescaleDB Continuous Aggregates.

• CREATE VIEW
• ALTER VIEW
• REFRESH MATERIALIZED VIEW
• DROP VIEW

CREATE VIEW (Continuous Aggregate) Community

CREATE VIEW statement is used to create continuous aggregates.

The syntax is:

CREATE VIEW <view_name> [ ( column_name [, ...] ) ]
WITH ( timescaledb.continuous [, timescaledb.<option> = <value> ] )
AS
<select_query>

<select_query> is of the form :

SELECT <grouping_exprs>, <aggregate_functions>
    FROM <hypertable>
[WHERE ... ]
GROUP BY time_bucket( <const_value>, <partition_col_of_hypertable> ),
         [ optional grouping exprs>]
[HAVING ...]

Note that continuous aggregates have some limitations of what types of queries they can support, described in more length below. For example, the FROM clause must provide only one hypertable, i.e., no joins, views or subqueries are supported. The GROUP BY clause must include a time bucket on the hypertable’s time column, and all aggregates must be parallelizable.

Parameters

Required WITH clause options

Optional WITH clause options

TIP:Say, the continuous aggregate uses time_bucket(INTERVAL ‘2h’, time_column) and we want to keep the view up to date with the data. We can do this by modifying the refresh_lag setting. Set refresh_lag to -2h. E.g. ALTER VIEW contview set (timescaledb.refresh_lag = '-2h'); Please refer to the caveats.

Restrictions

• SELECT query should be of the form specified in the syntax above.
• The hypertable used in the SELECT may not have row-level-security policies enabled.
• GROUP BY clause must include a time_bucket expression. The time_bucket expression must use the time dimension column of the hypertable.
• The SELECT query should be of the form specified in the syntax above, which is discussed in the following items.
• Only a single hypertable can be specified in the FROM clause of the SELECT query. This means that including more hypertables, joins, tables, views, subqueries is not supported.
• The hypertable used in the SELECT may not have row-level-security policies enabled.
• The GROUP BY clause must include a time_bucket expression. The time_bucket expression must use the time dimension column of the hypertable.
• time_bucket_gapfill is not allowed in continuous aggs, but may be run in a SELECT from the continuous aggregate view.
• In general, aggregates which can be parallelized by PostgreSQL are allowed in the view definition, this includes most aggregates distributed with PostgreSQL. Aggregates with ORDER BY, DISTINCT and FILTER clauses are not permitted.
• All functions and their arguments included in SELECT, GROUP BY and HAVING clauses must be immutable.
• Queries with ORDER BY are disallowed.
• The view is not allowed to be a security barrier view.
• Window functions cannot be used in conjunction with continuous aggregates.

TIP:You can find the settings for continuous aggregates and statistics in timescaledb_information views.

Sample Usage

Create a continuous aggregate view.

CREATE VIEW continuous_aggregate_view( timec, minl, sumt, sumh )
WITH ( timescaledb.continuous,
    timescaledb.refresh_lag = '5 hours',
    timescaledb.refresh_interval = '1h' )
AS
    SELECT time_bucket('1day', timec), min(location), sum(temperature), sum(humidity)
        FROM conditions
        GROUP BY time_bucket('1day', timec)

Add additional continuous aggregates on top of the same raw hypertable.

CREATE VIEW continuous_aggregate_view( timec, minl, sumt, sumh )
WITH ( timescaledb.continuous,
    timescaledb.refresh_lag = '5 hours',
    timescaledb.refresh_interval = '1h' )
AS
    SELECT time_bucket('30day', timec), min(location), sum(temperature), sum(humidity)
        FROM conditions
        GROUP BY time_bucket('30day', timec);

TIP:In order to keep the continuous aggregate up to date with incoming data, the refresh lag can be set to -<bucket_width>. Please note that by doing so, you will incur higher write amplification and incur performance penalties.

CREATE VIEW continuous_aggregate_view( timec, minl, sumt, sumh )
WITH (timescaledb.continuous,
    timescaledb.refresh_lag = '-1h',
    timescaledb.refresh_interval = '30m')
AS
  SELECT time_bucket('1h', timec), min(location), sum(temperature), sum(humidity)
    FROM conditions
    GROUP BY time_bucket('1h', timec);

ALTER VIEW (Continuous Aggregate) Community

ALTER VIEW statement can be used to modify some of the WITH clause options for the continuous aggregate view.

ALTER VIEW <view_name> SET ( timescaledb.<option> =  <value> [, ... ] )

Parameters

Sample Usage

Set the max interval processed by a materializer job (that updates the continuous aggregate) to 1 week.

ALTER VIEW contagg_view SET (timescaledb.max_interval_per_job = '1 week');

Set the refresh lag to 1 hour, the refresh interval to 30 minutes and the max interval processed by a job to 1 week for the continuous aggregate.

ALTER VIEW contagg_view SET (timescaledb.refresh_lag = '1h', timescaledb.max_interval_per_job = '1 week', timescaledb.refresh_interval = '30m');

TIP:Only WITH options can be modified using the ALTER statment. If you need to change any other parameters, drop the view and create a new one.

REFRESH MATERIALIZED VIEW (Continuous Aggregate) Community

The continuous aggregate view can be manually updated by using REFRESH MATERIALIZED VIEW statement. A background materializer job will run immediately and update the continuous aggregate.

REFRESH MATERIALIZED VIEW <view_name>

Parameters

Sample Usage

Update the continuous aggregate view immediately.

REFRESH MATERIALIZED VIEW contagg_view;

TIP:Note that max_interval_per_job and refresh_lag parameter settings are used by the materialization job when the REFRESH is run. So the materialization (of the continuous aggregate) does not necessarily include all the updates to the hypertable.

DROP VIEW (Continuous Aggregate) Community

Continuous aggregate views can be dropped using DROP VIEW statement.

This deletes the hypertable that stores the materialized data for the continuous aggregate; it does not affect the data in the underlying hypertable from which the continuous aggregate is derived (i.e., the raw data). The CASCADE parameter is required for this command.

DROP VIEW <view_name> CASCADE;

Parameters

Sample Usage

Drop existing continuous aggregate.

DROP VIEW contagg_view CASCADE;

WARNING:CASCADE will drop those objects that depend on the continuous aggregate, such as views that are built on top of the continuous aggregate view.

Automation policies Community

TimescaleDB includes an automation framework for allowing background tasks to run inside the database, controllable by user-supplied policies. These tasks currently include capabilities around data retention and data reordering for improving query performance.

The following functions allow the administrator to create/remove/alter policies that schedule administrative actions to take place on a hypertable. The actions are meant to implement data retention or perform tasks that will improve query performance on older chunks. Each policy is assigned a scheduled job which will be run in the background to enforce it.

add_drop_chunks_policy() Community

Create a policy to drop chunks older than a given interval of a particular hypertable or continuous aggregate on a schedule in the background. (See drop_chunks). This implements a data retention policy and will remove data on a schedule. Only one drop-chunks policy may exist per hypertable.

Required Arguments

Optional Arguments

TIP:When dropping data from the raw hypertable while retaining data on a continuous aggregate, the older_than parameter to drop_chunks has to be longer than the timescaledb.ignore_invalidation_older_than parameter on the continuous aggregate. That is because we cannot process invalidations on data regions where the raw data has been dropped.

WARNING:If a drop chunks policy is setup which does not set cascade_to_materializations to either TRUE or FALSE on a hypertable that has a continuous aggregate, the policy will not drop any chunks.

Returns

Sample Usage

SELECT add_drop_chunks_policy('conditions', INTERVAL '6 months');

creates a data retention policy to discard chunks greater than 6 months old.

remove_drop_chunks_policy() Community

Remove a policy to drop chunks of a particular hypertable.

Required Arguments

Optional Arguments

Sample Usage

SELECT remove_drop_chunks_policy('conditions');

removes the existing data retention policy for the conditions table.

add_reorder_policy() Community

Create a policy to reorder chunks on a given hypertable index in the background. (See reorder_chunk). Only one reorder policy may exist per hypertable. Only chunks that are the 3rd from the most recent will be reordered to avoid reordering chunks that are still being inserted into.

TIP:Once a chunk has been reordered by the background worker it will not be reordered again. So if one were to insert significant amounts of data in to older chunks that have already been reordered, it might be necessary to manually re-run the reorder_chunk function on older chunks, or to drop and re-create the policy if many older chunks have been affected.

Required Arguments

Optional Arguments

Returns

Sample Usage

SELECT add_reorder_policy('conditions', 'conditions_device_id_time_idx');

creates a policy to reorder completed chunks by the existing (device_id, time) index. (See reorder_chunk).

remove_reorder_policy() Community

Remove a policy to reorder a particular hypertable.

Required Arguments

Optional Arguments

Sample Usage

SELECT remove_reorder_policy('conditions', if_exists => true);

removes the existing reorder policy for the conditions table if it exists.

alter_job_schedule() Community

Policy jobs are scheduled to run periodically via a job run in a background worker. You can change the schedule using alter_job_schedule. To alter an existing job, you must refer to it by job_id. The job_id which implements a given policy and its current schedule can be found in views in the timescaledb_information schema corresponding to different types of policies or in the general timescaledb_information.policy_stats view. This view additionally contains information about when each job was last run and other useful statistics for deciding what the new schedule should be.

TIP:Altering the schedule will only change the frequency at which the background worker checks the policy. If you need to change the data retention interval or reorder by a different index, you’ll need to remove the policy and add a new one.

Required Arguments

Optional Arguments

Returns

Sample Usage

SELECT alter_job_schedule(job_id, schedule_interval => INTERVAL '2 days')
FROM timescaledb_information.reorder_policies
WHERE hypertable = 'conditions'::regclass;

reschedules the reorder policy job for the conditions table so that it runs every two days.

SELECT alter_job_schedule(job_id, schedule_interval => INTERVAL '5 minutes')
FROM timescaledb_information.continuous_aggregate_stats
WHERE view_name = 'conditions_agg'::regclass;

reschedules the continuous aggregate job for the conditions_agg view so that it runs every five minutes.

SELECT alter_job_schedule(1015, next_start => '2020-03-15 09:00:00.0+00');

reschedules continuous aggregate job 1015 so that the next execution of the job starts at the specified time (9:00:00 am on March 15, 2020). This same query could have simultaneously changed the schedule_interval or queried the timescaledb_information.continuous_aggregate_stats informational view to extract the job_id, as shown above.

Analytics

first()

The first aggregate allows you to get the value of one column as ordered by another. For example, first(temperature, time) will return the earliest temperature value based on time within an aggregate group.

Required Arguments

Sample Usage

Get the earliest temperature by device_id:

SELECT device_id, first(temp, time)
FROM metrics
GROUP BY device_id;

WARNING:The last and first commands do not use indexes, and instead perform a sequential scan through their groups. They are primarily used for ordered selection within a GROUP BY aggregate, and not as an alternative to an ORDER BY time DESC LIMIT 1 clause to find the latest value (which will use indexes).

histogram()

The histogram() function represents the distribution of a set of values as an array of equal-width buckets. It partitions the dataset into a specified number of buckets (nbuckets) ranging from the inputted min and max values.

The return value is an array containing nbuckets+2 buckets, with the middle nbuckets bins for values in the stated range, the first bucket at the head of the array for values under the lower min bound, and the last bucket for values greater than or equal to the max bound. Each bucket is inclusive on its lower bound, and exclusive on its upper bound. Therefore, values equal to the min are included in the bucket starting with min, but values equal to the max are in the last bucket.

Required Arguments

Sample Usage

A simple bucketing of device’s battery levels from the readings dataset:

SELECT device_id, histogram(battery_level, 20, 60, 5)
FROM readings
GROUP BY device_id
LIMIT 10;

The expected output:

 device_id  |          histogram
------------+------------------------------
 demo000000 | {0,0,0,7,215,206,572}
 demo000001 | {0,12,173,112,99,145,459}
 demo000002 | {0,0,187,167,68,229,349}
 demo000003 | {197,209,127,221,106,112,28}
 demo000004 | {0,0,0,0,0,39,961}
 demo000005 | {12,225,171,122,233,80,157}
 demo000006 | {0,78,176,170,8,40,528}
 demo000007 | {0,0,0,126,239,245,390}
 demo000008 | {0,0,311,345,116,228,0}
 demo000009 | {295,92,105,50,8,8,442}

interpolate() Community

The interpolate function does linear interpolation for missing values. It can only be used in an aggregation query with time_bucket_gapfill. The interpolate function call cannot be nested inside other function calls.

Required Arguments

Optional Arguments

Because the interpolation function relies on having values before and after each bucketed period to compute the interpolated value, it might not have enough data to calculate the interpolation for the first and last time bucket if those buckets do not otherwise contain valid values. For example, the interpolation would require looking before this first time bucket period, yet the query’s outer time predicate WHERE time > … normally restricts the function to only evaluate values within this time range. Thus, the prev and next expression tell the function how to look for values outside of the range specified by the time predicate. These expressions will only be evaluated when no suitable value is returned by the outer query (i.e., the first and/or last bucket in the queried time range is empty). The returned record for prev and next needs to be a time, value tuple. The datatype of time needs to be the same as the time datatype in the time_bucket_gapfill call. The datatype of value needs to be the same as the value datatype of the interpolate call.

Sample Usage

Get the temperature every day for each device over the last week interpolating for missing readings:

SELECT
  time_bucket_gapfill('1 day', time, now() - INTERVAL '1 week', now()) AS day,
  device_id,
  avg(temperature) AS value,
  interpolate(avg(temperature))
FROM metrics
WHERE time > now () - INTERVAL '1 week'
GROUP BY day, device_id
ORDER BY day;
           day          | device_id | value | interpolate
------------------------+-----------+-------+-------------
 2019-01-10 01:00:00+01 |         1 |       |
 2019-01-11 01:00:00+01 |         1 |   5.0 |         5.0
 2019-01-12 01:00:00+01 |         1 |       |         6.0
 2019-01-13 01:00:00+01 |         1 |   7.0 |         7.0
 2019-01-14 01:00:00+01 |         1 |       |         7.5
 2019-01-15 01:00:00+01 |         1 |   8.0 |         8.0
 2019-01-16 01:00:00+01 |         1 |   9.0 |         9.0
(7 row)

Get the average temperature every day for each device over the last 7 days interpolating for missing readings with lookup queries for values before and after the gapfill time range:

SELECT
  time_bucket_gapfill('1 day', time, now() - INTERVAL '1 week', now()) AS day,
  device_id,
  avg(value) AS value,
  interpolate(avg(temperature),
    (SELECT (time,temperature) FROM metrics m2 WHERE m2.time < now() - INTERVAL '1 week' AND m.device_id = m2.device_id ORDER BY time DESC LIMIT 1),
    (SELECT (time,temperature) FROM metrics m2 WHERE m2.time > now() AND m.device_id = m2.device_id ORDER BY time DESC LIMIT 1)
  ) AS interpolate
FROM metrics m
WHERE time > now () - INTERVAL '1 week'
GROUP BY day, device_id
ORDER BY day;
           day          | device_id | value | interpolate
------------------------+-----------+-------+-------------
 2019-01-10 01:00:00+01 |         1 |       |         3.0
 2019-01-11 01:00:00+01 |         1 |   5.0 |         5.0
 2019-01-12 01:00:00+01 |         1 |       |         6.0
 2019-01-13 01:00:00+01 |         1 |   7.0 |         7.0
 2019-01-14 01:00:00+01 |         1 |       |         7.5
 2019-01-15 01:00:00+01 |         1 |   8.0 |         8.0
 2019-01-16 01:00:00+01 |         1 |   9.0 |         9.0
(7 row)

last()

The last aggregate allows you to get the value of one column as ordered by another. For example, last(temperature, time) will return the latest temperature value based on time within an aggregate group.

Required Arguments

Sample Usage

Get the temperature every 5 minutes for each device over the past day:

SELECT device_id, time_bucket('5 minutes', time) AS interval,
  last(temp, time)
FROM metrics
WHERE time > now () - INTERVAL '1 day'
GROUP BY device_id, interval
ORDER BY interval DESC;

WARNING:The last and first commands do not use indexes, and instead perform a sequential scan through their groups. They are primarily used for ordered selection within a GROUP BY aggregate, and not as an alternative to an ORDER BY time DESC LIMIT 1 clause to find the latest value (which will use indexes).

locf() Community

The locf function (last observation carried forward) allows you to carry the last seen value in an aggregation group forward. It can only be used in an aggregation query with time_bucket_gapfill. The locf function call cannot be nested inside other function calls.

Required Arguments

Optional Arguments

Because the locf function relies on having values before each bucketed period to carry forward, it might not have enough data to fill in a value for the first bucket if it does not contain a value. For example, the function would need to look before this first time bucket period, yet the query’s outer time predicate WHERE time > … normally restricts the function to only evaluate values within this time range. Thus, the prev expression tell the function how to look for values outside of the range specified by the time predicate. The prev expression will only be evaluated when no previous value is returned by the outer query (i.e., the first bucket in the queried time range is empty).

Sample Usage

Get the average temperature every day for each device over the last 7 days carrying forward the last value for missing readings:

SELECT
  time_bucket_gapfill('1 day', time, now() - INTERVAL '1 week', now()) AS day,
  device_id,
  avg(temperature) AS value,
  locf(avg(temperature))
FROM metrics
WHERE time > now () - INTERVAL '1 week'
GROUP BY day, device_id
ORDER BY day;
           day          | device_id | value | locf
------------------------+-----------+-------+------
 2019-01-10 01:00:00+01 |         1 |       |
 2019-01-11 01:00:00+01 |         1 |   5.0 |  5.0
 2019-01-12 01:00:00+01 |         1 |       |  5.0
 2019-01-13 01:00:00+01 |         1 |   7.0 |  7.0
 2019-01-14 01:00:00+01 |         1 |       |  7.0
 2019-01-15 01:00:00+01 |         1 |   8.0 |  8.0
 2019-01-16 01:00:00+01 |         1 |   9.0 |  9.0
(7 row)

Get the average temperature every day for each device over the last 7 days carrying forward the last value for missing readings with out-of-bounds lookup

SELECT
  time_bucket_gapfill('1 day', time, now() - INTERVAL '1 week', now()) AS day,
  device_id,
  avg(temperature) AS value,
  locf(
    avg(temperature),
    (SELECT temperature FROM metrics m2 WHERE m2.time < now() - INTERVAL '2 week' AND m.device_id = m2.device_id ORDER BY time DESC LIMIT 1)
  )
FROM metrics m
WHERE time > now () - INTERVAL '1 week'
GROUP BY day, device_id
ORDER BY day;
           day          | device_id | value | locf
------------------------+-----------+-------+------
 2019-01-10 01:00:00+01 |         1 |       |  1.0
 2019-01-11 01:00:00+01 |         1 |   5.0 |  5.0
 2019-01-12 01:00:00+01 |         1 |       |  5.0
 2019-01-13 01:00:00+01 |         1 |   7.0 |  7.0
 2019-01-14 01:00:00+01 |         1 |       |  7.0
 2019-01-15 01:00:00+01 |         1 |   8.0 |  8.0
 2019-01-16 01:00:00+01 |         1 |   9.0 |  9.0
(7 row)

time_bucket()

This is a more powerful version of the standard PostgreSQL date_trunc function. It allows for arbitrary time intervals instead of the second, minute, hour, etc. provided by date_trunc. The return value is the bucket’s start time. Below is necessary information for using it effectively.

TIP:TIMESTAMPTZ arguments are bucketed by the time at UTC. So the alignment of buckets is on UTC time. One consequence of this is that daily buckets are aligned to midnight UTC, not local time.

If the user wants buckets aligned by local time, the TIMESTAMPTZ input should be cast to TIMESTAMP (such a cast converts the value to local time) before being passed to time_bucket (see example below). Note that along daylight savings time boundaries the amount of data aggregated into a bucket after such a cast is irregular: for example if the bucket_width is 2 hours, the number of UTC hours bucketed by local time on daylight savings time boundaries can be either 3 hours or 1 hour.

Required Arguments

Optional Arguments

For Integer Time Inputs

Required Arguments

Optional Arguments

Sample Usage

Simple 5-minute averaging:

SELECT time_bucket('5 minutes', time) AS five_min, avg(cpu)
FROM metrics
GROUP BY five_min
ORDER BY five_min DESC LIMIT 10;

To report the middle of the bucket, instead of the left edge:

SELECT time_bucket('5 minutes', time) + '2.5 minutes'
  AS five_min, avg(cpu)
FROM metrics
GROUP BY five_min
ORDER BY five_min DESC LIMIT 10;

For rounding, move the alignment so that the middle of the bucket is at the 5 minute mark (and report the middle of the bucket):

SELECT time_bucket('5 minutes', time, '-2.5 minutes') + '2.5 minutes'
  AS five_min, avg(cpu)
FROM metrics
GROUP BY five_min
ORDER BY five_min DESC LIMIT 10;

To shift the alignment of the buckets you can use the origin parameter (passed as a timestamp, timestamptz, or date type). In this example, we shift the start of the week to a Sunday (the default is a Monday).

SELECT time_bucket('1 week', timetz, TIMESTAMPTZ '2017-12-31')
  AS one_week, avg(cpu)
FROM metrics
GROUP BY one_week
WHERE time > TIMESTAMPTZ '2017-12-01'  AND time < TIMESTAMPTZ '2018-01-03'
ORDER BY one_week DESC LIMIT 10;

The value of the origin parameter we used in this example was 2017-12-31, a Sunday within the period being analyzed. However, the origin provided to the function can be before, during, or after the data being analyzed. All buckets are calculated relative to this origin. So, in this example, any Sunday could have been used. Note that because time < TIMESTAMPTZ '2018-01-03' in this example, the last bucket would have only 4 days of data.

Bucketing a TIMESTAMPTZ at local time instead of UTC(see note above):

SELECT time_bucket(INTERVAL '2 hours', timetz::TIMESTAMP)
  AS five_min, avg(cpu)
FROM metrics
GROUP BY five_min
ORDER BY five_min DESC LIMIT 10;

Note that the above cast to TIMESTAMP converts the time to local time according to the server’s timezone setting.

WARNING:For users upgrading from a version before 1.0.0, please note that the default origin was moved from 2000-01-01 (Saturday) to 2000-01-03 (Monday) between versions 0.12.1 and 1.0.0. This change was made to make time_bucket compliant with the ISO standard for Monday as the start of a week. This should only affect multi-day calls to time_bucket. The old behavior can be reproduced by passing 2000-01-01 as the origin parameter to time_bucket.

time_bucket_gapfill() Community

The time_bucket_gapfill function works similar to time_bucket but also activates gap filling for the interval between start and finish. It can only be used with an aggregation query. Values outside of start and finish will pass through but no gap filling will be done outside of the specified range.

Starting with version 1.3.0, start and finish are optional arguments and will be inferred from the WHERE clause if not supplied as arguments.

TIP:We recommend using a WHERE clause whenever possible (instead of just start and finish arguments), as start and finish arguments will not filter input rows. Thus without a WHERE clause, this will lead TimescaleDB’s planner to select all data and not perform constraint exclusion to exclude chunks from further processing, which would be less performant.

The time_bucket_gapfill must be a top-level expression in a query or subquery, as shown in the above examples. You cannot, for example, do something like round(time_bucket_gapfill(...)) or cast the result of the gapfill call (unless as a subquery where the outer query does the type cast).

Required Arguments

Optional Arguments

Note that explicitly provided start and stop or derived from WHERE clause values need to be simple expressions. Such expressions should be evaluated to constants at the query planning. For example, simple expressions can contain constants or call to now(), but cannot reference to columns of a table.

For Integer Time Inputs

Required Arguments

Optional Arguments

Starting with version 1.3.0 start and finish are optional arguments and will be inferred from the WHERE clause if not supplied as arguments.

Sample Usage

Get the metric value every day over the last 7 days:

SELECT
  time_bucket_gapfill('1 day', time) AS day,
  device_id,
  avg(value) AS value
FROM metrics
WHERE time > now() - INTERVAL '1 week' AND time < now()
GROUP BY day, device_id
ORDER BY day;
           day          | device_id | value
------------------------+-----------+-------
 2019-01-10 01:00:00+01 |         1 |
 2019-01-11 01:00:00+01 |         1 |   5.0
 2019-01-12 01:00:00+01 |         1 |
 2019-01-13 01:00:00+01 |         1 |   7.0
 2019-01-14 01:00:00+01 |         1 |
 2019-01-15 01:00:00+01 |         1 |   8.0
 2019-01-16 01:00:00+01 |         1 |   9.0
(7 row)

Get the metric value every day over the last 7 days carrying forward the previous seen value if none is available in an interval:

SELECT
  time_bucket_gapfill('1 day', time) AS day,
  device_id,
  avg(value) AS value,
  locf(avg(value))
FROM metrics
WHERE time > now() - INTERVAL '1 week' AND time < now()
GROUP BY day, device_id
ORDER BY day;
           day          | device_id | value | locf
------------------------+-----------+-------+------
 2019-01-10 01:00:00+01 |         1 |       |
 2019-01-11 01:00:00+01 |         1 |   5.0 |  5.0
 2019-01-12 01:00:00+01 |         1 |       |  5.0
 2019-01-13 01:00:00+01 |         1 |   7.0 |  7.0
 2019-01-14 01:00:00+01 |         1 |       |  7.0
 2019-01-15 01:00:00+01 |         1 |   8.0 |  8.0
 2019-01-16 01:00:00+01 |         1 |   9.0 |  9.0

Get the metric value every day over the last 7 days interpolating missing values:

SELECT
  time_bucket_gapfill('5 minutes', time) AS day,
  device_id,
  avg(value) AS value,
  interpolate(avg(value))
FROM metrics
WHERE time > now() - INTERVAL '1 week' AND time < now()
GROUP BY day, device_id
ORDER BY day;
           day          | device_id | value | interpolate
------------------------+-----------+-------+-------------
 2019-01-10 01:00:00+01 |         1 |       |
 2019-01-11 01:00:00+01 |         1 |   5.0 |         5.0
 2019-01-12 01:00:00+01 |         1 |       |         6.0
 2019-01-13 01:00:00+01 |         1 |   7.0 |         7.0
 2019-01-14 01:00:00+01 |         1 |       |         7.5
 2019-01-15 01:00:00+01 |         1 |   8.0 |         8.0
 2019-01-16 01:00:00+01 |         1 |   9.0 |         9.0

Utilities/Statistics

timescaledb_information.hypertable

Get information about hypertables.

Available Columns

Sample Usage

Get information about all hypertables.

SELECT * FROM timescaledb_information.hypertable;
 table_schema | table_name | table_owner | num_dimensions | num_chunks | table_size | index_size | toast_size | total_size
--------------+------------+-------------+----------------+------------+------------+------------+------------+------------
 public       | metrics    | postgres    |              1 |          5 | 99 MB      | 96 MB      |            | 195 MB
 public       | devices    | postgres    |              1 |          1 | 8192 bytes | 16 kB      |            | 24 kB
(2 rows)

Check whether a table is a hypertable.

SELECT * FROM timescaledb_information.hypertable
WHERE table_schema='public' AND table_name='metrics';
 table_schema | table_name | table_owner | num_dimensions | num_chunks | table_size | index_size | toast_size | total_size
--------------+------------+-------------+----------------+------------+------------+------------+------------+------------
 public       | metrics    | postgres    |              1 |          5 | 99 MB      | 96 MB      |            | 195 MB
(1 row)

If you want to see the current interval length for your hypertables, you can check the _timescaledb_catalog as follows. Note that for time-based interval lengths, these are reported in microseconds.

SELECT h.table_name, c.interval_length FROM _timescaledb_catalog.dimension c
  JOIN _timescaledb_catalog.hypertable h ON h.id = c.hypertable_id;
table_name | interval_length
------------+-----------------
metrics       |    604800000000
(1 row)

timescaledb_information.license

Get information about current license.

Available Columns

Sample Usage

Get information about current license.

SELECT * FROM timescaledb_information.license;
edition   | expired |    expiration_time
------------+---------+------------------------
enterprise | f       | 2019-02-15 13:44:53-05
(1 row)

timescaledb_information.compressed_chunk_stats

Get statistics about chunk compression.

Available Columns

Sample Usage

SELECT * FROM timescaledb_information.compressed_chunk_stats;
-[ RECORD 1 ]------------+---------------------------------------
hypertable_name          | foo
chunk_name               | _timescaledb_internal._hyper_1_1_chunk
compression_status       | Uncompressed
uncompressed_heap_bytes  |
uncompressed_index_bytes |
uncompressed_toast_bytes |
uncompressed_total_bytes |
compressed_heap_bytes    |
compressed_index_bytes   |
compressed_toast_bytes   |
compressed_total_bytes   |
-[ RECORD 2 ]------------+---------------------------------------
hypertable_name          | foo
chunk_name               | _timescaledb_internal._hyper_1_2_chunk
compression_status       | Compressed
uncompressed_heap_bytes  | 8192 bytes
uncompressed_index_bytes | 32 kB
uncompressed_toast_bytes | 0 bytes
uncompressed_total_bytes | 40 kB
compressed_heap_bytes    | 8192 bytes
compressed_index_bytes   | 32 kB
compressed_toast_bytes   | 8192 bytes
compressed_total_bytes   | 48 kB

timescaledb_information.compressed_hypertable_stats

Get statistics about hypertable compression.

Available Columns

Sample Usage

SELECT * FROM timescaledb_information.compressed_hypertable_stats;
-[ RECORD 1 ]------------+-----------
hypertable_name          | foo
total_chunks             | 4
number_compressed_chunks | 1
uncompressed_heap_bytes  | 8192 bytes
uncompressed_index_bytes | 32 kB
uncompressed_toast_bytes | 0 bytes
uncompressed_total_bytes | 40 kB
compressed_heap_bytes    | 8192 bytes
compressed_index_bytes   | 32 kB
compressed_toast_bytes   | 8192 bytes
compressed_total_bytes   | 48 kB

timescaledb_information.continuous_aggregates

Get metadata and settings information for continuous aggregates.

Available Columns

Sample Usage

SELECT * FROM timescaledb_information.continuous_aggregates;
-[ RECORD 1 ]------------------+-------------------------------------------------
view_name                      | contagg_view
view_owner                     | postgres
refresh_lag                    | 02:00:00
refresh_interval               | 00:30:00
max_interval_per_job           | 20
ignore_invalidation_older_than | 7 days
materialized_only              | f
materialization_hypertable     | _timescaledb_internal._materialized_hypertable_2
view_definition                |  SELECT foo.a,                                  +
                               |     COUNT(foo.b) AS countb                      +
                               |    FROM foo                                     +
                               |   GROUP BY (time_bucket('1 day', foo.a)), foo.a;
-- description of foo
\d foo
                Table "public.foo"
 Column |  Type   | Collation | Nullable | Default
--------+---------+-----------+----------+---------
 a      | integer |           | not null |
 b      | integer |           |          |
 c      | integer |           |          |

timescaledb_information.continuous_aggregate_stats

Get information about background jobs and statistics related to continuous aggregates.

Available Columns

Sample Usage

SELECT * FROM timescaledb_information.continuous_aggregate_stats;
-[ RECORD 1 ]----------+------------------------------
view_name              | contagg_view
completed_threshold    | 1
invalidation_threshold | 1
job_id                 | 1003
last_run_started_at    | 2019-07-03 15:00:26.016018-04
last_run_status        | Success
job_status             | scheduled
last_run_duration      | 00:00:00.039163
next_scheduled_run     | 2019-07-03 15:00:56.055181-04
total_runs             | 3
total_successes        | 3
total_failures         | 0
total_crashes          | 0

timescaledb_information.drop_chunks_policies

Shows information about drop_chunks policies that have been created by the user. (See add_drop_chunks_policy for more information about drop_chunks policies).

Available Columns

Sample Usage

Get information about drop_chunks policies.

SELECT * FROM timescaledb_information.drop_chunks_policies;
       hypertable       | older_than | cascade | job_id | schedule_interval | max_runtime | max_retries | retry_period
------------------------+------------+---------+--------+-------------------+-------------+-------------+--------------
       conditions       | @ 4 mons   | t       |   1001 | @ 1 sec           | @ 5 mins    |          -1 | @ 12 hours
(1 row)

timescaledb_information.reorder_policies

Shows information about reorder policies that have been created by the user. (See add_reorder_policy for more information about reorder policies).

Available Columns