Indexing data

TimescaleDB supports the range of PostgreSQL index types, and creating, altering, or dropping an index on the hypertable ([PostgreSQL docs][postgres-createindex]) is similarly propagated to all its constituent chunks.

Data is indexed via the SQL CREATE INDEX command. For instance,

CREATE INDEX ON conditions (location, time DESC);

This can be done before or after converting the table to a hypertable.


For sparse data where a column is often NULL, we suggest adding a WHERE column IS NOT NULL clause to the index (unless you are often searching for missing data). For example,

CREATE INDEX ON conditions (time DESC, humidity)
  WHERE humidity IS NOT NULL;

this creates a more compact, and thus efficient, index.

Best practices

Our experience has shown that for time-series data, the most-useful index type varies depending on your data.

For indexing columns with discrete (limited-cardinality) values (e.g., where you are most likely to use an "equals" or "not equals" comparator) we suggest using an index like this (using our hypertable conditions for the example):

CREATE INDEX ON conditions (location, time DESC);

For all other types of columns, i.e., columns with continuous values (e.g., where you are most likely to use a "less than" or "greater than" comparator) the index should be in the form:

CREATE INDEX ON conditions (time DESC, temperature);

Having a time DESC column specification in the index allows for efficient queries by column-value and time. For example, the index defined above would optimize the following query:

SELECT * FROM conditions WHERE location = 'garage'

To understand why composite indexes should be defined in such a fashion, consider an example with two locations ("office" and "garage"), and various timestamps and temperatures:

An index on (location, time DESC) would be organized in sorted order as follows:


An index on (time DESC, location) would be organized in sorted order as follows:


One can think of an index's btrees as being constructed from the most significant bit downwards, so it first matches on the first character, then second, etc., while in the above example they are conveniently shown as two separate tuples.

Now, with a predicate like WHERE location = 'garage' and time >= 1 and time < 4, the top is much better to use: all readings from a given location are contiguous, so the first bit of the index precisely finds all metrics from "garage", and then we can use any additional time predicates to further narrow down the selected set. With the latter, you would have to look over all of the time records [1,4), and then once again find the right device in each. Much less efficient.

On the other hand, consider if our conditional was instead asking temperature > 80, particularly if that conditional matched a larger number of values. You still need to search through all sets of time values matching your predicate, but in each one, your query also grabs a (potentially large) subset of the values, rather than just one distinct one.


To define an index as UNIQUE or PRIMARY KEY, the time column and, if it exists, the partitioning column must be part of the index. That is, using our running example, you can define a unique index on just the (time, location) fields, or to include additional columns (say, temperature). That said, we find UNIQUE indexes in time-series data to be much less prevalent than in traditional relational data models.

Default indexes

By default, TimescaleDB automatically creates a time index on your data when a hypertable is created.

CREATE INDEX ON conditions (time DESC);

Additionally, if the create_hypertable command specifies an optional "space partition" in addition to time (say, the location column), TimescaleDB automatically creates the following index:

CREATE INDEX ON conditions (location, time DESC);

This default behavior can be overridden when executing the create_hypertable command.

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