Design for performance

To optimize the performance of a QuestDB instance, it is important to adjust system and table configuration according to the nature of the data. This page lists out common configurations that users should take into account when testing data using QuestDB.

To monitor various metrics of the QuestDB instances, refer to the Prometheus monitoring page or the Logging & Monitoring page.

Refer to Capacity planning for deployment considerations.

Optimizing queries

The following section describes the underlying aspects to consider when formulating queries.

Row serialization

Row serialization and deserialization has a cost on both client and server. The QuestDB Web Console limits fetching to 10,000 dataset. When fetching a large (10K+) dataset via a single query using other methods, consider using pagination, hence multiple queries instead.

Choosing a schema

This section provides some hints for choosing the right schema for a dataset based on the storage space that types occupy in QuestDB.


When creating tables, a partitioning strategy is recommended in order to be able to enforce a data retention policy to save disk space, and for optimizations on the number of concurrent file reads performed by the system. For more information on this topic, see the following resources:

  • partitions page which provides a general overview of this concept
  • data retention guide provides further details on partitioning tables with examples on how to drop partitions by time range

Records per partition

The number of records per partition should factor into the partitioning strategy (YEAR, MONTH, WEEK, DAY, HOUR). Having too many records per partition or having too few records per partition and having query operations across too many partitions has the result of slower query times. A general guideline is that roughly between 1 million and 100 million records is optimal per partition.


For storing high-cardinality string data QuestDB supports two types:

  1. varchar
  2. string

While these types can be used interchangeably, new tables should prefer varchar columns as it is more efficient.

StorageTypically lower storage requirementsHigher storage requirements
OptimizationOptimized for common operations (filtering, sorting)Not optimized for modern use cases
RecommendationRecommended for new tablesConsidered a legacy type, not recommended for new tables
CompatibilityCompatible with newer versions of QuestDBKept for compatibility with older versions, may be deprecated


Symbols are a data type that is recommended to be used for strings that are repeated often in a dataset. The benefit of using this data type is lower storage requirements than storing strings in varchar columns and faster performance on queries as symbols are internally stored as int values.

Only symbols can be indexed in QuestDB. Although multiple indexes can be specified for a table, there would be a performance impact on the rate of ingestion.

The following example shows the creation of a table with a symbol type that has multiple options passed for performance optimization.

CREATE TABLE my_table(
) timestamp(ts) PARTITION BY DAY;

This example adds a symbol type with:

  • capacity specified to estimate how many unique symbol values to expect
  • caching disabled which allows dealing with larger value counts
  • index for the symbol column with a storage block value

A full description of the options used above for symbol types can be found in the CREATE TABLE page.

Symbol caching

Symbol cache enables the use of on-heap cache for reads and can enhance performance. However, the cache size grows as the number of distinct value increases, and the size of the cached symbol may hinder query performance.

We recommend that users check the JVM and GC metrics via Prometheus monitoring before taking one of the following steps:

  • Disabling the symbol cache. See Usage of symbols for server-wide and table-wide configuration options.
  • Increasing the JVM heap size using the -Xmx argument.

Symbol capacity

Symbol capacity should be the same or slightly larger than the count of distinct symbol values.

Undersized symbol columns slow down query performance. Similarly, there is a performance impact when symbol is not used for its designed way, most commonly assigning symbol to columns with a unique value per row. It is crucial to choose a suitable data type based on the nature of the dataset.


Appropriate us of indexes provides faster read access to a table. However, indexes have a noticeable cost in terms of disk space and ingestion rate - we recommend starting with no indexes and adding them later, only if they appear to improve query performance. Refer to Index trade-offs for more information.


The storage space that numbers occupy can be optimized by choosing byte, short, and int data types appropriately. When values are not expected to exceed the limit for that particular type, savings on disk space can be made. See also Data types for more details.

typestorage per valuenumeric range
byte8 bits-128 to 127
short16 bits-32768 to 32767
int32 bits-2147483648 to 2147483647
float32 bitsSingle precision IEEE 754 floating point
double64 bitsDouble precision IEEE 754 floating point