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- INSERT Statements 11811
- INSERT statements are the standard way of loading data into a relational database. When using INSERT statements, the values are supplied row-by-row. While simple, there is significant overhead involved in parsing and processing individual I
...te transaction, meaning fsync will be called for every statement. This is typically unnecessary when bulk loading and will significantly slow down your program. Tip If you absolutely must use INSERT statements in a loop to load data, wrap them in calls to BEG... - https://man.mtool.work/duckdb/2.0/data/insert.html
- SAMPLE Clause 11811
- The SAMPLE clause allows you to run the query on a sample from the base table. This can significantly speed up processing of queries, at the expense of accuracy in the result. Samples can also be used to quickly see a snapshot of the data w
... - https://man.mtool.work/duckdb/2.0/sql/query_syntax/sample.html
- Out-of-Memory Issues 11242
- Configuration Options If you are experiencing out-of-memory issues with DuckDB, try tweaking the following configuration options: Reduce the number of threads using the SET threads = ... command. Setting the memory limit lower than the defa
...ce, using the most restrictive types possible when creating columns is necessary to reduce memory consumption. Indexes can significantly increase memory consumption. Additionally, they are currently not buffer-managed . Avoid using indexes if possible. If you... - https://man.mtool.work/duckdb/2.0/guides/performance/oom.html
- Parquet Tips 9586
- Below is a collection of tips to help when dealing with Parquet files. Tips for Reading Parquet Files Use union_by_name When Loading Files with Different Schemas The union_by_name option can be used to unify the schema of files that have di
...e files. Enabling PER_THREAD_OUTPUT If the final number of Parquet files is not important, writing one file per thread can significantly improve performance: COPY (FROM generate_series(10_000_000)) TO 'test.parquet' (FORMAT parquet, PER_THREAD_OUTPUT); Select... - https://man.mtool.work/duckdb/2.0/data/parquet/tips.html
- Building Configuration 9035
- Build Types DuckDB can be built in many different settings, most of these correspond directly to CMake but not all of them. release This build has been stripped of all the assertions and debug symbols and code, optimized for performance. de
...execution with line number information and D_ASSERT lines are still checked in this build. Binaries of this build mode are significantly faster than those of the debug mode. reldebug This build is similar to relassert in many ways, only assertions are also st... - https://man.mtool.work/duckdb/2.0/dev/building/build_configuration.html
- Environment 9035
- The environment where DuckDB is run has an obvious impact on performance. This page focuses on the effects of the hardware configuration and the operating system used. Hardware Configuration CPU DuckDB's officially supported architectures a
...c We distribute DuckDB builds with both glibc and musl libc . However, note that DuckDB binaries built with musl libc have significantly lower performance. In practice, this can lead to a slowdown of more than 5× on compute-intensive workloads. Therefore, it'... - https://man.mtool.work/duckdb/2.0/guides/performance/environment.html
- Schema 9035
- Types It is important to use the correct type for encoding columns (e.g., BIGINT , DATE , DATETIME ). While it is always possible to use string types ( VARCHAR , etc.) to encode more specific values, this is not recommended. Strings use mor
...all columns with the VARCHAR type. While the results of the queries are the same for both experiments, their runtimes vary significantly. The results below show that joining on BIGINT columns is approx. 1.8× faster than performing the same join on VARCHAR -ty... - https://man.mtool.work/duckdb/2.0/guides/performance/schema.html
- SELECT Statement 9035
- The SELECT statement retrieves rows from the database. Examples Select all columns from the table tbl : SELECT * FROM tbl; Select the rows from tbl : SELECT j FROM tbl WHERE i = 3; Perform an aggregate grouped by the column i : SELECT i, su
...ide a subquery node. SAMPLE Clause The SAMPLE clause allows you to run the query on a sample from the base table. This can significantly speed up processing of queries, at the expense of accuracy in the result. Samples can also be used to quickly see a snapsh... - https://man.mtool.work/duckdb/2.0/sql/statements/select.html
- I/O 8483
- Warning DuckDB ships with safe defaults. The configuration options described on this page are advanced options, so proceed with caution when changing them. Asynchronous I/O New Asynchronous I/O was introduced in DuckDB 2.0. Starting with v2
...the background and keeps multiple requests in flight while worker threads process data that has already arrived. This can significantly speed up queries when synchronous I/O does not saturate the available bandwidth, e.g., when reading data from object stora... - https://man.mtool.work/duckdb/2.0/data/io.html
- File Formats 8483
- Handling Parquet Files DuckDB has advanced support for Parquet files, which includes directly querying Parquet files . When deciding on whether to query these files directly or to first load them to the database, you need to consider severa
...r=true, columns={'hello': 'BIGINT', 'world': 'VARCHAR'}); Reducing Storage Size with DuckLake Storing data in DuckLake can significantly reduce storage size compared to CSV files or even DuckDB's native format, because DuckLake writes the data as Parquet file... - https://man.mtool.work/duckdb/2.0/guides/performance/file_formats.html
DuckDB 2.0 Documentation / man.mtool.work