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- Import Data 11874
- Overview The JDBC driver offers two ways to load data into DuckDB in bulk: the high-performance Appender and a JDBC batch writer. This page describes both and when to use each. Appender The Appender is available in the DuckDB JDBC driver vi
...hort where possible. DuckDB stores strings of up to 12 bytes inline; longer strings are stored out of line and add a small overhead per value. Single-Value Appender (Legacy) An earlier appender, org.duckdb.DuckDBSingleValueAppender , created with DuckDBC...ter The DuckDB JDBC driver offers batch write functionality. The batch writer supports prepared statements to mitigate the overhead of query parsing. The preferred method for bulk inserts is to use the Appender due to its higher performance. However, whe... - https://man.mtool.work/duckdb/2.0/clients/java/data_import.html
- INSERT Statements 11874
- 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
... - https://man.mtool.work/duckdb/2.0/data/insert.html
- Importing Data 8838
- The first step to using a database system is to insert data into that system. DuckDB can directly connect to many popular data sources and offers several data ingestion methods that allow you to easily and efficiently fill up the database.
...e system. They are suitable for quick prototyping, but should be avoided for bulk loading as they have significant per-row overhead. INSERT INTO people VALUES (1, 'Mark'); For a more detailed description, see the page on the INSERT statement . File Loadi... - https://man.mtool.work/duckdb/2.0/data/overview.html
- Parquet Tips 8838
- 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
...ate pushdown to only scan the row groups whose metadata ranges match the WHERE clause of the query. However, there is some overhead associated with reading the metadata in each group. A good rule of thumb is to ensure that the number of row groups per fi... - https://man.mtool.work/duckdb/2.0/data/parquet/tips.html
- PEG Parser 8838
- New Starting with DuckDB 2.0, the PEG parser is the default parser. In earlier versions it was an experimental, opt-in feature enabled with enable_peg_parser() , which is no longer required or available. DuckDB uses a parser based on PEG (
...tial backtracking on deeply nested expressions. As a tradeoff, parsing uses additional memory and has a slight performance overhead. This is in the range of milliseconds and is thus negligible for analytical queries. Checking a Query The check_peg_parser... - https://man.mtool.work/duckdb/2.0/sql/peg_parser.html
- Handle Results 8079
- Overview Beyond the standard JDBC ResultSet , the DuckDB driver can hand results to Apache Arrow , stream large results instead of materializing them, and expose raw columnar data chunks. This page covers each of these result-handling optio
...org.duckdb.DuckDBChunkedResult class. This exposes the C API's duckdb_fetch_chunk function to Java and avoids the per-row overhead required by the JDBC ResultSet interface. Chunk contents are read through the same DuckDBDataChunkReader API that is used... - https://man.mtool.work/duckdb/2.0/clients/java/result_handling.html
- File Formats 7320
- 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
...all CSV Files The CSV reader runs the CSV sniffer on all files. For many small files, this may cause an unnecessarily high overhead. A potential optimization to speed this up is to turn the sniffer off. Assuming that all files have the same CSV dialect a... - https://man.mtool.work/duckdb/2.0/guides/performance/file_formats.html
- Tuning Workloads 6561
- DuckDB aims to perform well out of the box, so most workloads do not require any tuning. When tuning is worthwhile, there is no single configuration or magic number that is optimal for every workload: the right settings depend on what you a
...m best when reusing the same database connection many times. Disconnecting and reconnecting on every query will incur some overhead, which can reduce performance when running many small queries. DuckDB also caches some data and metadata in memory, and th... - https://man.mtool.work/duckdb/2.0/guides/performance/how_to_tune_workloads.html
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DuckDB 2.0 Documentation / man.mtool.work