Announcing DuckDB 0.8.0
The DuckDB team is happy to announce the latest DuckDB release (0.8.0). This release is named “Fulvigula” after the Mottled Duck (Anas Fulvigula) native to the Gulf of Mexico.
To install the new version, please visit the installation guide. The full release notes can be found here.
What’s new in 0.8.0
There have been too many changes to discuss them each in detail, but we would like to highlight several particularly exciting features!
- New pivot and unpivot statements
- Improvements to parallel data import/export
- Time series joins
- Recursive globbing
- Lazy-loading of storage metadata for faster startup times
- User-defined functions for Python
- Arrow Database Connectivity (ADBC) support
- New Swift integration
Below is a summary of those new features with examples, starting with two breaking changes in our SQL dialect that are designed to produce more intuitive results by default.
Breaking SQL Changes
This release includes two breaking changes to the SQL dialect: The division operator uses floating point division by default, and the default null sort order is changed from
NULLS FIRST to
NULLS LAST. While DuckDB is stil in Beta, we recognize that many DuckDB queries are already used in production. So, the old behavior can be restored using the following settings:
SET integer_division=true; SET default_null_order='nulls_first';
Division Operator. The division operator
/ will now always perform a floating point division even with integer parameters. The new operator
// retains the old semantics and can be used to perform integer division. This makes DuckDB’s division operator less error prone for beginners, and consistent with the division operator in Python 3 and other systems in the OLAP space like Spark, Snowflake and BigQuery.
SELECT 42 / 5, 42 // 5;
|(42 / 5)||(42 // 5)|
Default Null Sort Order. The default null sort order is changed from
NULLS FIRST to
NULLS LAST. The reason for this change is that
NULLS LAST sort-order is more intuitive when combined with
NULLS FIRST, Top-N queries always return the
NULL values first. With
NULLS LAST, the actual Top-N values are returned instead.
CREATE TABLE bigdata(col INTEGER); INSERT INTO bigdata VALUES (NULL), (42), (NULL), (43); FROM bigdata ORDER BY col DESC LIMIT 3;
New SQL Features
Pivot and Unpivot. There are many shapes and sizes of data, and we do not always have control over the process in which data is generated. While SQL is well-suited for reshaping datasets, turning columns into rows or rows into columns is tedious in vanilla SQL. With this release, DuckDB introduces the
UNPIVOT statements that allow reshaping data sets so that rows are turned into columns or vice versa. A key advantage of DuckDB’s syntax is that the column names to pivot or unpivot can be automatically deduced. Here is a short example:
CREATE TABLE sales(year INT, amount INT); INSERT INTO sales VALUES (2021, 42), (2022, 100), (2021, 42); PIVOT sales ON year USING SUM(amount);
The documentation contains more examples.
ASOF Joins for Time Series. When joining time series data with background fact tables, the timestamps often do not exactly match. In this case it is often desirable to join rows so that the timestamp is joined with the nearest timestamp. The ASOF join can be used for this purpose - it performs a fuzzy join to find the closest join partner for each row instead of requiring an exact match.
CREATE TABLE a(ts TIMESTAMP); CREATE TABLE b(ts TIMESTAMP); INSERT INTO a VALUES (TIMESTAMP '2023-05-15 10:31:00'), (TIMESTAMP '2023-05-15 11:31:00'); INSERT INTO b VALUES (TIMESTAMP '2023-05-15 10:30:00'), (TIMESTAMP '2023-05-15 11:30:00'); FROM a ASOF JOIN b ON a.ts >= b.ts;
|2023-05-15 10:31:00||2023-05-15 10:30:00|
|2023-05-15 11:31:00||2023-05-15 11:30:00|
Please refer to the documentation for a more in-depth explanation.
Data Integration Improvements
Default Parallel CSV Reader. In this release, the parallel CSV reader has been vastly improved and is now the default CSV reader. We would like to thank everyone that has tried out the experimental reader for their valuable feedback and reports. The
experimental_parallel_csv flag has been deprecated and is no longer required. The parallel CSV reader enables much more efficient reading of large CSV files.
CREATE TABLE lineitem AS FROM lineitem.csv;
Parallel Parquet, CSV and JSON Writing. This release includes support for parallel order-preserving writing of Parquet, CSV and JSON files. As a result, writing to these file formats is parallel by default, also without disabling insertion order preservation, and writing to these formats is greatly sped up.
COPY lineitem TO 'lineitem.csv'; COPY lineitem TO 'lineitem.parquet'; COPY lineitem TO 'lineitem.json';
Recursive File Globbing using
**. This release adds support for recursive globbing where an arbitrary number of subdirectories can be matched using the
** operator (double-star).
The documentation has been updated with various examples of this syntax.
Lazy-Loading Table Metadata. DuckDB’s internal storage format stores metadata for every row group in a table, such as min-max indices and where in the file every row group is stored. In the past, DuckDB would load this metadata immediately once the database was opened. However, once the data gets very big, the metadata can also get quite large, leading to a noticeable delay on database startup. In this release, we have optimized the metadata handling of DuckDB to only read table metadata as its being accessed. As a result, startup is near-instantaneous even for large databases, and metadata is only loaded for columns that are actually used in queries. The benchmarks below are for a database file containing a single large TPC-H
lineitem table (120x SF1) with ~770 million rows and 16 columns:
|FROM lineitem LIMIT 1;||1.62s||0.32s||0.03s||0.27s|
User-Defined Scalar Functions for Python. Arbitrary Python functions can now be registered as scalar functions within SQL queries. This will only work when using DuckDB from Python, because it uses the actual Python runtime that DuckDB is running within. While plain Python values can be passed to the function, there is also a vectorized variant that uses PyArrow under the hood for higher efficiency and better parallelism.
import duckdb from duckdb.typing import * from faker import Faker def random_date(): fake = Faker() return fake.date_between() duckdb.create_function('random_date', random_date, , DATE) res = duckdb.sql('select random_date()').fetchall() print(res) # [(datetime.date(2019, 5, 15),)]
See the documentation for more information.
Arrow Database Connectivity Support (ADBC). ADBC is a database API standard for database access libraries that uses Apache Arrow to transfer query result sets and to ingest data. Using Arrow for this is particularly beneficial for columnar data management systems which traditionally suffered a performance hit by emulating row-based APIs such as JDBC/ODBC. From this release, DuckDB natively supports ADBC. We’re happy to be one of the first systems to offer native support, and DuckDB’s in-process design fits nicely with ADBC.
Swift Integration. DuckDB has gained another official language integration: Swift. Swift is a language developed by Apple that most notably is used to create Apps for Apple devices, but also increasingly used for server-side development. The DuckDB Swift API allows developers on all swift platforms to harness DuckDB using a native Swift interface with support for Swift features like strong typing and concurrency.
The full release notes can be found on Github. We would like to thank all of the contributors for their hard work on improving DuckDB.back to news archive