Databend v1.1.0 was officially released on April 14, 2023! This release marks the first major update of Databend since version 1.0. In comparison to v1.0.0, we have made over 1,616 commits, 505 optimizations and fixes, 2,069 changed files with 118,455 additions and 42,100 deletions in v1.1.0. We would like to thank all of our community partners who participated and ev*eryone who helped make Databend better!
In version 1.1.0, we introduced new data transformation capabilities to the
COPY INTO command, allowing users to easily manipulate data during the loading process. Additionally, we have made significant enhancements to Databend, enabling it to run all TPC-DS queries successfully, along with other major performance optimizations and feature enhancements.
New Features and Enhancements
Discover Databend's new features and find your perfect fit with a quick overview.
COPY INTO now has ETL capabilities
COPY INTO is an important path for Databend to import data across multiple clouds. Now it also has basic data transformation capabilities, eliminating the need for temporary tables to store pre-transformed data. This includes support for column reordering, column omission, and transformation queries based on SELECT, enhancing its data manipulation capabilities.
CREATE TABLE my_table(id int, name string, time date);
COPY INTO my_table
FROM (SELECT t.id, t.name, to_date(t.timestamp) FROM @mystage t)
FILE_FORMAT = (type = parquet) PATTERN='.*parquet';
Full Support for TPC-DS Queries
Databend now supports all 99 TPC-DS queries!
TPC-DS is a decision support benchmark that includes multidimensional general application models for decision support systems. It models several universally applicable aspects of decision support systems, including queries and data maintenance. TPC-DS is widely used to measure the performance of decision support and analysis systems.
Databend now supports using REPLACE INTO statements to insert or update data.
This statement allows you to specify a conflict key to determine whether to insert a new row of data or update an existing row of data. If a row with the same conflict key already exists in the table, Databend will update this row with new data. Otherwise, new data will be added as a new record to the table.
You can use this statement to easily synchronize data from different sources or process duplicate records.
#> CREATE TABLE employees(id INT, name VARCHAR, salary INT);
#> REPLACE INTO employees (id, name, salary) ON (id) VALUES (1, 'John Doe', 50000);
#> SELECT * FROM Employees;
| id | name | salary |
| 1 | John Doe | 50000 |
For each input row, a window function returns one output row that depends on the specific row passed to the function and the values of the other rows in the window. In analytical queries such as reports, window functions can elegantly express certain requirements and play an irreplaceable role.
-- use aggrerate window function
SELECT date, AVG(amount) over (partition by date)
Aggregate window functions can apply aggregate operations to each row of data in the window. All aggregate functions supported by Databend can be used as aggregate window functions.
Databend's ecosystem has been further improved. It's time to introduce Databend into your data insight workflow!
Integration with Visualization Tools
Metabase, Redash and Grafana are all open source visualization tools that can query data from multiple sources and visualize it.
Databend now provides support for the above three tools. You can use Databend as a data source to easily build visualization dashboards and better understand and analyze your data.
- Docs - Data Visualization | Metabase
- Docs - Data Visualization | Redash
- Docs - Data Visualization | Connecting Databend With Grafana
Support for Rust
In addition to supporting Python, Go, and Java, Databend now also has its own Rust driver. This means you can easily connect to Databend and execute SQL queries using Rust.
let dsn = "databend://root:@localhost:8000/default?sslmode=disable";
let conn = new_connection(dsn).unwrap();
let sql_create = "CREATE TABLE books (
Databend x AI
When a cloud data warehouse intersects with cutting-edge AI models, what can you anticipate? Databend and OpenAI now work together to make you more productive.
Databend now has a built-in practical AI function
ai_to_sql, which supports converting natural language into SQL statements, making it easy to write high-quality SQL for complex analysis tasks.
SELECT * FROM ai_to_sql(
'List the total amount spent by users from the USA who are older than 30 years, grouped by their names, along with the number of orders they made in 2022');
In addition, Databend also supports text embedding generation, similarity retrieval, text completion and other capabilities, one-stop easy to build SQL-based queries, AI-enabled productivity tools.
SELECT doc_id, text_content, cosine_distance(embedding, ai_embedding_vector('What is a subfield of artificial intelligence?')) AS distance
ORDER BY distance ASC
SELECT ai_text_completion('Artificial intelligence is a fascinating field. What is a subfield of artificial intelligence?') AS completion;
Got a question? AskBend! Databend now has an AskBend knowledge base Q&A system that can answer questions about Databend.
P.S. AskBend is open source and powered by Databend Cloud and AI Functions. You can also create and deploy an intelligent assistant like AskBend with your own markdown files.
Download Databend v1.1.0
If you are interested in Databend v1.1.0, go to https://github.com/datafuselabs/databend/releases/tag/v1.1.0-nightly to view all changelogs or download the release.
If you are using an old version of Databend, we recommend you upgrade to the latest version. For the upgrade process, please refer to: https://databend.rs/doc/operations/upgrade .
If you need any help when working with the new release, submit an issue on GitHub Issue.
🎉 Contributors 35 contributors
Thanks a lot to the contributors for their excellent work.
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