Databend is a modern cloud data warehouse, serving your massive-scale analytics needs at low cost and complexity. Open source alternative to Snowflake. Also available in the cloud: https://app.databend.com .

What's New​

Check out what we've done this week to make Databend even better for you.

Accepted RFCs :flight_departure:​

• rfc: query result cache (#10014)

Features & Improvements ✨​

Planner

• support EXPLAIN ANALYZE statement to profile query execution (#10023)
• derive new filter and push down (#10021)

Query

• alter table add/drop column SQL support (#9851)
• new table function infer_schema (#9936)
• add privilege check for select (#9924)
• improve signed numeric keys (#9987)
• support to parse jwt metadata and add multiple identity issuer configuration (#9971)
• support create file format (#10009)

Storage

• adopt OpenDAL's native scan support (#9985)
• add drop_table_by_id api (#9990)

Expression

• add operation for decimal (#9926)

Functions

• support array_any function (#9953)
• support array_sort (#9941)

Sqllogictest

• add time travel test for alter table (#9939)

Code Refactoring 🎉​

Meta

• move application level types such as user/role/storage-config to crate common-meta/app (#9944)
• fix abuse of ErrorCode (#10056)

Query

• use transform_sort_merge use heap to sort blocks (#10047)

Storage

• introduction of FieldIndex and ColumnId types for clear differentiation of use (#10017)

Build/Testing/CI Infra Changes 🔌​

• run benchmark for clickbench result format (#10019)
• run benchmark both s3 & fs (#10050)

Bug Fixes 🔧​

Privilege

• add privileges on system.one to PUBLIC by default (#10040)

Catalog

• parts was not distributed evenly (#9951)

Planner

• type assertion failed on subquery (#9937)
• enable outer join to inner join optimization (#9943)
• fix limit pushdown outer join (#10043)

Query

• fix add column update bug (#10037)

Storage

• fix sub-column of added-tuple column return default 0 bug (#9973)
• new bloom filter that bind index with Column Id instead of column name (#10022)

What's On In Databend​

Stay connected with the latest news about Databend.

RFC: Query Result Cache​

Caching the results of queries against data that doesn't update frequently can greatly reduce response time. Once cached, the result will be returned in a much shorter time if you run the query again.

• Docs | RFC: Query Result Cache
• Tracking Issue | RFC: query result cache

How to Write a Scalar / Aggregate Function​

Did you know that you can enhance the power of Databend by creating your own scalar or aggregate functions? Fortunately, it's not a difficult task!

The following guides are intended for Rust developers and Databend users who want to create their own workflows. The guides provide step-by-step instructions on how to create and register your own functions using Rust, along with code snippets and examples of various types of functions to walk you through the process.

• Docs | How to Write a Scalar Function
• Docs | How to Write an Aggregate Function

Profile-Guided Optimization​

Profile-guided optimization (PGO) is a compiler optimization technique that collects execution data during the program runtime and allows for tailoring optimizations tailored to both hot and cold code paths.

In this blog, we'll guide you through the process of optimizing Databend binary builds using PGO. We'll use Databend's SQL logic tests as an example to illustrate the step-by-step procedure.

Please note that PGO always requires generating perf data using workloads that are statistically representative. However, there's no guarantee that performance will always improve. Decide whether to use it based on your actual needs.

Learn More

• Blog | Optimizing Databend Binary Builds with Profile-guided Optimization

What's Up Next​

We're always open to cutting-edge technologies and innovative ideas. You're more than welcome to join the community and bring them to Databend.

Restructure function-related documentation​

To make our documentation clearer and easier to understand, we plan to restructure our function-related documentation to follow the same format as DuckDB's documentation. This involves breaking down the task into smaller sub-tasks based on function categories, so that anyone who wants to help improve Databend's documentation can easily get involved.

Issue 10029: Tracking: re-org the functions doc

Please let us know if you're interested in contributing to this issue, or pick up a good first issue at https://link.databend.rs/i-m-feeling-lucky to get started.

Changelog​

You can check the changelog of Databend Nightly for details about our latest developments.

• v0.9.39-nightly
• v0.9.38-nightly
• v0.9.37-nightly
• v0.9.36-nightly
• v0.9.35-nightly
• v0.9.34-nightly
• v0.9.33-nightly
• v0.9.32-nightly
• v0.9.31-nightly

Contributors​

Thanks a lot to the contributors for their excellent work this week.

andylokandy	b41sh	Big-Wuu	BohuTANG	dantengsky	dependabot[bot]

drmingdrmer	everpcpc	flaneur2020	johnhaxx7	leiysky	lichuang

mergify[bot]	PsiACE	RinChanNOWWW	soyeric128	sundy-li	TCeason

wubx	Xuanwo	xudong963	xxchan	youngsofun	yufan022

zhang2014	ZhiHanZ	zhyass

Connect With Us​

We'd love to hear from you. Feel free to run the code and see if Databend works for you. Submit an issue with your problem if you need help.

DatafuseLabs Community is open to everyone who loves data warehouses. Please join the community and share your thoughts.

• Databend Official Website
• GitHub Discussions (Feature requests, bug reports, and contributions)
• Twitter (Stay in the know)
• Slack Channel (Chat with the community)

Recently someone in the community suggested that we try profile-guided optimization (#9387). Let's see how we can use Rust to build a PGO-optimized Databend!

Background​

Profile-guided Optimization is a compiler optimization technique, which collects typical execution data (possible branches) during program execution and then optimizes for inlining, conditional branches, machine code layout, register allocation, etc.

The reason to introduce this technique is that static analysis techniques only consider code performance improvements without actually executing the program. However, these optimizations may not be fully effective. In the absence of runtime information, the compiler cannot take into account the actual execution of the program.

PGO allows data to be collected based on application scenarios in a production environment, so the optimizer can optimize the speed for hot code paths and size for cold code paths and produce faster and smaller code for applications.

rustc supports PGO by building data collection into the binaries, then collecting perf data during runtime to prepare for the final compilation optimization. The implementation relies entirely on LLVM.

Workflow​

Follow the workflow below to generate a PGO-optimized program:

• Compile the program with instrumentation enabled.
• Run the instrumented program to generate a profraw file.
• Convert the .profraw file into a .profdata file using LLVM's llvm-profdata tool.
• Compile the program again with the profiling data.

Preparations​

The data collected during the run will be eventually converted with llvm-profdata. To do so, install the llvm-tools-preview component via rustup, or consider using the program provided by a recent LLVM or Clang version.

rustup component add llvm-tools-preview

After the installation, llvm-profdata may need to be added to the following PATH:

~/.rustup/toolchains/<toolchain>/lib/rustlib/<target-triple>/bin/

Step-By-Step​

The following procedure uses Databend's SQL logic tests for demonstration purposes only to help us understand how it works, so you may not get positive results for performance. Use a typical workload for your production environment.

The caveat, however, is that the sample of data fed to the program during the profiling stage must be statistically representative of the typical usage scenarios; otherwise, profile-guided feedback has the potential to harm the overall performance of the final build instead of improving it.

1. Make sure there is no left-over profiling data from previous runs.

   rm -rf /tmp/pgo-data
   

2. Build the instrumented binaries (with release profile), using the RUSTFLAGS environment variable in order to pass the PGO compiler flags to the compilation of all crates in the program.

   RUSTFLAGS="-Cprofile-generate=/tmp/pgo-data" \
       cargo build --release --target=x86_64-unknown-linux-gnu
   

3. Instrumented binaries were run with some typical workload and we strongly recommend using workload that is statistically representative of the real scenario. This example runs SQL logic tests for reference only.

• Start a stand-alone Databend via a script, or a Databend cluster. Note that a production environment is more likely to run in cluster mode.

• Import the dataset and run a typical query workload.

  BUILD_PROFILE=release ./scripts/ci/deploy/databend-query-standalone.sh
  ulimit -n 10000;ulimit -s 16384; cargo run -p sqllogictests --release -- --enable_sandbox --parallel 16 --no-fail-fast
  

4. Merge the .profraw files into a .profdata file with llvm-profdata.

   llvm-profdata merge -o /tmp/pgo-data/merged.profdata /tmp/pgo-data
   

5. Use the .profdata file for guiding optimizations. In fact, you can notice that both builds use the --release flag, because in an actual runtime case we always use the release build binary.

   RUSTFLAGS="-Cprofile-use=/tmp/pgo-data/merged.profdata -Cllvm-args=-pgo-warn-missing-function" \
       cargo build --release --target=x86_64-unknown-linux-gnu
   

6. Run the compiled program again with the previous workload and check the performance:

   BUILD_PROFILE=release ./scripts/ci/deploy/databend-query-standalone.sh
   ulimit -n 10000;ulimit -s 16384; cargo run -p sqllogictests --release -- --enable_sandbox --parallel 16 --no-fail-fast
   

References​

• https://en.wikipedia.org/wiki/Profile-guided_optimization
• https://doc.rust-lang.org/rustc/profile-guided-optimization.html
• https://clang.llvm.org/docs/UsersManual.html#profile-guided-optimization
• https://learn.microsoft.com/en-us/cpp/build/profile-guided-optimizations?view=msvc-170

Databend is a modern cloud data warehouse, serving your massive-scale analytics needs at low cost and complexity. Open source alternative to Snowflake. Also available in the cloud: https://app.databend.com .

What's New​

Check out what we've done this week to make Databend even better for you.

Features & Improvements ✨​

Meta

• add databend-meta config grpc_api_advertise_host (#9835)

AST

• select from stage with files/pattern (#9877)
• parse decimal type (#9894)

Expression

• add Decimal128 and Decimal256 type (#9856)

Functions

• support array_indexof (#9840)
• support array function array_unique, array_distinct (#9875)
• support array aggregate functions (#9903)

Query

• add column id in TableSchema; use column id instead of index when read and write data (#9623)
• support view in system.columns (#9853)

Storage

• ParquetTable support topk optimization (#9824)

Sqllogictest

• leverage sqllogictest to benchmark tpch (#9887)

Code Refactoring 🎉​

Meta

• remove obsolete meta service api read_msg() and write_msg() (#9891)
• simplify UserAPI and RoleAPI by introducing a method update_xx_with(id, f: FnOnce) (#9921)

Cluster

• split exchange source to reader and deserializer (#9805)
• split and eliminate the status for exchange transform and sink (#9910)

Functions

• rename some array functions add array_ prefix (#9886)

Query

• TableArgs preserve info of positioned and named args (#9917)

Storage

• ParquetTable list file in read_partition (#9871)

Build/Testing/CI Infra Changes 🔌​

• support for running benchmark on PRs (#9788)

Bug Fixes 🔧​

Functions

• fix nullable and or domain cal (#9928)

Planner

• fix slow planner when ndv error backtrace (#9876)
• fix order by contains aggregation function (#9879)
• prevent panic when delete with subquery (#9902)

Query

• fix insert default value datatype (#9816)

What's On In Databend​

Stay connected with the latest news about Databend.

Why You Should Try Sccache​

Sccache is a ccache-like project started by the Mozilla team, supporting C/CPP, Rust and other languages, and storing caches locally or in a cloud storage backend. The community first added native support for the Github Action Cache Service to Sccache in version 0.3.3, then improved the functionality in v0.4.0-pre.6 so that the production CI can now use it.

Now, opendal, open-sourced by Datafuse Labs, acts as a storage access layer for sccache to interface with various storage services (s3/gcs/azlob etc.).

Learn More

• Blog | Why You Should Try Sccache

What's Up Next​

We're always open to cutting-edge technologies and innovative ideas. You're more than welcome to join the community and bring them to Databend.

Try using build-info​

To get information about git commits, build options and credits, we now use vergen and cargo-license.

build-info can collect build-information of your Rust crate. It might be possible to use it to refactor the relevant logic in common-building.

pub struct BuildInfo {
    pub timestamp: DateTime<Utc>,
    pub profile: String,
    pub optimization_level: OptimizationLevel,
    pub crate_info: CrateInfo,
    pub compiler: CompilerInfo,
    pub version_control: Option<VersionControl>,
}

Issue 9874: Refactor: Try using build-info

Please let us know if you're interested in contributing to this issue, or pick up a good first issue at https://link.databend.rs/i-m-feeling-lucky to get started.

Changelog​

You can check the changelog of Databend Nightly for details about our latest developments.

• v0.9.30-nightly
• v0.9.29-nightly
• v0.9.28-nightly
• v0.9.27-nightly
• v0.9.26-nightly
• v0.9.25-nightly
• v0.9.24-nightly
• v0.9.23-nightly
• v0.9.22-nightly

Contributors​

Thanks a lot to the contributors for their excellent work this week.

andylokandy	ariesdevil	b41sh	BohuTANG	dependabot[bot]	drmingdrmer

everpcpc	flaneur2020	johnhaxx7	leiysky	lichuang	mergify[bot]

PsiACE	RinChanNOWWW	soyeric128	sundy-li	TCeason	Xuanwo

xudong963	youngsofun	zhang2014

Connect With Us​

We'd love to hear from you. Feel free to run the code and see if Databend works for you. Submit an issue with your problem if you need help.

DatafuseLabs Community is open to everyone who loves data warehouses. Please join the community and share your thoughts.

• Databend Official Website
• GitHub Discussions (Feature requests, bug reports, and contributions)
• Twitter (Stay in the know)
• Slack Channel (Chat with the community)

Sccache is a ccache-like project started by the Mozilla team, supporting C/CPP, Rust and other languages, and storing caches locally or in a cloud storage backend. In v0.3.3, Sccache added native support for the Github Action Cache Service; in the subsequent v0.4.0-pre.6, the community has continued to improve this functionality, and it is now ready for use in the production CI.

I recently added sccache to the PyO3/maturin CI for testing, and found it to have the following advantages

• Easy deployment configuration: No need to specify shared key, no need to worry about GHA's internal cache-from/cache-to logic, just configure SCCACHE_GHA_ENABLED: "true".
• Multi-language support: sccache supports caching different compilers for C/CPP, Rust and nvcc at the same time.
• Faster in most scenarios: sccache caches the compilation product, no need to load the entire cache in advance, and no need to upload the cached content after the build is complete.
• Concurrent job friendly: sccache can share caches between multiple concurrent jobs/workflows, no need to wait until the end of the build.
• No cache conflicts: sccache performs hash calculations on each build product input (parameters, environment variables, files, etc.) to build a global conflict-free cache without cache conflicts and without the need to specify additional different cache keys.
• No vendor lock-in: sccache is built on [opendal] (https://github.com/datafuselabs/opendal) and naturally supports a variety of different storage services, allowing seamless migration to s3/gcs/azlob etc. in future CI evolutions without relying on GHA cache services.
• Actively maintained: sccache is currently actively maintained by me, you can submit feedback directly if you encounter problems using it.

The following are the results of the maturin project tests.

The table compares the difference between a second/third run with sccache and a run with rust-cache, with bolded plural entries indicating that sccache is faster than rust-cache. As you can see, as the cache hit rate increases, sccache achieves a maximum improvement of almost 50% over rust-cache.

Give sccache a try, and if you don't like it, go to issues for feedback, and feel free to contribute!

In the next section I will first describe the internal API of the Github Action Cache Service and how it works, and then compare the differences between the rust-cache / sccache implementations to show why sccache is better / faster.

Github Action Cache Service Principle​

The Github Action Cache Service is essentially an immutable storage service that supports prefix queries and provides the following non-public API.

Query Cache​

GET /cache?keys=abc,ab,a&version=v1

• keys: Specify a comma-separated set of query keys, the result will be the latest key with the same version prefix match
• version：Specify the namespace used by the cache key

Reserve Cache​

POST /caches

• inputs: {key: <cache_key>, version: <cache_version>}
• outputs: {cache_id: <cache_id>}

After each set of (key, version) has been reserved, all subsequent requests with the same key & version will return an already exists error, meaning that the cache cannot be overwritten. A successful request will return a numeric cache_id, which will be used for subsequent uploads and cache creation.

Upload Cache​

PATCH /caches/[cache_id]

Upload specific cache content, using Content-Range to mark the cache location for this upload.

Create Cache​

POST /caches/[cache_id]

This API can be used to create a cache after all the cached content has been created. The cache will only be queried after a successful response from this API.

rust-cache implementation​

On top of this internal API (which is reasonably suspected to be provided by Azure DevOps services), Github provides actions/cache for users to use, and rust-cache is based on the @actions/cache implementation.

rust-cache calculates a cache key based on the github job_id, rustc version, environment variables, Cargo.lock, etc., and then packages ~/.cargo and . /target into one package and upload it. If the cache hits, it will be loaded locally and unpacked, if the cache misses, the cache will be uploaded in the post action.

The advantage of rust-cache is that the GHA Cache API is only called once for the whole process and the rate limit is rarely triggered.

sccache implementation​

The GHA implementation of sccache is completely file based.

sccache calculates a hash as a cache key based on the environment variables, binary, compilation parameters, input files, etc. passed during each rustc call, and loads the file directly from the storage service if it exists, skipping this compilation operation, otherwise it compiles and writes the result to the storage service. This means that

• sccache does not have to deal with cache conflicts caused by different inputs, and can use an always unique hash as the cache key.
• sccache can download the required cache at rustc compile time, without having to load the entire contents beforehand, or upload them all after the job has finished.
• Sccache's cache loading logic is not heavily dependent on Cargo.lock itself, so the cache can be reused even if a large number of dependencies change.
• sccache can reuse the cache between concurrent jobs, as they all share the same non-conflicting memory space.

In addition, sccache can be used for compilation caching of languages such as c/cpp, and if compilers such as gcc/clang are also used in the project, the cache can be shared with a simple configuration.

To help users make better use of Sccache in Github Action, we have developed sccache-action. However, my PR is not currently merged and can be used first with my fork:

- name: Sccache Setup
  # Just for test, come back to upstream after released
  uses: Xuanwo/sccache-action@c94e27bef21ab3fb4a5152c8a878c53262b4abb0
  with:
    version: "v0.4.0-pre.6"

Next, only two environment variables need to be configured.

env:
    SCCACHE_GHA_ENABLED: "true"
    RUSTC_WRAPPER: "sccache"

At the end of each Job, sccache-action outputs the usage of this Cache.

/opt/hostedtoolcache/sccache/0.4.0-pre.6/x64/sccache --show-stats
Compile requests                   1887
Compile requests executed          1035
Cache hits                          836
Cache hits (C/C++)                   22
Cache hits (Rust)                   814
Cache misses                        189
Cache misses (Rust)                 189
Cache timeouts                        0
Cache read errors                     0
Forced recaches                       0
Cache write errors                    0
Compilation failures                 10
Cache errors                          0
Non-cacheable compilations            0
Non-cacheable calls                 852
Non-compilation calls                 0
Unsupported compiler calls            0
Average cache write               0.051 s
Average compiler                  1.132 s
Average cache read hit            0.000 s
Failed distributed compilations       0

Non-cacheable reasons:
crate-type                          521
-                                   320
unknown source language              11

Cache location                  ghac, name: sccache-v0.4.0-pre.6, prefix: /sccache/

Users can use this information to adjust their strategy for using sccache.

Conclusion​

Sccache uses GHA Cache in a new way to accelerate the compilation of Rust projects, with the following advantages over existing solutions

• Easy deployment and configuration
• Multiple language support
• Faster in most scenarios
• Concurrent task friendly
• No cache conflicts
• No vendor lock-in
• Active maintenance

Feel free to try and use Sccache in your own projects!

Databend is a powerful cloud data warehouse. Built for elasticity and efficiency. Free and open. Also available in the cloud: https://app.databend.com .

What's New​

Check out what we've done this week to make Databend even better for you.

Features & Improvements ✨​

AST

• add syntax about parsing presign options with content type (#9771)

Format

• add TSV file format back (#9732)

Functions

• support array functions prepend and append (#9844)
• support array concat (#9804)

Query

• add topn runtime filter in native storage format (#9738)
• enable hashtable state pass from partial to final (#9809)

Storage

• add pruning stats to EXPLAIN (#9724)
• cache bloom index object (#9712)

Code Refactoring 🎉​

• 'select from stage' use ParquetTable (#9801)

Meta

• expose a single "kvapi" as public interface (#9791)
• do not remove the last node from a cluster (#9781)

AST/Expression/Planner

• unify Span and Result (#9713)

Executor

• merge simple pipe and resize pipe (#9782)

Bug Fixes 🔧​

Base

• fix not linux and macos jemalloc fallback to std (#9786)

Config

• fix table_meta_cache can't be disabled (#9767)

Meta

• when import data to meta-service dir, the specified "id" has to be one of the "initial_cluster" (#9755)

Query

• fix and refactor aggregator (#9748)
• fix memory leak for data port (#9762)
• fix panic when cast jsonb to string (#9813)

Storage

• fix up max_file_size may oom (#9740)

What's On In Databend​

Stay connected with the latest news about Databend.

DML Command - UPDATE​

Modifies rows in a table with new values.

Note: Databend guarantees data integrity. In Databend, Insert, Update, and Delete operations are guaranteed to be atomic, which means that all data in the operation must succeed or all must fail.

Syntax

UPDATE <table_name>
SET <col_name> = <value> [ , <col_name> = <value> , ... ]
    [ FROM <table_name> ]
    [ WHERE <condition> ]

Learn More

• Docs | DML Command - UPDATE

What's Up Next​

We're always open to cutting-edge technologies and innovative ideas. You're more than welcome to join the community and bring them to Databend.

Support Arrow Flight SQL Protocol​

Currently Databend supports the MySQL protocol, and it would be great if Databend could support the Arrow Flight SQL protocol as well.

Typically a lakehouse stores data in parquet files using the MySQL protocol while Databend has to do deserialization from parquet to arrow and then back to MySQL data types. Again on the caller end users use data frames or MySQL result iterators, which also requires serialization of types. With Arrow Flight SQL all of these back and forth serialization costs can be avoided.

Issue 9832: Feature: Support Arrow Flight SQL protocol

Please let us know if you're interested in contributing to this issue, or pick up a good first issue at https://link.databend.rs/i-m-feeling-lucky to get started.

Changelog​

You can check the changelog of Databend Nightly for details about our latest developments.

• v0.9.21-nightly
• v0.9.20-nightly
• v0.9.19-nightly
• v0.9.18-nightly
• v0.9.17-nightly
• v0.9.16-nightly
• v0.9.15-nightly

Contributors​

Thanks a lot to the contributors for their excellent work this week.

andylokandy	ariesdevil	b41sh	BohuTANG	dantengsky	dependabot[bot]

drmingdrmer	everpcpc	flaneur2020	johnhaxx7	leiysky	mergify[bot]

PsiACE	RinChanNOWWW	soyeric128	sundy-li	TCeason	Xuanwo

youngsofun	yufan022	zhang2014

Connect With Us​

We'd love to hear from you. Feel free to run the code and see if Databend works for you. Submit an issue with your problem if you need help.

DatafuseLabs Community is open to everyone who loves data warehouses. Please join the community and share your thoughts.

• Databend Official Website
• GitHub Discussions (Feature requests, bug reports, and contributions)
• Twitter (Stay in the know)
• Slack Channel (Chat with the community)

Databend v0.9.0 was officially released on January 13, 2023! This release is the last major release of Databend before version 1.0, and it is also the version in which we have refactored the core code the most so far. Compared to v0.8.0, we have made more than 5,000 commits, 700+ optimizations and fixes, 4,347 file changes, and 340,000 lines of code changes in v0.9.0. Thanks to all the community partners who participated and to everyone who made Databend better!

In v0.9.0, we introduced a new type system, a new expression calculation framework, JSONB support, complete JOIN support and optimization, CBO support, a native storage format and other major feature optimizations. A lot of optimization and enhancements have also been made in terms of performance, stability, and usability.

Performance comparison​

In the new version, we have made a lot of optimizations in the execution engine, optimizer, and storage layer. The efficiency gets doubled in most scenarios. The following is a performance comparison between v0.8 and v0.9 benchmarking the Hits dataset with the default FUSE engine on S3:

Brand new type system​

To give databend an easy-to-understand yet powerful type derivation system, we learned from the compiler internals of a number of good programming languages and then refined a subset of them for SQL. Based on the current purely static type system, we have a sophisticated type derivation mechanism that infers the execution of expressions as much as possible at the compile time of SQL, a minimalist expression function registration logic, and the implementation of generic derivation at the database type level.

On top of the new type system, modules such as constant folding, type derivation, function registration, query data trimming, etc. can benefit from the new type system.

Support for JSONB​

In the new version, we implemented the Rust version of JSONB, the default JSON data type will be stored in JSONB, and it is also compatible with the old JsonText format. Based on the binary JSON format, both storage space and query performance have been significantly optimized.

More info: https://databend.rs/doc/contributing/rfcs/json-optimization

Full support for JOINs​

Full Join types are supported: inner/natural/cross/outer/semi/anti joins. In the past few months, according to the feedback from the community and online users, hash join has been deeply optimized to meet the performance requirements in most scenarios.

Support for CBO​

We have added the statistical calculation logic of NDV in the statistical information, and users can now generate statistical information tables through the "Analyze" command similar to presto. JOINs can use the existing statistical information to optimize the logical plan based on cost. After the subsequent CBO support is improved, we will update the query performance data comparison of TPCH 100G data.

Native Storage Format​

Databend supports Git-Like's Fuse engine. Based on this engine, we can quickly go back to a certain historical point in time to query, and realize "time travel" inside the database. Inside the Fuse engine, we also support a new Storage Format besides Parquet --- strawboat: https://github.com/sundy-li/strawboat.

Strawboat is based on Arrow's native storage format. Based on it, we can read data more efficiently than Parquet. In the hits dataset, the full table scan native format can be 2-3 times faster. In the hits data set, a very considerable improvement has been achieved in the local deployment scenario, and we will improve the performance comparison in clickbench later.

Efficient bloom filter filtering​

In the new version, we introduced the xor filter to calculate and store the bloom filter for each column. Compared with the previous version, the new bloom filter has improved a lot in import query performance and occupied space. See https://databend.rs/blog/xor-filter

Designing and open-sourcing serverless DataSharing protocol​

In the new version we created a zero-trust data sharing solution among multi-tenants based on object storage presign short-term access token. In the case of consistent basic performance, aws lambda will be used to implement data sharing in a serverless manner.

Stage​

We implemented UserStage in the new version, similar to the home directory of linux: COPY INTO my_table FROM @~;

• Stage's data import supports meta storage status, which means we can always save and import new files from stage.
• Support exporting multiple files in different formats from Stage.
• Importing tables from Stage supports parallelization.

Other improvements​

The new release also includes these improvements:

• Read_parquet of duckdb supports reading local parquet files directly without importing
• Commonly used function performance optimization, commonly used GEO function support
• Distinct performance optimization
• Adaptive String HashTable
• SQLancer integration
• Parquet reading acceleration
• The previous python version sqllogictest was rewritten to use Rust
• NDJSON and JSON output format support
• ALTER TABLE supports recluster
• Support hyperloglog update and delete: https://db.in.tum.de/~freitag/papers/p23-freitag-cidr19.pdf

Download v0.9.0​

If you are interested in Databend v0.9.0, go to https://github.com/datafuselabs/databend/releases/tag/v0.9.0-nightly to view all changelogs or download the release.

If you are using an old version of Databend, you can directly upgrade to the new version. For the upgrade process, please refer to: https://databend.rs/doc/operations/upgrade

Feedback​

If you need any help when working with the new release, submit an issue on Github Issue. GitHub: https://github.com/datafuselabs/databend/

Databend is a powerful cloud data warehouse. Built for elasticity and efficiency. Free and open. Also available in the cloud: https://app.databend.com .

What's New​

Check out what we've done this week to make Databend even better for you.

Features & Improvements ✨​

SQL

• eliminate extra group by scalars (#9708)

Query

• add privilege check for insert/delete/optimize (#9664)
• enable empty projection (#9675)
• add aggregate limit in final aggregate stage (#9716)
• add optional column names to create/alter view statement (#9715)

Storage

• add prewhere support in native storage format (#9600)

Code Refactoring 🎉​

IO

• move io constants to common/io (#9700)
• refine fuse/io/read (#9711)

Planner

• rename Scalar to ScalarExpr (#9665)

Storage

• refactor cache layer (#9672)
• pruner.rs -> fuse_bloom_pruner.rs (#9710)
• make pruner hierarchy to chain (#9714)

Build/Testing/CI Infra Changes 🔌​

• support setup minio storage & external s3 storage in docker image (#9676)

Bug Fixes 🔧​

Expression

• fix missing simple_cast (#9671)

Query

• fix efficiently_memory_final_aggregator result is not stable (#9685)
• fix max_result_rows only limit output results nums (#9661)
• fix query hang in two level aggregator (#9694)

Storage

• may get wrong datablocks if not sorted by output schema (#9470)
• bloom filter is using wrong cache key (#9706)

What's On In Databend​

Stay connected with the latest news about Databend.

Databend All-in-One Docker Image​

Databend Docker Image now supports setting up MinIO storage and external AWS S3 storage.

Now you can easily use a Docker image for your first experiment with Databend.

Run with MinIO as backend

docker run \
    -p 8000:8000 \
    -p 9000:9000 \
    -e MINIO_ENABLED=true \
    datafuselabs/databend

Run with self managed query config

docker run \
    -p 8000:8000 \
    -e DATABEND_QUERY_CONFIG_FILE=/etc/databend/mine.toml \
    -v query_config_file:/etc/databend/mine.toml \
    datafuselabs/databend

Learn More

• PR | ci: support setup minio storage & external s3 storage in docker image
• Docker Hub | Databend All-in-One Docker Image

What's Up Next​

We're always open to cutting-edge technologies and innovative ideas. You're more than welcome to join the community and bring them to Databend.

Vector Search​

Vector search captures the meaning and context of unstructured data, and is commonly used for text or image processing, enabling the use of semantics to find similar results and obtain more valid results than traditional keyword retrieval.

Databend plans to provide users with a richer and more efficient means of querying by supporting vector search, and the introduction of Faiss Index may be an initial solution.

Issue 9699: feat: vector search (Faiss index)

Please let us know if you're interested in contributing to this issue, or pick up a good first issue at https://link.databend.rs/i-m-feeling-lucky to get started.

Changelog​

You can check the changelog of Databend Nightly for details about our latest developments.

• v0.9.14-nightly
• v0.9.13-nightly
• v0.9.12-nightly
• v0.9.11-nightly
• v0.9.10-nightly
• v0.9.09-nightly
• v0.9.08-nightly
• v0.9.07-nightly

Contributors​

Thanks a lot to the contributors for their excellent work this week.

andylokandy	ariesdevil	b41sh	BohuTANG	dantengsky	dependabot[bot]

everpcpc	flaneur2020	johnhaxx7	leiysky	mergify[bot]	PsiACE

RinChanNOWWW	sandflee	sundy-li	xudong963	zhang2014	zhyass

Connect With Us​

We'd love to hear from you. Feel free to run the code and see if Databend works for you. Submit an issue with your problem if you need help.

DatafuseLabs Community is open to everyone who loves data warehouses. Please join the community and share your thoughts.

• Databend Official Website
• GitHub Discussions (Feature requests, bug reports, and contributions)
• Twitter (Stay in the know)
• Slack Channel (Chat with the community)

Databend is a powerful cloud data warehouse. Built for elasticity and efficiency. Free and open. Also available in the cloud: https://app.databend.com .

What's New​

Check out what we've done this week to make Databend even better for you.

Features & Improvements ✨​

Meta

• use expression::TableSchema to replace obsolete datavalues::DataSchema (#9506)
• iter() iterate every tree and every records in theses trees (#9621)

Expression

• add other base geo functions (#9588)

Optimizer

• improve cardinality estimation for join based on histogram (#9594)

Planner

• improve join reorder algorithm (#9571)

Query

• support insert with placeholder (#9575)
• set setting support expr (#9574)
• add information_schema for sharding-jdbc (#9583)
• support named params for table functions (#9630)

Storage

• read_parquet page index (#9563)
• update interpreter and storage support (#9261)

Code Refactoring 🎉​

• refine on_error mode (#9473)

Meta

• remove unused meta types and conversion util (#9584)

Parser

• more strict parser for format_options (#9635)

Expression

• rearrange common_expression and common_function (#9585)

Build/Testing/CI Infra Changes 🔌​

• run sqllogictests with binary (#9603)

Bug Fixes 🔧​

Expression

• constant folder should run repeatedly until stable (#9572)
• check_date() and to_string(boolean) may panic (#9561)

Planner

• fix stack overflow when applying RuleFilterPushDownJoin (#9645)

Storage

• fix range filter read stat with index (#9619)

Sqllogictest

• sqllogic test hangs (cluster mod + clickhouse handler) (#9615)

What's On In Databend​

Stay connected with the latest news about Databend.

Upgrade Databend Query from 0.8 to 0.9​

Databend-query-0.9 introduces incompatible changes in metadata, these metadata has to be manually migrated. Databend provides a program for this job: databend-meta-upgrade-09, which you can find in a release package or can be built from source.

Upgrade

databend-meta-upgrade-09 --cmd upgrade --raft-dir "<./your/raft-dir/>"

Learn More

• PR | feat(meta/upgrade): meta data upgrade program
• Doc | Upgrade Databend Query from 0.8 to 0.9

Release Proposal: Nightly v1.0​

The call for proposals for the release of v1.0 is now open.

The preliminary plan is to release in March, mainly focusing on alter table, update, and group by spill.

Learn More

• Release proposal: Nightly v1.0

What's Up Next​

We're always open to cutting-edge technologies and innovative ideas. You're more than welcome to join the community and bring them to Databend.

Add Type Checker for Sqllogictest​

We can check if each row's each element's type is correct.

databend/tests/sqllogictests/src/client/mysql_client.rs

 // Todo: add types to compare 
 Ok(DBOutput::Rows { 
     types, 
     rows: parsed_rows, 

Issue 9647: Feature: Add type checker for sqllogictest

Please let us know if you're interested in contributing to this issue, or pick up a good first issue at https://link.databend.rs/i-m-feeling-lucky to get started.

Changelog​

You can check the changelog of Databend Nightly for details about our latest developments.

• v0.9.6-nightly
• v0.9.5-nightly
• v0.9.4-nightly
• v0.9.3-nightly
• v0.9.2-nightly
• v0.9.1-nightly
• v0.9.0-nightly

We're gearing up for the v0.9 release of Databend. Stay tuned.

Contributors​

Thanks a lot to the contributors for their excellent work this week.

andylokandy	ariesdevil	b41sh	BohuTANG	dantengsky	drmingdrmer

everpcpc	leiysky	mergify[bot]	PsiACE	RinChanNOWWW	soyeric128

sundy-li	TCeason	Xuanwo	xudong963	youngsofun	yufan022

zhang2014	zhyass

Connect With Us​

We'd love to hear from you. Feel free to run the code and see if Databend works for you. Submit an issue with your problem if you need help.

DatafuseLabs Community is open to everyone who loves data warehouses. Please join the community and share your thoughts.

• Databend Official Website
• GitHub Discussions (Feature requests, bug reports, and contributions)
• Twitter (Stay in the know)
• Slack Channel (Chat with the community)

Databend is a powerful cloud data warehouse. Built for elasticity and efficiency. Free and open. Also available in the cloud: https://app.databend.com .

What's New​

Check out what we've done this week to make Databend even better for you.

Features & Improvements ✨​

Meta

• add reader-min-msg-ver and msg-min-reader-ver in proto-conv (#9535)

Planner

• support tuple.1 and get(1)(tuple) (#9493)
• support display estimated rows in EXPLAIN (#9528)

Query

• efficiently memory two level group by in standalone mode (#9504)

Storage

• support nested type in read_parquet (#9486)
• add build options table (#9502)

Code Refactoring 🎉​

• merge new expression (#9411)
• remove and rename crates (#9481)
• bump rust version (#9540)

Expression

• move negative functions to binder (#9484)
• use error_to_null() to eval try_cast (#9545)

Functions

• replace h3ron to h3o (#9553)

Format

• extract AligningStateTextBased (#9472)
• richer error context (#9534)

Query

• use ctx to store the function evaluation error (#9501)
• refactor map access to support view read tuple inner (#9516)

Storage

• bump opendal for streaming read support (#9503)
• refactor bloom index to use vectorized siphash function (#9542)

Bug Fixes 🔧​

HashTable

• fix memory leak for unsized hash table (#9551)

Storage

• fix row group stats collection (#9537)

What's On In Databend​

Stay connected with the latest news about Databend.

New Year, New Expression!​

We're so thrilled to tell you that Databend now fully works with New Expression after more than a half year of dedicated work. New Expression introduces a formal type system to Databend and supports type-safe downward casting , making the definition of functions easier.

New Expression is still being tuned, and a new version (v0.9) of Databend will be released once the tuning work is complete.

Learn More

• PR | refactor: Merge new expression
• Issue | Tracking: issues about new expression

What's Up Next​

We're always open to cutting-edge technologies and innovative ideas. You're more than welcome to join the community and bring them to Databend.

UNNEST Function​

The UNNEST function takes an array as a parameter, and returns a table containing each element of the array in a row.

Syntax

UNNEST(ARRAY) [WITH OFFSET]

If you're interested in becoming a contributor, helping us develop the UNNEST function would be a good start.

Issue 9549: Feature: Support unnest

Please let us know if you're interested in contributing to this issue, or pick up a good first issue at https://link.databend.rs/i-m-feeling-lucky to get started.

Changelog​

You can check the changelog of Databend Nightly for details about our latest developments.

• v0.8.177-nightly
• v0.8.176-patch1

We're gearing up for the v0.9 release of Databend. Stay tuned.

Contributors​

Thanks a lot to the contributors for their excellent work this week.

andylokandy	ariesdevil	b41sh	BohuTANG	ClSlaid	dantengsky

dependabot[bot]	drmingdrmer	everpcpc	flaneur2020	leiysky	mergify[bot]

PsiACE	RinChanNOWWW	soyeric128	sundy-li	TCeason	wubx

Xuanwo	xudong963	youngsofun	zhang2014

Connect With Us​

We'd love to hear from you. Feel free to run the code and see if Databend works for you. Submit an issue with your problem if you need help.

DatafuseLabs Community is open to everyone who loves data warehouses. Please join the community and share your thoughts.

• Databend Official Website
• GitHub Discussions (Feature requests, bug reports, and contributions)
• Twitter (Stay in the know)
• Slack Channel (Chat with the community)

Databend is a powerful cloud data warehouse. Built for elasticity and efficiency. Free and open. Also available in the cloud: https://app.databend.com .

What's New​

Check out what we've done this week to make Databend even better for you.

Features & Improvements ✨​

Format

• basic output format JSON (#9447)

Query

• check connection params (#9437)
• add max_query_row_nums (#9406)

Storage

• support prewhere in hive (#9427)
• add generic cache trait for different object reader (#9436)
• add metrics for new cache (#9445)

New Expression

• migrate hash func to func-v2 (#9402)

Sqllogictest

• run all tests in parallel (#9400)

Code Refactoring 🎉​

Storage

• add to_bytes and from_bytes for CachedObject (#9439)
• better table-meta and parquet reader function (#9434)
• convert fuse_snapshot unit tests to sqlloigc test (#9428)

Bug Fixes 🔧​

Format

• catch unwind when read split (#9420)

User

• lazy load JWKS (#9446)

Planner

• create Stage URL's path should ends with / (#9450)

What's On In Databend​

Stay connected with the latest news about Databend.

Databend 2022 Recap​

Let's look back and see how Databend did in 2022.

• Open source: got 2,000+ stars, merged 2,400+ PRs, and solved 1,900 issues.
• From data warehouse to lakehouse: Brand-new design with enhanced capabilities.
• Rigorous testing: SQL Logic Tests, SQLancer, and https://perf.databend.rs.
• Building the ecosystem: More customers chose, trusted, and grew with Databend, including Kuaishou and SAP.
• Databend Cloud: Built on top of Databend, the next big data analytics platform.

We wish everyone a Happy New Year and look forward to engaging with you.

Learn More

• Blog | Databend 2022 Recap

Databend 2023 Roadmap​

As the new year approaches, Databend is also actively planning its roadmap for 2023.

We will continue to polish the Planner and work on data and query caching. Enhancing storage and query issues for PB-level data volumes is also on our list.

Try Databend and join the roadmap discussion.

Learn More

• Issue | Roadmap 2023

What's Up Next​

We're always open to cutting-edge technologies and innovative ideas. You're more than welcome to join the community and bring them to Databend.

Profile-Guided Optimization (PGO)​

The basic concept of PGO is to collect data about the typical execution of a program (e.g. which branches it is likely to take) and then use this data to inform optimizations such as inlining, machine-code layout, register allocation, etc.

rustc supports doing profile-guided optimization (PGO). We expect to be able to use it to enhance the build.

Issue 9387: Feature: Add PGO Support

Please let us know if you're interested in contributing to this issue, or pick up a good first issue at https://link.databend.rs/i-m-feeling-lucky to get started.

Changelog​

You can check the changelog of Databend Nightly for details about our latest developments.

• v0.8.176-nightly
• v0.8.175-nightly
• v0.8.174-nightly
• v0.8.173-nightly
• v0.8.172-nightly
• v0.8.171-nightly
• v0.8.170-nightly
• v0.8.169-nightly
• v0.8.168-nightly

Contributors​

Thanks a lot to the contributors for their excellent work this week.

ariesdevil	BohuTANG	dantengsky	dependabot[bot]	everpcpc	flaneur2020

hantmac	leiysky	mergify[bot]	PsiACE	sandflee	soyeric128

sundy-li	TCeason	Xuanwo	xudong963	youngsofun	zhang2014

Connect With Us​

We'd love to hear from you. Feel free to run the code and see if Databend works for you. Submit an issue with your problem if you need help.

DatafuseLabs Community is open to everyone who loves data warehouses. Please join the community and share your thoughts.

• Databend Official Website
• GitHub Discussions (Feature requests, bug reports, and contributions)
• Twitter (Stay in the know)
• Slack Channel (Chat with the community)

The year is coming to an end, and Databend is about to enter its third year. Before we count down the new year, it's a good idea to look back and see how Databend did in 2022.

Open Source: Receiving Increased Attention​

Databend is a powerful cloud data warehouse. Built for elasticity and efficiency. Free and open. Also available in the cloud: https://app.databend.com .

The open-source philosophy has guided Databend from the very beginning. The entire team works seamlessly on GitHub where the Rust community and many data pros are fully involved. In 2022, the Databend repository:

• Got 2,000+ stars, totaling 5,000 .
• Merged 2,400+ PRs, totaling 5,600 .
• Solved 1,900 issues, totaling 3,000 .
• Received 16,000 commits, totaling 23,000 .
• Attracted more contributors, totaling 138 .

Development: Inspired by Real Scenarios​

Databend brought many new features and improvements in 2022 to help customers with their real work scenarios.

Brand-New Data Warehouse​

As a data warehouse inspired by and benchmarking itself against Snowflake and Clickhouse, Databend fully took advantage of "Cloud Native" to bring you a new design and implementation without breaking the balance between performance and maintainability:

• Added support for Stage and Data Sharing, helping users manage their data life cycle with more options.
• Introduced a new planner with user-friendly error prompts and efficient optimization techniques for the execution plan.
• Redesigned the type system to support type checking and type-safe downward casting.
• Enhanced the new processor framework: It can now work in both Pull and Push modes.
• Added experimental support for Native Format to improve performance when running on a local disk.

Databend as Lakehouse​

Storing and managing massive data is key to our vision "Databend as Lakehouse" . A lot of efforts have been made in 2022 for a larger data payload and a wider range of accepted data sources:

• Adopted OpenDAL in the data access layer as a unified interface.
• Expanded support for structured and semi-structured data.
• Added the ability to keep multiple catalogs: This makes integrations with custom catalogs such as Hive much easier.
• Added the ability to query data directly from a local, staged, or remote file.

Optimal Efficiency Ratio​

After a year of continuous tuning, we brought Databend to a new stage featuring elastic scheduling and separating storage from compute. We're thrilled to see a significant improvement in the efficiency ratio:

• In some scenarios, Databend works as efficiently as Clickhouse.
• Lowered costs by 90% compared to Elasticsearch, and by 30% compared to Clickhouse.

Testing: Put Us at Ease​

Comprehensive tests help make a database management system robust. While optimizing performance, we also care about the accuracy and reproducibility of SQL results returned from Databend.

Correctness Testing​

In 2022, we replaced stateless tests with SQL Logic Tests for Databend in the first place. We also migrated a large number of mature test cases to cover as many scenarios as possible. Afterward, we started to use a Rust native test program called sqllogictest-rs instead of the previous Python one, which saved us a lot of time on CI without losing the maintainability of the tests.

Furthermore, we also planned and implemented three types of automated testing (TLP, QPS, and NoREC) supported by SQLancer. All of them have been successfully merged into the main branch with dozens of bug fixes.

Performance Testing​

Performance testing is also essential for us. In 2022, we launched a website (https://perf.databend.rs/) to track daily performance changes and spot potential issues. Meanwhile, we actively evaluated Databend against Clickbench and some other benchmarks.

Ecosystem: Give and Take​

The Databend ecosystem and users benefit from each other. More and more users were attracted to the ecosystem and joined the community in 2022. As they brought their own creative ideas to Databend and made them come true, the Databend ecosystem made tremendous progress and started to flourish in the field.

Positive Expansion​

We build and value the Databend ecosystem. Databend is now compatible with the MySQL protocol and Clickhouse HTTP Handler, and can seamlessly integrate with the following data services or utilities:

• Airbyte
• DBT
• Addax (Datax)
• Vector
• Jupyter Notebook
• DBeaver

To help users develop and customize services based on Databend, we developed drivers in multiple languages, including Python and Go.

Growing with Users​

Users are the basis of Databend. They help develop Databend and stir up the whole community.

In 2022, Databend added support for the Hive Catalog with the help of Kuaishou Technology. This connected Databend to the Hive ecosystem and encouraged us to consider the possibility of multiple catalogs. DMALL implemented and verified data archiving with Databend. We also appreciate SHAREit, Voyance, DigiFinex, and Weimob for their trust and support.

The Databend ecosystem includes a few projects that are loved and trusted by other products:

• OpenDAL now manages the data access layer for sccache , which provides further support for Firefox CI . Other database and data analysis projects, such as GreptimeDB and deepeth/mars , also used OpenDAL for data access.
• OpenRaft was used to implement a Feature Registry (database to hold feature metadata) in Azure/Feathr. SAP, Huobi, and Meituan also used it in a few internal projects.
• The MySQL protocol implementation in OpenSrv has been applied to multiple database projects such as GreptimeDB and CeresDB .

Knowledge Sharing​

In 2022, the Databend community launched the "Data Infra Club" for knowledge sharing. Our friends from PingCAP, Kuaishou Technology, DMALL, and SHAREit were invited to share their insights on big data platforms, Data Mesh, and Modern Data Stack. You can find all the video replays on Bilibili if you're interested.

Going Cloud: Sky's the Limit​

Going cloud is part of Databend's business strategy where most Databend users come from the cloud.

Built on top of Databend, Databend Cloud is a big-data analytics platform of the next generation, featuring easy-to-use , low-cost , and high-performance . Two versions of Databend Cloud are now available and open for trial:

• https://databend.com (for international users)
• https://databend.cn (for China users only)

Rewriting sqllogictest Framework with Rust

This post is about a big move we've made for Databend. We successfully switched the sqllogictest framework from Python to Rust using sqllogictest-rs, a robust implementation of the sqllogictest framework for the Rust ecosystem. Sqllogictest was designed with SQLite in mind. Benefiting from its neutrality towards database engines, we can use Sqllogictest to verify the accuracy of a SQL database engine as well. This is done by comparing query results from multiple SQL engines running the same query.

Why sqllogictest-rs?

The original sqllogictest framework (RFC for sqllogictest) was written in Python. We planned a switch to sqllogictest-rs for the following reasons:

• The entire Databend team is proficient in Rust. Working with a unified codebase written in Rust would boost our productivity over the long term.
• The previous framework lacked a strict parser at the front end and resulted in errors going undetected.
• As the sqllogictest-rs crate is maturing, building a new sqllogictest framework based on it would save us a lot of effort in the long run.
• We expected a 10x performance boost from the switch to Rust. The Python sqllogictest had been experiencing suboptimal runtime that resulted in a slower CI.

How We Nailed It

Our first version of sqllogictest doesn't strictly follow the sqllogictest wiki. This is a little bit frustrating because we have to manually adjust the format of the test files, for example, in some cases like these:

• Extra blank lines between the query and ----.
• Non-identical comment formats.
• Confusing empty strings: It displays results from queries like select ' ' with , rather than (empty).

Databend supports three types of client handlers: MySQL, HTTP, and ClickHouse. Each type of them returns content in a different format. The HTTP handler returns content in JSON format and the ClickHouse handler returns it in TSV, both of which require the following substitutions:

• inf -> Infinity
• nan -> NaN
• \N -> NULL

We introduced sandbox tenant to increase parallelism. Each test file now runs in parallel in its own sandbox environment that is separated from each other. The benefits of doing so include preventing a database or table from being dropped by mistake and significantly reducing test time.

Unsolved Issues​

We're still figuring out the most effective way to test a query that returns dynamic results. For example, the Create_time in the result returned from SHOW TABLE STATUS.

After the Switch

We're glad to see an efficiency improvement after going with sqllogictest-rs and this will benefit the entire Databend community. Our special thanks go to sqllogictest-rs for the great support, and everyone who has been involved. If you're also a fan of sqllogictest, stay tuned for more exciting news by visiting the following links:

• README
• sqllogictest tracking

Databend is a powerful cloud data warehouse. Built for elasticity and efficiency. Free and open. Also available in the cloud: https://app.databend.com .

What's New​

Check out what we've done this week to make Databend even better for you.

Features & Improvements ✨​

Meta

• remove stream when a watch client is dropped (#9334)

Planner

• support selectivity estimation for range predicates (#9398)

Query

• support copy on error (#9312)
• support databend-local (#9282)
• external storage support location part prefix (#9381)

Storage

• rangefilter support in (#9330)
• try to improve object storage io read (#9335)
• support table compression (#9370)

Metrics

• add more metrics for fuse compact and block write (#9399)

Sqllogictest

• add no-fail-fast support (#9391)

Code Refactoring 🎉​

*

• adopt rustls entirely, removing all deps to native-tls (#9358)

Format

• remove format_xxx settings (#9360)
• adjust interface of FileFormatOptionsExt (#9395)

Planner

• remove SyncTypeChecker (#9352)

Query

• split fuse source to read data and deserialize (#9353)
• avoid io copy in read parquet data (#9365)
• add uncompressed buffer for parquet reader (#9379)

Storage

• add read/write settings (#9359)

Bug Fixes 🔧​

Format

• fix align_flush with header only (#9327)

Settings

• use logical CPU number as default value of num_cpus (#9396)

Processors

• the data type on both sides of the union does not match (#9361)

HTTP Handler

• false alarm (warning log) about query not exists (#9380)

Sqllogictest

• refactor sqllogictest http client and fix expression string like (#9363)

What's On In Databend​

Stay connected with the latest news about Databend.

Introducing databend-local​​

Inspired by clickhouse-local, databend-local allows you to perform fast processing on local files, without the need of launching a Databend cluster.

> export CONFIG_FILE=tests/local/config/databend-local.toml
> cargo run --bin=databend-local -- --sql="SELECT * FROM tbl1" --table=tbl1=/path/to/databend/docs/public/data/books.parquet

exec local query: SELECT * FROM tbl1
+------------------------------+---------------------+------+
| title                        | author              | date |
+------------------------------+---------------------+------+
| Transaction Processing       | Jim Gray            | 1992 |
| Readings in Database Systems | Michael Stonebraker | 2004 |
| Transaction Processing       | Jim Gray            | 1992 |
| Readings in Database Systems | Michael Stonebraker | 2004 |
+------------------------------+---------------------+------+
4 rows in set. Query took 0.015 seconds.

Learn More

• PR | support databend-local

What's Up Next​

We're always open to cutting-edge technologies and innovative ideas. You're more than welcome to join the community and bring them to Databend.

Compressing Short Strings​​

When processing the same queries with short strings involved, Databend usually reads more data than other databases, such as Snowflake.

SELECT SearchPhrase, MIN(URL), COUNT(*) AS c FROM hits WHERE URL LIKE '%google%' AND SearchPhrase <> '' GROUP BY SearchPhrase ORDER BY c DESC LIMIT 10;

Such queries might be more efficient if short strings (URLs, etc) are compressed.

Issue 9001: performance: compressing for short strings

Please let us know if you're interested in contributing to this issue, or pick up a good first issue at https://link.databend.rs/i-m-feeling-lucky to get started.

Changelog​

You can check the changelog of Databend Nightly for details about our latest developments.

• v0.8.167-nightly
• v0.8.166-nightly
• v0.8.165-nightly
• v0.8.164-nightly
• v0.8.163-nightly
• v0.8.162-nightly
• v0.8.161-nightly

Contributors​

Thanks a lot to the contributors for their excellent work this week.

andylokandy	ariesdevil	BohuTANG	dantengsky	drmingdrmer	eastfisher

everpcpc	leiysky	mergify[bot]	PsiACE	RinChanNOWWW	soyeric128

sundy-li	Xuanwo	xudong963	youngsofun	zhang2014	zhyass

Connect With Us​

We'd love to hear from you. Feel free to run the code and see if Databend works for you. Submit an issue with your problem if you need help.

DatafuseLabs Community is open to everyone who loves data warehouses. Please join the community and share your thoughts.

• Databend Official Website
• GitHub Discussions (Feature requests, bug reports, and contributions)
• Twitter (Stay in the know)
• Slack Channel (Chat with the community)

Databend is a powerful cloud data warehouse. Built for elasticity and efficiency. Free and open. Also available in the cloud: https://app.databend.com .

What's New​

Check out what we've done this week to make Databend even better for you.

Features & Improvements ✨​

Multiple Catalogs

• implement show tables (from|in catalog.database) (#9153)

Planner

• introduce histogram in column statistics (#9310)

Query

• support attaching stage for insert values (#9249)
• add native format in fuse table (#9279)
• add internal_enable_sandbox_tenant config and sandbox_tenant (#9277)

Sqllogictest

• introduce rust native sqllogictest framework (#9150)

Code Refactoring 🎉​

*

• unify apply_file_format_options for copy & insert (#9323)

IO

• remove unused code (#9266)

meta

• test watcher count (#9324)

Planner

• replace TableContext in planner with PlannerContext (#9290)

Bug Fixes 🔧​

Base

• try fix SIGABRT when catch unwind (#9269)
• replace #[thread_local] to thread_local macro (#9280)

Query

• fix unknown database in query without relation to this database (#9250)
• fix wrong current_role when drop the role (#9276)

What's On In Databend​

Stay connected with the latest news about Databend.

Introduced a Rust Native Sqllogictest Framework​

Sqllogictest verifies the results returned from a SQL database engine by comparing them with the results of other engines for the same queries.

In the past, Databend ran such tests using a program written in Python and migrated a large number of test cases from other popular databases. We implemented the program again with sqllogictest-rs in recent days.

Learn More

• Doc | sqllogictest
• PR | introduce rust native sqllogictest framework

Experimental: Native Format​

PA is a native storage format based on Apache Arrow. Similar to Arrow IPC, PA aims at optimizing the storage layer.

Databend is introducing PA as a native storage format in the hope of getting a performance boost, though it's still at an early stage of development.

create table tmp (a int) ENGINE=FUSE STORAGE_FORMAT='native';

Learn More

• PR | add native format in fuse table
• GitHub | PA - A native storage format based on Apache Arrow

What's Up Next​

We're always open to cutting-edge technologies and innovative ideas. You're more than welcome to join the community and bring them to Databend.

Checking File Existence Before Returning Presigned URL​​

When presigning a file, Databend currently returns a potentially valid URL based on the filename without checking if the file really exists. Thus, the 404 error might occur if the file doesn't exist at all.

Issue 8702: Before return presign url add file exist judgement

Please let us know if you're interested in contributing to this issue, or pick up a good first issue at https://link.databend.rs/i-m-feeling-lucky to get started.

Changelog​

You can check the changelog of Databend Nightly for details about our latest developments.

• v0.8.160-nightly
• v0.8.159-nightly
• v0.8.158-nightly
• v0.8.157-nightly
• v0.8.156-nightly
• v0.8.155-nightly
• v0.8.154-nightly
• v0.8.153-nightly

Contributors​

Thanks a lot to the contributors for their excellent work this week.

ariesdevil	b41sh	BohuTANG	ClSlaid	drmingdrmer	everpcpc

leiysky	mergify[bot]	PsiACE	sandflee	soyeric128	sundy-li

Xuanwo	xudong963	youngsofun	zhang2014	ZhiHanZ	zhyass

Connect With Us​

We'd love to hear from you. Feel free to run the code and see if Databend works for you. Submit an issue with your problem if you need help.

DatafuseLabs Community is open to everyone who loves data warehouses. Please join the community and share your thoughts.

• Databend Official Website
• GitHub Discussions (Feature requests, bug reports, and contributions)
• Twitter (Stay in the know)
• Slack Channel (Chat with the community)

Profiling CPU and memory for Go applications is easy and can be of great help in performance troubleshooting, for example, with flamegraphs. For Rust applications, however, the profiling requires extra work. This post explains how to use flamegraphs to visualize performance data of your CPU and memory for Databend.

To support CPU and memory profiling, some APIs need to be included in the application. For example, Databend includes the following in the code:

• cpu/pprof.rs
• mem/jeprof.rs

CPU Profiling​

To do a CPU profiling, simply run the following command on the Databend server:

go tool pprof -http="0.0.0.0:8081" http://localhost:8080/debug/pprof/profile?seconds=30

• localhost:8080: Databend management address.
• 0.0.0.0:8081: pprof server address.
• seconds=30: Profiling lasts for 30 seconds.

Then open the URL <your-ip>:8081/ui/flamegraph in your browser to view the flamegraph:

Memory Profiling​

Compared to CPU profiling, memory profiling is a bit more involved, and can be done in the following steps:

1. Enable Memory Profiling​

cargo build --bin databend-query --release --features memory-profiling

2. Start with MALLOC_CONF​

MALLOC_CONF=prof:true,lg_prof_interval:30 ./target/release/databend-query

• lg_prof_interval:30: Profiles are dumped into a file for each allocation of 1 GiB (2^30 bytes).

3. Replace add2line with a Faster One​

This will rocket your jeprof from 30 minutes to 3 seconds.

git clone https://github.com/gimli-rs/addr2line
cd addr2line
cargo b --examples -r
cp ./target/release/examples/addr2line <your-addr2line-find-with-whereis-addr2line>

4. Upgrade jeprof to the Latest Version​

jeprof needs an upgrade because the old version doesn't support some parameters for creating flamegraphs. jeporf is a perl script, so the way to upgrade it is a little bit rough-and-ready.

First, find out the path of your local jeprof file:

whereis jeprof

Open and copy the latest version of jeprof, then overwrite your local copy with the copied script EXCEPT for the following two parameters:

my $JEPROF_VERSION = "5.2.1-0-gea6b3e973b477b8061e0076bb257dbd7f3faa756";
my $PPROF_VERSION = "2.0";

5. Create a Flamegraph​

jeprof ./databend-query-main ./jeprof.206330.563.i563.heap --collapse | flamegraph.pl --reverse --invert --minwidth 3 > heap.svg

• flamegraph.pl: Download from GitHub.
• databend-query-main: Path to your executable.
• jeprof.206330.563.i563.heap: Selects a heap file.

References​

• FlameGraph
• https://github.com/jemalloc/jemalloc/blob/dev/bin/jeprof.in
• Databend, Cloud Lakehouse: https://github.com/datafuselabs/databend

Databend is a powerful cloud data warehouse. Built for elasticity and efficiency. Free and open. Also available in the cloud: https://app.databend.com .

What's New​

Check out what we've done this week to make Databend even better for you.

Features & Improvements ✨​

Multiple Catalogs

• extends show databases SQL (#9152)

Stage

• support select from URI (#9247)

Streaming Load

• support file_format syntax in streaming load insert sql (#9063)

Planner

• push down limit to union (#9210)

Query

• use analyze table instead of optimize table statistic (#9143)
• fast parse insert values (#9214)

Storage

• use distinct count calculated by the xor hash function (#9159)
• read_parquet read meta before read data (#9154)
• push down filter to parquet reader (#9199)
• prune row groups before reading (#9228)

Open Sharing

• add prototype open sharing and add sharing stateful tests (#9177)

Code Refactoring 🎉​

*

• simplify the global data registry logic (#9187)

Storage

• refactor deletion (#8824)

Build/Testing/CI Infra Changes 🔌​

• release databend deb package and databend with hive (#9138, #9241, etc.)

Bug Fixes 🔧​

Format

• support ASCII control code hex as format field delimiter (#9160)

Planner

• prewhere_column empty and predicate is not const will return empty (#9116)
• don't push down topk to Merge when it's child is Aggregate (#9183)
• fix nullable column validity not equal (#9220)

Query

• address unit test hang on test_insert (#9242)

Storage

• too many io requests for read blocks during compact (#9128)
• collect orphan snapshots (#9108)

What's On In Databend​

Stay connected with the latest news about Databend.

Breaking Change: Unified File Format Options​

To simplify, we're rolling out a set of unified file format options as follows for the COPY INTO command, the Streaming Load API, and all the other cases where users need to describe their file formats:

[ FILE_FORMAT = ( TYPE = { CSV | TSV | NDJSON | PARQUET | XML} [ formatTypeOptions ] ) ]

• Please note that the current format options starting with format_* will be deprecated.
• ... FORMAT CSV ... will still be accepted by the ClickHouse handler.
• Support for customized formats created by CREATE FILE FORMAT ... will be added in a future release: ... FILE_FORMAT = (format_name = 'MyCustomCSV') .... .

Learn More

• Issue | unify format options and remove format_ settings
• PR | support file_format syntax in streaming load insert sql

Open Sharing​

Open Sharing is a simple and secure data-sharing protocol designed for databend-query nodes running in a multi-cloud environment.

• Simple & Free: Open Sharing is open-source and basically a RESTful API implementation.
• Secure: Open Sharing verifies incoming requesters' identities and access permissions, and provides an audit log.
• Multi-Cloud: Open Sharing supports a variety of public cloud platforms, including AWS, Azure, GCP, etc.

Learn More

• Docs | Open Sharing
• PR | add prototype open sharing and add sharing stateful tests

What's Up Next​

We're always open to cutting-edge technologies and innovative ideas. You're more than welcome to join the community and bring them to Databend.

Refactoring Stage-Related Tests​

We're about to run stage-related tests again using the Streaming Load API to move files to a stage instead of an AWS command like this:

aws --endpoint-url ${STORAGE_S3_ENDPOINT_URL} s3 cp s3://testbucket/admin/data/ontime_200.csv s3://testbucket/admin/stage/internal/s1/ontime_200.csv >/dev/null 2>&1

This is because Databend users do not need to take care of, or do not even know the stage paths that the AWS command requires.

Issue 8528: refactor stage related tests

Please let us know if you're interested in contributing to this issue, or pick up a good first issue at https://link.databend.rs/i-m-feeling-lucky to get started.

Changelog​

You can check the changelog of Databend Nightly for details about our latest developments.

• v0.8.152-nightly
• v0.8.151-nightly
• v0.8.150-nightly
• v0.8.149-nightly
• v0.8.148-nightly
• v0.8.147-nightly
• v0.8.146-nightly
• v0.8.145-nightly

Contributors​

Thanks a lot to the contributors for their excellent work this week.

ariesdevil	b41sh	BohuTANG	Chasen-Zhang	ClSlaid	dantengsky

drmingdrmer	hantmac	lichuang	mergify[bot]	PsiACE	RinChanNOWWW

soyeric128	sundy-li	wubx	Xuanwo	xudong963	youngsofun

ZhiHanZ	zhyass	zzzdong

Connect With Us​

We'd love to hear from you. Feel free to run the code and see if Databend works for you. Submit an issue with your problem if you need help.

DatafuseLabs Community is open to everyone who loves data warehouses. Please join the community and share your thoughts.

• Databend Official Website
• GitHub Discussions (Feature requests, bug reports, and contributions)
• Twitter (Stay in the know)
• Slack Channel (Chat with the community)

When working with Databend, you don't bother maintaining indexes. Databend takes advantage of these indexing techniques to automatically build and manage indexes on the fly:

• Min/Max index
• Bloom index
• Cluster key

Min/Max Index​

Min/Max Index is the key indexing technique for OLAP databases. Databend Fuse Engine uses it as the main indexing method to build indexes and store them in snapshots, segments, and blocks. The following shows how the Min/Max Index works for a table in Databend.

First, use SHOW CREATE TABLE to find the initial snapshot file created for the table:

show create table ontime(
`Year` INT,  -- First column
...
) ENGINE=FUSE SNAPSHOT_LOCATION='1/458/_ss/71b460c61fa943d1a391d3118ebd984c_v1.json'

Downdoad and open the snapshot file with VSCODE:

{
    "format_version": 1,
    "snapshot_id": "71b460c6-1fa9-43d1-a391-d3118ebd984c",
    "timestamp": "2022-11-29T03:44:03.419194Z",
    "prev_snapshot_id": null,
    "schema": {
        "fields": [
            ... -- Field definitions
        ],
        "metadata": {}
    },  
    "summary": {
    "row_count": 90673588,
    "block_count": 200,
    "perfect_block_count": 0,
    "uncompressed_byte_size": 65821591614,
    "compressed_byte_size": 2761791374,
    "index_size": 1194623,
    "col_stats": {
           ...
           "0": {  --  Min/Max indexes for the first column 'Year' in the table
                "min": {
                    "Int64": 1987
                },
                "max": {
                    "Int64": 2004
                },
                "null_count": 0,
                "in_memory_size": 362694352,
                "distinct_of_values": 0
            },
           ...
        }，
    },
    "segments": [
        ...
        [
            "1/458/_sg/ddccbb022ba74387be0b41eefd16bbbe_v1.json",
            1
        ],
        ...
    ],
    "cluster_key_meta": null
}

The file above indicates that the min value of the first column is 1987 and the max is 2004. The indexes in a snapshot file can tell you whether the data you want to retrieve exists in the table. For example, no data would be returned for the following query if Databend cannot find a matching Min/Max interval in all snapshots:

select avg(DepDelay) from ontime where Year='2003';

Databend Fuse Engine stores the most important indexes in segment files. At the end of a snashot file, you can find information about which segments are related to the snapshot. Here's a sample segment file:

{
   "format_version":1,
   "blocks":[
       { -- block ...
           ...
                   "row_count": 556984,
                   "block_size": 405612604,
                   "file_size": 25302413,
                    "col_stats": {
                    ...
                   "0": {
                   "min": {
                       "Int64": 2003
                   },
                   "max": {
                       "Int64": 2003
                   },
                   "null_count": 0,
                   "in_memory_size": 2227936,
                   "distinct_of_values": 1
               },
                    ...
                    },
         "col_metas": {
          -- Used to record the start position and length of each column
         },
           "cluster_stats": null,
           "location": [
               "1/458/_b/e4f3795c79004f22b80ed5ee821edf23_v0.parquet",
               0
           ],
           "bloom_filter_index_location": [
               "1/458/_i_b_v2/e4f3795c79004f22b80ed5ee821edf23_v2.parquet",
               2
           ],
           "bloom_filter_index_size": 60207,
           "compression": "Lz4Raw"
           ...
       }    
   ],
   "summary": {
       "row_count": 11243809,
       "block_count": 25,
       "perfect_block_count": 25,
       "uncompressed_byte_size": 8163837349,
       "compressed_byte_size": 339392734,
       "index_size": 1200133,
       "col_stats": {
       ...
          "0": {
               "min": {
                   "Int64": 1988
               },
               "max": {
                   "Int64": 2003
               },
               "null_count": 0,
               "in_memory_size": 44975236,
               "distinct_of_values": 0
           },
       ...
       }
   }
}

From the sample above, we can see that a segment file contains its own Min/Max index information. So does a block file. The Min/Max indexes are layered and distributed among snapshots, segments, and blocks like this:

When retrieving data for a query, Databend starts from the snapshot indexes and locates the corresponding segment by matching the Min/Max interval. Then, it looks up the indexes in the segment file to find the block where the required data is stored and reads data from the block file with information about the start position from col_metas. So Databend literally processes a query by finding the right segments and blocks with the Min/Max Index.

Bloom Index​

For queries requiring an exact string match, Databend uses the Min/Max Index to find the right block first, and then locates the offsets with the bloom index information in bloom_filter_index_location to retrieve data from the block.

For more information about the Bloom Index, see https://databend.rs/blog/xor-filter.

Cluster Key​

The Min/Max Index seems to work perfectly for Databend, but in fact, data is usually written into a table out of order. As a result, segments and blocks might be created with overlapped Min/Max intervals.

For example, you need to access up to three parquet files for a query condition like Age = 20 & Age = 35. If Age is set as the cluster key, Databend will sort the data by the Age column and combine as many small parquet files as possible.

For more information about the cluster key, see https://databend.rs/doc/sql-commands/ddl/clusterkey/.

Databend is a powerful cloud data warehouse. Built for elasticity and efficiency. Free and open. Also available in the cloud: https://app.databend.com .

What's New​

Check out what we've done this week to make Databend even better for you.

Features & Improvements ✨​

Planner

• optimize topk in cluster mode (#9092)

Query

• support select * exclude [column_name | (col_name, col_name,...)] (#9009)
• alter table flashback (#8967)
• new table function read_parquet to read parquet files as a table (#9080)
• support select * from @stage (#9123)

Storage

• cache policy (#9062)
• support hive nullable partition (#9064)

Code Refactoring 🎉​

Memory Tracker

• keep tracker state consistent (#8973)

REST API

• drop ctx after query finished (#9091)

Bug Fixes 🔧​

Configs

• add more tests for hive config loading (#9074)

Planner

• try to fix table name case sensibility (#9055)

Functions

• vector_const like bug fix (#9082)

Storage

• update last_snapshot_hint file when purge (#9060)

Cluster

• try fix broken pipe or connect reset (#9104)

What's On In Databend​

Stay connected with the latest news about Databend.

RESTORE TABLE​

By the snapshot ID or timestamp you specify in the command, Databend restores the table to a prior state where the snapshot was created. To retrieve snapshot IDs and timestamps of a table, use FUSE_SNAPSHOT.

-- Restore with a snapshot ID
ALTER TABLE <table> FLASHBACK TO (SNAPSHOT => '<snapshot-id>');
-- Restore with a snapshot timestamp
ALTER TABLE <table> FLASHBACK TO (TIMESTAMP => '<timestamp>'::TIMESTAMP);

Learn More

• Docs | RESTORE TABLE
• PR | alter table flashback

What's Up Next​

We're always open to cutting-edge technologies and innovative ideas. You're more than welcome to join the community and bring them to Databend.

Adding Build Information to Error Report​

An error report currently only contains an error code and some information about why the error occurred. When build information is available, troubleshooting will become easier.

"Code: xx. Error: error msg... (version ...)"

Issue 9117: Add Build Information to the Error Report

Please let us know if you're interested in contributing to this issue, or pick up a good first issue at https://link.databend.rs/i-m-feeling-lucky to get started.

Changelog​

You can check the changelog of Databend Nightly for details about our latest developments.

• v0.8.144-nightly
• v0.8.143-nightly
• v0.8.142-nightly
• v0.8.141-nightly
• v0.8.140-nightly
• v0.8.139-nightly
• v0.8.138-nightly
• v0.8.137-nightly

Contributors​

Thanks a lot to the contributors for their excellent work this week.

andylokandy	b41sh	BohuTANG	dantengsky	drmingdrmer	everpcpc

lichuang	mergify[bot]	PsiACE	RinChanNOWWW	sandflee	soyeric128

sundy-li	TCeason	Xuanwo	xudong963	youngsofun	zhang2014

ZhiHanZ

Connect With Us​

We'd love to hear from you. Feel free to run the code and see if Databend works for you. Submit an issue with your problem if you need help.

DatafuseLabs Community is open to everyone who loves data warehouses. Please join the community and share your thoughts.

• Databend Official Website
• GitHub Discussions (Feature requests, bug reports, and contributions)
• Twitter (Stay in the know)
• Slack Channel (Chat with the community)

Databend is a powerful cloud data warehouse. Built for elasticity and efficiency. Free and open. Also available in the cloud: https://app.databend.com .

What's New​

Check out what we've done this week to make Databend even better for you.

Features & Improvements ✨​

Format

• better checking of format options (#8981)
• add basic schema infer for parquet (#9043)

Query

• QualifiedName support 'db.table.' and 'table.' (#8965)
• support bulk insert without exprssion (#8966)

Storage

• add cache layer for fuse engine (#8830)
• add system table system.memory_statistics (#8945)
• add optimize statistic ddl support (#8891)

Code Refactoring 🎉​

Base

• remove common macros (#8936)

Format

• TypeDeserializer get rid of FormatSetting (#8950)

Planner

• refactor extract or predicate (#8951)

Processors

• optimize join by merging build data block (#8961)

New Expression

• allow sparse column id in chunk, redo #8789 with a new approach. (#9008)

Documentation 📔​

• i18n support with crowdin (#8987, #8997, etc.)

Bug Fixes 🔧​

Base

• try fix lost tracker (#8932)

Meta

• fix share db bug, create DatabaseIdToName if need (#9006)

Mysql handler

• fix mysql conns leak (#8894)

Processors

• try fix update list memory leak (#9023)

Storage

• read and write block in parallel when compact (#8921)

What's On In Databend​

Stay connected with the latest news about Databend.

Infer Schema at a Glance​

You usually need to create a table before loading data from a file stored on a stage or somewhere. Unfortunately, sometimes you might not know the file schema to create the table or are unable to input the schema due to its complexity.

Introducing the capability to infer schema from an existing file will make the work much easier. You will even be able to query data directly from a stage using a SELECT statement like select * from @my_stage.

INFER 's3://mybucket/data.csv' FILE_FORMAT = ( TYPE = CSV );
+-------------+---------+----------+
| COLUMN_NAME | TYPE    | NULLABLE |
|-------------+---------+----------|
| CONTINENT   | TEXT    | True     |
| COUNTRY     | VARIANT | True     |
+-------------+---------+----------+

We've added support for inferring the basic schema from parquet files in #9043, and we're now working on #7211 to implement select from @stage.

Learn More

• PR | add basic schema infer for parquet
• Issue | query data from S3 location or stage
• PR | rfc: Infer Schema

What's Up Next​

We're always open to cutting-edge technologies and innovative ideas. You're more than welcome to join the community and bring them to Databend.

Add Tls Support for Mysql Handler​

opensrv-mysql v0.3.0 that was released recently includes support for TLS. It sounds like a good idea to introduce it to Databend.

let (is_ssl, init_params) = opensrv_mysql::AsyncMysqlIntermediary::init_before_ssl(
    &mut shim,
    &mut r,
    &mut w,
    &Some(tls_config.clone()),
)
.await
.unwrap();

opensrv_mysql::secure_run_with_options(shim, w, ops, tls_config, init_params).await

Issue 8983: Feature: tls support for mysql handler

Please let us know if you're interested in contributing to this issue, or pick up a good first issue at https://link.databend.rs/i-m-feeling-lucky to get started.

Changelog​

You can check the changelog of Databend Nightly for details about our latest developments.

• v0.8.136-nightly
• v0.8.135-nightly
• v0.8.134-nightly
• v0.8.133-nightly
• v0.8.132-nightly
• v0.8.131-nightly
• v0.8.130-nightly
• v0.8.129-nightly
• v0.8.128-nightly
• v0.8.127-nightly
• v0.8.126-nightly

Contributors​

Thanks a lot to the contributors for their excellent work this week.

andylokandy	ariesdevil	b41sh	BohuTANG	dantengsky	drmingdrmer

everpcpc	flaneur2020	leiysky	lichuang	mergify[bot]	PsiACE

sandflee	soyeric128	sundy-li	TCeason	TracyZYJ	Xuanwo

xudong963	youngsofun	yufan022	zhang2014	zhyass

Connect With Us​

We'd love to hear from you. Feel free to run the code and see if Databend works for you. Submit an issue with your problem if you need help.

DatafuseLabs Community is open to everyone who loves data warehouses. Please join the community and share your thoughts.

• Databend Website
• GitHub Discussions (Feature/Bug reports, Contributions)
• Twitter (Get the news fast)
• Slack Channel (For live discussion with the Community)

Backgroup​

now databend support hive catalog to run hive queries, this docs shows how to set up databend-hive environment and run hive sqls.

How to set up databend-hive cluster​

hiveserver&metastore&hdfs is supposed to be pre-installed.

1. download a databend-release with hive support, or build from source

## make sure JAVA_HOME is set
export JAVA_HOME=/path/to/java
export LD_LIBRARY_PATH=${JAVA_HOME}/lib/server:${LD_LIBRARY_PATH}
cargo build --features hive,storage-hdfs

2. setup a databend cluster, refer to deploying-databend
3. add hive catalog and hdfs storage to databend-query.toml

[storage]
type = "hdfs"

[storage.hdfs]
# hdfs namenode address,such as 127.0.0.1:8020
name_node = "xx"
root = ""

[catalogs.hive]
type = "hive"
# hive metastore address, such as 127.0.0.1:9083
address = "xx"

4. run databend-query with java&hadoop environment

export HADOOP_HOME=xxx
export JAVA_HOME=xxx, such as /Library/Java/JavaVirtualMachines/openjdk-11.jdk/Contents/Home
export LD_LIBRARY_PATH=$JAVA_HOME/lib/server:$LD_LIBRARY_PATH

./bin/databend-query -c ./databend-query.toml > query.log 2>&1 &

5. setup hive related settings with mysql client

set global sql_dialect = 'hive';

suggest settings:

-- for chinese users
set global timezone = 'Asia/Shanghai';
set global max_execute_time = 180000;

-- support hive nvl function
create FUNCTION nvl as (a,b) -> ifnull(a,b);

6. query hive data using mysql client or mysql jdbc client. Note: hive tables must be referred as hive.db.table

select * from hive.$db.$table limit 10;

Limititions​

1. only support parquet table, not support orc,txt
2. not support struct&map&decimal hive data types
3. only support hive select queries, not support DDL, insert, DML sqls
4. not support hive udfs, hive functions are limited supported

hive features is now in beta stage, please feel free to report bugs&suggestions in databend issues.

Many optimizations are usually required for big data analytics to "reduce the distance to data." For example, using the Bloom Filter Index, queries can be filtered to determine whether data should be fetched from backend storage:

Why We Replaced the Bloom Filter​

Most popular databases use Bloom Filters to handle equivalent queries and avoid useless data readings. Databend also used the classic Bloom Filter algorithm in the first version (databend#6639). However, we found the Bloom Filter Index required plenty of storage space which even exceeded the data storage size (Databend automatically created Bloom indexes for some data types to make it easier for users to work with). The Bloom Filter Index didn't show a significant performance improvement because it is not much different from reading data directly from storage.

The reason is that Bloom Filter does not know the cardinality of the data when it is generated. Take the Boolean type as an example, the algorithm allocates space for it without considering the cardinality (2, True or False).

As a result, the Databend community began exploring new solutions and determined a feasible solution that uses HyperLoglog to sort out distinct values before allocating space.

At the TiDB User Conference on a Saturday in September, I met XP (@drmingdrmer) and talked about the solution again with him. He came up with using Trie to solve the problem. An excellent idea, but lots of work. 

XP is a master for Trie, so I'm sure implementation would be no big deal for him. But I think some existing technologies might be able to help.

Why Xor Filter?​

A few explorations later, the Xor Filter algorithm, proposed by Daniel Lemire and his team in 2019: [Xor Filters: Faster and Smaller Than Bloom Filters, caught my attention(https://lemire.me/blog/2019/12/19/xor-filters-faster-and-smaller-than-bloom-filters/).

I did a test (Xor Filter Bench) with the Rust version (xorfilter) and got a very positive result, so we replaced Bloom Filters with Xor Filters by databend#7860. Let's do a test and see how it performs with Xor Filters.

u64: 
xor bitmap encode:1230069 bytes, raw:8000000 bytes, ratio:0.15375863

bool:
xor bitmap encode:61 bytes, raw:1000000 bytes, ratio:0.000061

string:
xor bitmap encode:123067 bytes, raw:3000000 bytes, ratio:0.041022334

100000 records of the same key:
xor bitmap encode: 61 bytes, raw:3000000 bytes, ratio:0.000020333333

Test Environment​

Databend: v0.8.122-nightly, single node
VM: 32 vCPU, 32 GiB (Cloud VM)
Object Storage: S3
Dataset: 10 billion records, 350G Raw Data, Xor Filter Index 700MB, all indexes and data are stored in object storage.
Table:

mysql> desc t10b;
+-------+-----------------+------+---------+-------+
| Field | Type            | Null | Default | Extra |
+-------+-----------------+------+---------+-------+
| c1    | BIGINT UNSIGNED | NO   | 0       |       |
| c2    | VARCHAR         | NO   |         |       |
+-------+-----------------+------+---------+-------+

Deploying Databend​

Step 1: Download installation package

wget https://github.com/datafuselabs/databend/releases/download/v0.8.122-nightly/databend-v0.8.122-nightly-x86_64-unknown-linux-musl.tar.gz 
tar zxvf databend-v0.8.122-nightly-x86_64-unknown-linux-musl.tar.gz

You can find the following content after extracting the package:

tree
.
├── bin
│   ├── databend-meta
│   ├── databend-metabench
│   ├── databend-metactl
│   └── databend-query
├── configs
│   ├── databend-meta.toml
│   └── databend-query.toml
├── readme.txt
└── scripts
    ├── start.sh
    └── stop.sh

Step 2: Start Databend Meta

./bin/databend-meta -c configs/databend-meta.toml

Step 3：Configure Databend Query

For more information, refer to https://databend.rs/doc/deploy/deploying-databend

vim configs/databend-query.toml

... ...

[meta]
endpoints = ["127.0.0.1:9191"]
username = "root"
password = "root"
client_timeout_in_second = 60
auto_sync_interval = 60

# Storage config.
[storage]
# fs | s3 | azblob | obs
type = "s3"

# To use S3-compatible object storage, uncomment this block and set your values.
 [storage.s3]
bucket = "<your-bucket-name>"
endpoint_url = "<your-s3-endpoint>"
access_key_id = "<your-key>"
secret_access_key = "<your-access-key>"

Step 4: Start Databend Query

./bin/databend-query -c configs/databend-query.toml

Step 5: Construct the test data set

mysql -uroot -h127.0.0.1 -P3307

Construct 10 billion rows of test data (time: 16 min 0.41 sec):

create table t10b as select number as c1, cast(rand() as string) as c2 from numbers(10000000000)

Run a query (no cache, all data and indexes are in object storage):

mysql> select * from t10b where  c2='0.6622377673133426';
+-----------+--------------------+
| c1        | c2                 |
+-----------+--------------------+
| 937500090 | 0.6622377673133426 |
+-----------+--------------------+
1 row in set (20.57 sec)
Read 40000000 rows, 1009.75 MiB in 20.567 sec., 1.94 million rows/sec., 49.10 MiB/sec.

With the Xor Filter Index, it takes about 20 seconds for a single-node Databend to complete a point query on a scale of 10 billion rows. You can also speed up point queries by expanding a single Databend node into a cluster. Refer to https://databend.rs/doc/deploy/expanding-to-a-databend-cluster#deploying-a-new-query-node for details.

References​

[1] Arxiv: Xor Filters: Faster and Smaller Than Bloom and Cuckoo Filters

[2] Daniel Lemire’s blog: Xor Filters: Faster and Smaller Than Bloom Filters

[3] Databend, Cloud Lakehouse: https://github.com/datafuselabs/databend

Life is a journey, sometimes you must go back to move forward. When working with databases, you need to access the data history now and then. Time Travel is one of the most valuable features that Databend has rolled out. The time travel feature acts as a data recovery utility that enables you to restore a dropped table or get back a previous version of your data in a table. For example, when you accidentally delete a table or update some rows by mistake, you will need the help from Time Travel. This post sheds some light on what you can do with Time Travel in Databend.

First things first, you must know that not all the historical data can be restored based on the Databend retention policy. The default retention period is 24 hours, which means you can restore your historical data within 24 hours after it is deleted or outdated.

If you run SHOW TABLES HISTORY against a database, you will find the dropped tables (if any) and their drop time. The command does not list the dropped tables that have passed their retention period.

Restore a Dropped Table​

When you delete a file from your computer, the file goes to the trash bin and you can restore the file by putting it back to its original folder.

In Databend, restoring a table is as easy as you restore a file from the trash bin. The UNDROP TABLE command makes a dropped table become available again with the data of the latest version. The "latest version" means that Databend recovers a table as well as the data that the table was holding when you deleted it.

Query Old Data​

This is the most glorious part of the Time Travel story in Databend. When we say that you can get back a previous version of your data in a table, it does not mean that you roll back your table to an earlier point in time, it shows you the table's data at that point instead.

Databend automatically takes and saves a snapshot of your tables after each transaction that updates your table data. A version of a table's data practically refers to a snapshot that saves the data of the table when the snapshot was taken.

Databend provides a system function named FUSE_SNAPSHOT that enables you to find the saved snapshots. Each snapshot comes with a snapshot ID and a timestamp.

The saved snapshots are the behind-the-scenes heroes that make the time travel become true. So when you try to get back your history data, you need to tell Databend which version you want by the snapshot ID or the timestamp with an AT clause in the SELECT statement.

Create a New Table from Old Data​

The Time Travel feature makes it possible to create an OLD table, which means you can create a table to hold and move on from a previous version of your data.

The CREATE TABLE statement can include a SNAPSHOT_LOCATION clause that allows you to specify a snapshot file that holds your old data. This command enables you to insert the data stored in the snapshot file when you create a table. Please note that the table you create must have same column definitions as the data from the snapshot.

Go without Time Travel​

Tables in Databend support Time Travel out-of-the-box. However, you might not need it for some cases, for example, when you're running low of your storage space or the data is big but unimportant. Databend currently does not provide a setting to switch it off, but you can CREATE TRANSIENT TABLE.

Transient tables are used to hold transitory data that does not require a data protection or recovery mechanism. Dataebend does not hold historical data for a transient table so you will not be able to query from a previous version of the transient table with the Time Travel feature, for example, the AT clause in the SELECT statement will not work for transient tables. Please note that you can still drop and undrop a transient table.

JSON (JavaScript Object Notation) is a commonly used semi-structured data type. With the self-describing schema structure, JSON can hold all data types, including multi-level nested data types, such as Array, Object, etc. JSON takes advantage of high flexibility and easy dynamic expansion compared with the structured data types that must strictly follow the fields in a tabular data structure.

As data volume increases rapidly in recent years, many platforms have started to use and get the most out of semi-structured data types (such as JSON). For example, the JSON data shared by various platforms through open interfaces, and the public datasets and application logs stored in JSON format.

Databend supports structured data types, as well as JSON. This post dives deeply into the JSON data type in Databend.

Working with JSON in Databend​

Databend stores semi-structured data as the VARIANT (also called JSON) data type:

CREATE TABLE test
  (
     id INT32,
     v1 VARIANT,
     v2 JSON
  );

The JSON data needs to be generated by calling the parse_json or try_parse_json function. The input string must be in the standard JSON format, including Null, Boolean, Number, String, Array, and Object. In case of parsing failure due to invalid string, the parse_json function will return an error while the try_parse_json function will return a NULL value.

INSERT INTO test VALUES
  (1, parse_json('{"a":{"b":1,"c":[1,2]}}'), parse_json('[["a","b"],{"k":"a"}]')),
  (2, parse_json('{"a":{"b":2,"c":[3,4]}}'), parse_json('[["c","d"],{"k":"b"}]'));

SELECT * FROM test;
+----+-------------------------+-----------------------+
| id | v1                      | v2                    |
+----+-------------------------+-----------------------+
| 1  | {"a":{"b":1,"c":[1,2]}} | [["a","b"],{"k":"a"}] |
| 2  | {"a":{"b":2,"c":[3,4]}} | [["c","d"],{"k":"b"}] |
+----+-------------------------+-----------------------+

JSON usually holds data of Array or Object type. Due to the nested hierarchical structure, the internal elements can be accessed through JSON PATH. The syntax supports the following delimiters:

• :: Colon can be used to obtain the elements in an object by the key.

• .: Dot can be used to obtain the elements in an object by the key. Do NOT use a dot as the first delimiter in a statement, or Databend would consider the dot as the delimiter to separate the table name from the column name.

• []: Brackets can be used to obtain the elements in an object by the key or the elements in an array by the index.

You can mix the three types of delimiters above.

SELECT v1:a.c, v1:a['b'], v1['a']:c, v2[0][1], v2[1].k FROM test;

+--------+-----------+-----------+----------+---------+
| v1:a.c | v1:a['b'] | v1['a']:c | v2[0][1] | v2[1].k |
+--------+-----------+-----------+----------+---------+
| [1,2]  | 1         | [1,2]     | "b"      | "a"     |
| [3,4]  | 2         | [3,4]     | "d"      | "b"     |
+--------+-----------+-----------+----------+---------+