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A Modern Cloud Data Warehouse with the Elasticity and Performance both on Object Storage

Documentation | Roadmap(v0.8)

• What is Databend?
• Design Overview
  • Meta Service Layer
  • Compute Layer
  • Storage Layer
• Getting Started
• Roadmap

Databend is an open source Elastic and Scalable Modern Cloud Data Warehouse.

Databend uses the latest techniques in vectorized query processing to allow you to do blazing-fast data analytics on Object Storage.

• Instant Elasticity

  Databend separates the storage and compute, which allows you easily scale up or scale down based on your application's needs.

• Blazing Performance

  Databend leverages data-level parallelism(Vectorized Query Execution) and instruction-level parallelism(SIMD) technology, offers blazing performance data analytics.

• Support for Semi-Structured Data

  Databend supports ingestion of semi-structured data in various formats like CSV, JSON and Parquet which located in cloud or your local file system.

• MySQL/ClickHouse Compatible

  Databend is ANSI SQL compliant and MySQL/ClickHouse wire protocol compatible, making it easy to connect with existing tools.

• Easy to Use

  Databend has no indexes to build, no manual tuning required, no manual figuring out partitions or shard data, it’s all done for you as data is loaded into table.

This is the high-level architecture of Databend, it consists of three components:

• meta service layer
• compute layer
• storage layer

The meta service is a layer to service multiple tenants. This layer implements a persistent key-value store to store each tenant's state. In current implementation, the meta service has many components:

• Metadata, which manages all metadata of databases, tables, clusters, the transaction, etc.
• Administration, which stores user info, user management, access control information, usage statistics, etc.
• Security, which performs authorization and authentication to protect the privacy of users' data.

The code of Meta Service Layer mainly resides in the metasrv directory of the repository.

The compute layer is the layer to carry out computation for query processing. This layer may consist of many clusters, and each cluster may consist of many nodes. Each node is a compute unit, and is a collection of components:

• Planner

  The query planner builds an execution plan from the user's SQL statement and represents the query with different types of relational operators (such as Projection, Filter, Limit, etc.).

  For example:

  databend :) EXPLAIN SELECT avg(number) FROM numbers(100000) GROUP BY number % 3
  ┌─explain─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
  │ Projection: avg(number):Float64                                                                                                                                                         │
  │   AggregatorFinal: groupBy=[[(number % 3)]], aggr=[[avg(number)]]                                                                                                                       │
  │     AggregatorPartial: groupBy=[[(number % 3)]], aggr=[[avg(number)]]                                                                                                                   │
  │       Expression: (number % 3):UInt8, number:UInt64 (Before GroupBy)                                                                                                                    │
  │         ReadDataSource: scan schema: [number:UInt64], statistics: [read_rows: 100000, read_bytes: 800000, partitions_scanned: 11, partitions_total: 11], push_downs: [projections: [0]] │
  └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
  

• Optimizer

  A rule based optimizer, some rules like predicate push down or pruning of unused columns.

• Processors

  A Pull&Push-Based query execution pipeline, which is built by planner instructions. Each pipeline executor is a processor(such as SourceTransform, FilterTransform, etc.), it has zero or more inputs and zero or more outputs, and connected as a pipeline, it also can be distributed on multiple nodes judged by your query workload.

  For example:

  databend :) EXPLAIN PIPELINE SELECT avg(number) FROM numbers(100000) GROUP BY number % 3
  ┌─explain────────────────────────────────────────────────────────────────────────────────┐
  │ ProjectionTransform × 16 processors                                                    │
  │   Mixed (GroupByFinalTransform × 1 processor) to (ProjectionTransform × 16 processors) │
  │     GroupByFinalTransform × 1 processor                                                │
  │       Merge (GroupByPartialTransform × 16 processors) to (GroupByFinalTransform × 1)   │
  │         GroupByPartialTransform × 16 processors                                        │
  │           ExpressionTransform × 16 processors                                          │
  │             SourceTransform × 16 processors                                            │
  └────────────────────────────────────────────────────────────────────────────────────────┘
  

Node is the smallest unit of the compute layer. A set of nodes can be registered as one cluster via namespace. Many clusters can attach the same database, so they can serve the query in parallel by different users. When you add new nodes to a cluster, the currently running computational tasks can be scaled(known as work-stealing) guarantee.

The Compute Layer codes mainly in the query directory.

Databend stores data in an efficient, columnar format as Parquet files. Each Parquet file is sorted by the primary key before being written to the underlying shared storage. For efficient pruning, Databend also creates indexes for each Parquet file:

• min_max.idx The index file stores the minimum and maximum value of this Parquet file. -sparse.idx The index file store the <key, parquet-page> mapping for every [N] records granularity.

With the indexes, we can speed up the queries by reducing the I/O and CPU cost. Imagine that Parquet file f1 has min_max.idx of [3, 5) and Parquet file f2 has min_max.idx of [4, 6) in column x, if the query predicate is WHERE x < 4, only f1 needs to be accessed and processed.

• Databend Deploy
• Databend Develop
• Databend Contributing
• Databend Performance
• Databend Weekly

• Roadmap v0.8
• Roadmap 2022

• Slack Channel
• @Databend on Twitter

Databend is licensed under Apache 2.0.

• Databend is inspired by ClickHouse and Snowflake, its computing model is based on apache-arrow.
• The documentation website hosted by Vercel.