ClickHouse Rust client

The official Rust client for connecting to ClickHouse, originally developed by Paul Loyd. The client source code is available in the GitHub repository.

Overview

Uses serde for encoding/decoding rows.
Supports serde attributes: skip_serializing, skip_deserializing, rename.
Uses RowBinary format over the HTTP transport.
- There are plans to switch to Native over TCP.
Supports TLS (via native-tls and rustls-tls features).
Supports compression and decompression (LZ4).
Provides APIs for selecting or inserting data, executing DDLs, and client-side batching.
Provides convenient mocks for unit testing.

Installation

To use the crate, add the following to your Cargo.toml:

[dependencies]
clickhouse = "0.12.2"

[dev-dependencies]
clickhouse = { version = "0.12.2", features = ["test-util"] }

Cargo features

lz4 (enabled by default) — enables Compression::Lz4 and Compression::Lz4Hc(_) variants. If enabled, Compression::Lz4 is used by default for all queries except for WATCH.
native-tls — supports urls with the HTTPS schema via hyper-tls, which links against OpenSSL.
rustls-tls — supports urls with the HTTPS schema via hyper-rustls, which doesn’t link against OpenSSL.
inserter — enables client.inserter().
test-util — adds mocks. See the example. Use it only in dev-dependencies.
watch — enables client.watch functionality. See the corresponding section for details.
uuid — adds serde::uuid to work with uuid crate.
time — adds serde::time to work with time crate.

:::important When connecting to ClickHouse via an HTTPS url, either the native-tls or rustls-tls feature should be enabled. If both are enabled, the rustls-tls feature will take precedence. :::

ClickHouse versions compatibility

The client is compatible with the LTS or newer versions of ClickHouse, as well as ClickHouse Cloud. ClickHouse server older than v22.6 handles RowBinary incorrectly in some rare cases. You could use v0.11+ and enable wa-37420 feature to solve this problem. Note: this feature shouldn’t be used with newer ClickHouse versions.

Examples

We aim to cover various scenarios of client usage with the examples in the client repository. The overview is available in the examples README. If something is unclear or missing from the examples or from the following documentation, feel free to contact us.

Usage

ch2rs crate is useful to generate a row type from ClickHouse.

Creating a client instance

Reuse created clients or clone them in order to reuse the underlying hyper connection pool.

use clickhouse::Client;

let client = Client::default()
    // should include both protocol and port
    .with_url("http://localhost:8123")
    .with_user("name")
    .with_password("123")
    .with_database("test");

HTTPS or ClickHouse Cloud connection

HTTPS works with either rustls-tls or native-tls cargo features. Then, create client as usual. In this example, the environment variables are used to store the connection details: :::important The URL should include both protocol and port, e.g. https://instance.clickhouse.cloud:8443. :::

fn read_env_var(key: &str) -> String {
    env::var(key).unwrap_or_else(|_| panic!("{key} env variable should be set"))
}

let client = Client::default()
    .with_url(read_env_var("CLICKHOUSE_URL"))
    .with_user(read_env_var("CLICKHOUSE_USER"))
    .with_password(read_env_var("CLICKHOUSE_PASSWORD"));

Selecting rows

use serde::Deserialize;
use clickhouse::Row;
use clickhouse::sql::Identifier;

#[derive(Row, Deserialize)]
struct MyRow<'a> {
    no: u32,
    name: &'a str,
}

let table_name = "some";
let mut cursor = client
    .query("SELECT ?fields FROM ? WHERE no BETWEEN ? AND ?")
    .bind(Identifier(table_name))
    .bind(500)
    .bind(504)
    .fetch::<MyRow<'_>>()?;

while let Some(row) = cursor.next().await? { .. }

Placeholder ?fields is replaced with no, name (fields of Row).
Placeholder ? is replaced with values in following bind() calls.
Convenient fetch_one::<Row>() and fetch_all::<Row>() methods can be used to get a first row or all rows, correspondingly.
sql::Identifier can be used to bind table names.

NB: as the entire response is streamed, cursors can return an error even after producing some rows. If this happens in your use case, you could try query(...).with_option("wait_end_of_query", "1") in order to enable response buffering on the server-side. More details. The buffer_size option can be useful, too.

Use wait_end_of_query with caution when selecting rows, as it can will to higher memory consumption on the server side and will likely decrease the overall performance.

Inserting rows

use serde::Serialize;
use clickhouse::Row;

#[derive(Row, Serialize)]
struct MyRow {
    no: u32,
    name: String,
}

let mut insert = client.insert("some")?;
insert.write(&MyRow { no: 0, name: "foo".into() }).await?;
insert.write(&MyRow { no: 1, name: "bar".into() }).await?;
insert.end().await?;

If end() isn’t called, the INSERT is aborted.
Rows are being sent progressively as a stream to spread the network load.
ClickHouse inserts batches atomically only if all rows fit in the same partition and their number is less max_insert_block_size.

Async insert (server-side batching)

You could use ClickHouse asynchronous inserts to avoid client-side batching of the incoming data. This can be done by simply providing the async_insert option to the insert method (or even to the Client instance itself, so that it will affect all the insert calls).

let client = Client::default()
    .with_url("http://localhost:8123")
    .with_option("async_insert", "1")
    .with_option("wait_for_async_insert", "0");

Inserter feature (client-side batching)

Requires the inserter cargo feature.

let mut inserter = client.inserter("some")?
    .with_timeouts(Some(Duration::from_secs(5)), Some(Duration::from_secs(20)))
    .with_max_bytes(50_000_000)
    .with_max_rows(750_000)
    .with_period(Some(Duration::from_secs(15)));

inserter.write(&MyRow { no: 0, name: "foo".into() })?;
inserter.write(&MyRow { no: 1, name: "bar".into() })?;
let stats = inserter.commit().await?;
if stats.rows > 0 {
    println!(
        "{} bytes, {} rows, {} transactions have been inserted",
        stats.bytes, stats.rows, stats.transactions,
    );
}

// don't forget to finalize the inserter during the application shutdown
// and commit the remaining rows. `.end()` will provide stats as well.
inserter.end().await?;

Inserter ends the active insert in commit() if any of the thresholds (max_bytes, max_rows, period) are reached.
The interval between ending active INSERTs can be biased by using with_period_bias to avoid load spikes by parallel inserters.
Inserter::time_left() can be used to detect when the current period ends. Call Inserter::commit() again to check limits if your stream emits items rarely.
Time thresholds implemented by using quanta crate to speed the inserter up. Not used if test-util is enabled (thus, time can be managed by tokio::time::advance() in custom tests).
All rows between commit() calls are inserted in the same INSERT statement.

Don’t forget to flush if you want to terminate/finalize inserting:

inserter.end().await?;

Executing DDLs

With a single-node deployment, it is enough to execute DDLs like this:

client.query("DROP TABLE IF EXISTS some").execute().await?;

However, on clustered deployments with a load-balancer or ClickHouse Cloud, it is recommended to wait for the DDL to be applied on all the replicas, using the wait_end_of_query option. This can be done like this:

client
    .query("DROP TABLE IF EXISTS some")
    .with_option("wait_end_of_query", "1")
    .execute()
    .await?;

ClickHouse settings

You can apply various ClickHouse settings using the with_option method. For example:

let numbers = client
    .query("SELECT number FROM system.numbers")
    // This setting will be applied to this particular query only;
    // it will override the global client setting.
    .with_option("limit", "3")
    .fetch_all::<u64>()
    .await?;

Besides query, it works similarly with insert and inserter methods; additionally, the same method can be called on the Client instance to set global settings for all queries.

Query ID

Using .with_option, you can set the query_id option to identify queries in the ClickHouse query log.

let numbers = client
    .query("SELECT number FROM system.numbers LIMIT 1")
    .with_option("query_id", "some-query-id")
    .fetch_all::<u64>()
    .await?;

Besides query, it works similarly with insert and inserter methods.

If you set query_id manually, make sure that it is unique. UUIDs are a good choice for this.

See also: query_id example in the client repo.

Session ID

Similarly to query_id, you can set the session_id to execute the statements in the same session. session_id can be set either globally on the client level, or per query, insert, or inserter call.

let client = Client::default()
    .with_url("http://localhost:8123")
    .with_option("session_id", "my-session");

With clustered deployments, due to lack of “sticky sessions”, you need to be connected to a particular cluster node in order to properly utilize this feature, cause, for example, a round-robin load-balancer won’t guarantee that the consequent requests will be processed by the same ClickHouse node.

See also: session_id example in the client repo.

Custom HTTP headers

If you’re using proxy authentication or need to pass custom headers, you can do it like this:

let client = Client::default()
    .with_url("http://localhost:8123")
    .with_header("X-My-Header", "hello");

See also: custom HTTP headers example in the client repo.

Custom HTTP client

This could be useful for tweaking the underlying HTTP connection pool settings.

use hyper_util::client::legacy::connect::HttpConnector;
use hyper_util::client::legacy::Client as HyperClient;
use hyper_util::rt::TokioExecutor;

let connector = HttpConnector::new(); // or HttpsConnectorBuilder
let hyper_client = HyperClient::builder(TokioExecutor::new())
    // For how long keep a particular idle socket alive on the client side (in milliseconds).
    // It is supposed to be a fair bit less that the ClickHouse server KeepAlive timeout,
    // which was by default 3 seconds for pre-23.11 versions, and 10 seconds after that.
    .pool_idle_timeout(Duration::from_millis(2_500))
    // Sets the maximum idle Keep-Alive connections allowed in the pool.
    .pool_max_idle_per_host(4)
    .build(connector);

let client = Client::with_http_client(hyper_client).with_url("http://localhost:8123");

This example relies on the legacy Hyper API and is a subject to change in the future.

See also: custom HTTP client example in the client repo.

Data types

See also the additional examples:

(U)Int(8|16|32|64|128) maps to/from corresponding (u|i)(8|16|32|64|128) types or newtypes around them.
(U)Int256 aren’t supported directly, but there is a workaround for it.
Float(32|64) maps to/from corresponding f(32|64) or newtypes around them.
Decimal(32|64|128) maps to/from corresponding i(32|64|128) or newtypes around them. It’s more convenient to use fixnum or another implementation of signed fixed-point numbers.
Boolean maps to/from bool or newtypes around it.
String maps to/from any string or bytes types, e.g. &str, &[u8], String, Vec<u8> or SmartString. New types are also supported. To store bytes, consider using serde_bytes, because it’s more efficient.

#[derive(Row, Debug, Serialize, Deserialize)]
struct MyRow<'a> {
    str: &'a str,
    string: String,
    #[serde(with = "serde_bytes")]
    bytes: Vec<u8>,
    #[serde(with = "serde_bytes")]
    byte_slice: &'a [u8],
}

FixedString(N) is supported as an array of bytes, e.g. [u8; N].

#[derive(Row, Debug, Serialize, Deserialize)]
struct MyRow {
    fixed_str: [u8; 16], // FixedString(16)
}

Enum(8|16) are supported using serde_repr.

use serde_repr::{Deserialize_repr, Serialize_repr};

#[derive(Row, Serialize, Deserialize)]
struct MyRow {
    level: Level,
}

#[derive(Debug, Serialize_repr, Deserialize_repr)]
#[repr(u8)]
enum Level {
    Debug = 1,
    Info = 2,
    Warn = 3,
    Error = 4,
}

UUID maps to/from uuid::Uuid by using serde::uuid. Requires the uuid feature.

#[derive(Row, Serialize, Deserialize)]
struct MyRow {
    #[serde(with = "clickhouse::serde::uuid")]
    uuid: uuid::Uuid,
}

IPv6 maps to/from std::net::Ipv6Addr.
IPv4 maps to/from std::net::Ipv4Addr by using serde::ipv4.

#[derive(Row, Serialize, Deserialize)]
struct MyRow {
    #[serde(with = "clickhouse::serde::ipv4")]
    ipv4: std::net::Ipv4Addr,
}

Date maps to/from u16 or a newtype around it and represents a number of days elapsed since 1970-01-01. Also, time::Date is supported by using serde::time::date, that requires the time feature.

#[derive(Row, Serialize, Deserialize)]
struct MyRow {
    days: u16,
    #[serde(with = "clickhouse::serde::time::date")]
    date: Date,
}

Date32 maps to/from i32 or a newtype around it and represents a number of days elapsed since 1970-01-01. Also, time::Date is supported by using serde::time::date32, that requires the time feature.

#[derive(Row, Serialize, Deserialize)]
struct MyRow {
    days: i32,
    #[serde(with = "clickhouse::serde::time::date32")]
    date: Date,
}

DateTime maps to/from u32 or a newtype around it and represents a number of seconds elapsed since UNIX epoch. Also, time::OffsetDateTime is supported by using serde::time::datetime, that requires the time feature.

#[derive(Row, Serialize, Deserialize)]
struct MyRow {
    ts: u32,
    #[serde(with = "clickhouse::serde::time::datetime")]
    dt: OffsetDateTime,
}

DateTime64(_) maps to/from i32 or a newtype around it and represents a time elapsed since UNIX epoch. Also, time::OffsetDateTime is supported by using serde::time::datetime64::*, that requires the time feature.

#[derive(Row, Serialize, Deserialize)]
struct MyRow {
    ts: i64, // elapsed s/us/ms/ns depending on `DateTime64(X)`
    #[serde(with = "clickhouse::serde::time::datetime64::secs")]
    dt64s: OffsetDateTime,  // `DateTime64(0)`
    #[serde(with = "clickhouse::serde::time::datetime64::millis")]
    dt64ms: OffsetDateTime, // `DateTime64(3)`
    #[serde(with = "clickhouse::serde::time::datetime64::micros")]
    dt64us: OffsetDateTime, // `DateTime64(6)`
    #[serde(with = "clickhouse::serde::time::datetime64::nanos")]
    dt64ns: OffsetDateTime, // `DateTime64(9)`
}

Tuple(A, B, ...) maps to/from (A, B, ...) or a newtype around it.
Array(_) maps to/from any slice, e.g. Vec<_>, &[_]. New types are also supported.
Map(K, V) behaves like Array((K, V)).
LowCardinality(_) is supported seamlessly.
Nullable(_) maps to/from Option<_>. For clickhouse::serde::* helpers add ::option.

#[derive(Row, Serialize, Deserialize)]
struct MyRow {
    #[serde(with = "clickhouse::serde::ipv4::option")]
    ipv4_opt: Option<Ipv4Addr>,
}

Nested is supported by providing multiple arrays with renaming.

// CREATE TABLE test(items Nested(name String, count UInt32))
#[derive(Row, Serialize, Deserialize)]
struct MyRow {
    #[serde(rename = "items.name")]
    items_name: Vec<String>,
    #[serde(rename = "items.count")]
    items_count: Vec<u32>,
}

Geo types are supported. Point behaves like a tuple (f64, f64), and the rest of the types are just slices of points.

type Point = (f64, f64);
type Ring = Vec<Point>;
type Polygon = Vec<Ring>;
type MultiPolygon = Vec<Polygon>;
type LineString = Vec<Point>;
type MultiLineString = Vec<LineString>;

#[derive(Row, Serialize, Deserialize)]
struct MyRow {
    point: Point,
    ring: Ring,
    polygon: Polygon,
    multi_polygon: MultiPolygon,
    line_string: LineString,
    multi_line_string: MultiLineString,
}

Variant, Dynamic, (new) JSON data types aren’t supported yet.

Mocking

The crate provides utils for mocking CH server and testing DDL, SELECT, INSERT and WATCH queries. The functionality can be enabled with the test-util feature. Use it only as a dev-dependency. See the example.

Troubleshooting

CANNOT_READ_ALL_DATA

The most common cause for the CANNOT_READ_ALL_DATA error is that the row definition on the application side does match that in ClickHouse. Consider the following table:

CREATE OR REPLACE TABLE event_log (id UInt32)
ENGINE = MergeTree
ORDER BY timestamp

Then, if EventLog is defined on the application side with mismatching types, e.g.:

#[derive(Debug, Serialize, Deserialize, Row)]
struct EventLog {
    id: String, // <- should be u32 instead!
}

When inserting the data, the following error can occur:

Error: BadResponse("Code: 33. DB::Exception: Cannot read all data. Bytes read: 5. Bytes expected: 23.: (at row 1)\n: While executing BinaryRowInputFormat. (CANNOT_READ_ALL_DATA)")

In this example, this is fixed by the correct definition of the EventLog struct:

#[derive(Debug, Serialize, Deserialize, Row)]
struct EventLog {
    id: u32
}

Known limitations

Variant, Dynamic, (new) JSON data types aren’t supported yet.
Server-side parameter binding isn’t supported yet; see this issue for tracking.

Contact us

If you have any questions or need help, feel free to reach out to us in the Community Slack or via GitHub issues.

JavaScript

Python

Java

C#

Rust

C++

Third-party clients

Overview

Installation

Cargo features

ClickHouse versions compatibility

Examples

Usage

Creating a client instance

HTTPS or ClickHouse Cloud connection

Selecting rows

Inserting rows

Async insert (server-side batching)

Inserter feature (client-side batching)

Executing DDLs

ClickHouse settings

Query ID

Session ID

Custom HTTP headers

Custom HTTP client

Data types

Mocking

Troubleshooting

CANNOT_READ_ALL_DATA

Known limitations

Contact us

JavaScript

Python

Java

C#

Rust

C++

Third-party clients

Documentation Index

​Overview

​Installation

​Cargo features

​ClickHouse versions compatibility

​Examples

​Usage

​Creating a client instance

​HTTPS or ClickHouse Cloud connection

​Selecting rows

​Inserting rows

​Async insert (server-side batching)

​Inserter feature (client-side batching)

​Executing DDLs

​ClickHouse settings

​Query ID

​Session ID

​Custom HTTP headers

​Custom HTTP client

​Data types

​Mocking

​Troubleshooting

​CANNOT_READ_ALL_DATA

​Known limitations

​Contact us

Overview

Installation

Cargo features

ClickHouse versions compatibility

Examples

Usage

Creating a client instance

HTTPS or ClickHouse Cloud connection

Selecting rows

Inserting rows

Async insert (server-side batching)

Inserter feature (client-side batching)

Executing DDLs

ClickHouse settings

Query ID

Session ID

Custom HTTP headers

Custom HTTP client

Data types

Mocking

Troubleshooting

CANNOT_READ_ALL_DATA

Known limitations

Contact us