ClickHouse TypeScript client by @bytadaniel
Introduction
💙 Typescript
This package is written in TypeScript because Node.js typing development is already an industry standard.
🖐 Batch insert*
It has been empirically verified that in-memory collecting rows is the most efficient and consistent way to insert into Clickhouse. To work with built-in caching, you just need to call the useCaching() method
💪 Transparent and Stability
clickhouse-ts doesn't use a lot of abstractions and dependencies, so it's fast and stable.
🏗 Ready for production
The Lookforsale team has been using clickhouse-ts effectively for over a year under extreme loads!
👍 Batch validation
Double checking data for anomalies during in-memory caching and when inserting a finished batch.
✨ Flexibility
Flexible configuration of the Clickhouse client instance and support for all features provided by Clickhouse developers.
🔐 Security
SQL Injection Protection with sqlstring
🌈 Free for use
The package has a public license and is available for download to any developer!## Installation
Installation
npm i clickhouse-ts
npm i clickcache*How can I insert in-memory batches?
Starting from version 2.0.0 the caching module should be imported separately.
This is because clickcache package, like clickhouse-ts, is going to be part of my Clickhouse Node.js ecosystem.
In addition, it planned to introduce data validation, as in Joi and model generation, as in mongodb/mongoose.
Connection
Only HTTP(s) protocol is supported.
const client = new Clickhouse(
{
url: 'url',
port: 8443,
user: 'user',
password: 'password',
database: 'database',
ca: fs.readFileSync('cert.crt')
},
{
/* https://clickhouse.com/docs/en/interfaces/formats */
defaultResponseFormat: 'JSON',
clickhouseOptions: {
/* https://clickhouse.tech/docs/en/operations/settings/settings/ */
send_progress_in_http_headers: '1'
}
}
)Usage examples
Basically, this client supports data insertion, but you should consider collecting your data before passing it as an argument here. Use clickcache to prepare batches!
Insert
const response = await client.insert('table_strings', rows, {
format: 'CSVWithNames'
})Select
await clickhouseInstance.query<{ t: string }>('WITH now() as t SELECT t', {
format: 'TSV',
send_progress_in_http_headers: '1'
})DDL queries
await clickhouseInstance.query(`
CREATE TABLE strings (
date DateTime('UTC'),
string String
) Engine = ReplacingMergeTree()
PARTITION BY toMonday(date)
ORDER BY (date, string)
`, { noFormat: true })