Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Spelling and small clarifications to README #16

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Spelling and small clarifications to README #16

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
86 changes: 33 additions & 53 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,109 +1,89 @@
# 🚚 Bulker

Bulker is a tool for streaming and batching large amount of semi-structured data into data warehouses. It uses Kafka internally
Bulker is a tool for streaming and batching large amounts of semi-structured data into data warehouses. It uses Kafka internally.

## How it works?
## How it works

<p align="center">
<img src="./.docs/assets/bulker-summary.excalidraw.png" width="600" />
</p>

Send and JSON object to Bulker HTTP endpoint, and it will make sure it will be saved to data warehouse:
Send a JSON object to the Bulker HTTP endpoint, and it will ensure it is saved to the data warehouse:

* **JSON flattening**. Your object will be flattened - `{a: {b: 1}}` becomes `{a_b: 1}`
* **Schema managenent** for **semi-structured** data. For each field, bulker will make sure that a corresponding column exist in destination table. If not, Bulker
will create it. Type will be best-guessed by value, or it could be explicitely set via type hint as in `{"a": "test", "__sql_type_a": "varchar(4)"}`
* **Reliability**. Bulker will put the object to Kafka Queue immediately, so if datawarehouse is down, data won't be lost
* **Streaming** or **Batching**. Bulker will send data to datawarehouse either as soon it become available in Kafka (streaming) or after some time (batching). Most
data warehouses won't tolerate large number of inserts, that's why we implemented batching
* **JSON flattening**. The JSON object will be flattened - `{a: {b: 1}}` becomes `{a_b: 1}`.
* **Schema management** for **semi-structured** data. For each field, Bulker will ensure that a corresponding column exists in the destination table. If not, Bulker will create it. The type will be best-guessed by value, or it can be explicitly set via a type hint as in `{"a": "test", "__sql_type_a": "varchar(4)"}`.
* **Reliability**. Bulker will place the object in a Kafka queue immediately, so if the data warehouse is down, data won't be lost.
* **Streaming** or **Batching**. Bulker will send data to the data warehouse either as soon as it becomes available in Kafka (streaming) or after some time (batching). Most data warehouses won't tolerate a large number of inserts, which is why we implemented batching.

Bulker is at the 💜 of [Jitsu](https://github.com/jitsucom/jitsu), an open-source data integration platform.

Bulker is a 💜 of [Jitsu](https://github.com/jitsucom/jitsu), an open-source data integration platform.
See the full list of features below.

See full list of features below


Bulker is also available as a go library if you want to embed it into your application as opposed to use a HTTP-server
Bulker is also available as a Go library if you want to embed it into your application instead of using an HTTP server.

## Features

* 🛢️ **Batching** - Bulker sends data in batches in most efficient way for particular database. For example, for Postgres it uses
COPY command, for BigQuery it uses batch-files
* 🚿 **Streaming** - alternatively, Bulker can stream data to database. It is useful when number of records is low. Up to 10 records
per second for most databases
* 🐫 **Deduplication** - if configured, Bulker will deduplicate records by primary key
* 📋 **Schema management** - Bulker creates tables and columns on the fly. It also flattens nested JSON-objects. Example if you send `{"a": {"b": 1}}` to
bulker, it will make sure that there is a column `a_b` in the table (and will create it)
* 🦾 **Implicit typing** - Bulker infers types of columns from JSON-data.
* 📌 **Explicit typing** - Explicit types can be by type hints that are placed in JSON. Example: for event `{"a": "test", "__sql_type_a": "varchar(4)"}`
Bulker will make sure that there is a column `a`, and it's type is `varchar(4)`.
* 📈 **Horizontal Scaling**. Bulker scales horizontally. Too much data? No problem, just add Bulker instances!
* 📦 **Dockerized** - Bulker is dockerized and can be deployed to any cloud provider and k8s.
* ☁️ **Cloud Native** - each Bulker instance is stateless and is configured by only few environment variables.
* 🛢️ **Batching** - Bulker sends data in batches in the most efficient way for each particular database. For example, for Postgres, it uses the COPY command; for BigQuery, it uses batch files.
* 🚿 **Streaming** - Alternatively, Bulker can stream data to the database. This is useful when the number of records is low, up to 10 records per second for most databases.
* 🐫 **Deduplication** - If configured, Bulker will deduplicate records by primary key.
* 📋 **Schema management** - Bulker creates tables and columns on the fly. It also flattens nested JSON objects. For example, if you send `{"a": {"b": 1}}` to Bulker, it will ensure that there is a column `a_b` in the table (and will create it if needed).
* 🦾 **Implicit typing** - Bulker infers column types from JSON data.
* 📌 **Explicit typing** - Explicit types can be set by type hints placed in the JSON. For example, for the event `{"a": "test", "__sql_type_a": "varchar(4)"}`, Bulker will ensure that there is a column `a`, and its type is `varchar(4)`.
* 📈 **Horizontal Scaling** - Bulker scales horizontally. Too much data? No problem, just add more Bulker instances!
* 📦 **Dockerized** - Bulker is containerized and can be deployed to any cloud provider and Kubernetes.
* ☁️ **Cloud Native** - Each Bulker instance is stateless and is configured with only a few environment variables.

## Supported databases

Bulker supports the following databases:

* ✅ PostgresSQL <br/>
* ✅ Redshit <br/>
* ✅ PostgreSQL <br/>
* ✅ Redshift <br/>
* ✅ Snowflake <br/>
* ✅ Clickhouse <br/>
* ✅ ClickHouse <br/>
* ✅ BigQuery <br/>
* ✅ MySQL <br/>
* ✅ S3 <br/>
* ✅ GCS <br/>

Please see [Compatibility Matrix](.docs/db-feature-matrix.md) to learn what Bulker features are supported by each database.

Please see the [Compatibility Matrix](./.docs/db-feature-matrix.md) to learn which Bulker features are supported by each database.

## Documentation Links

> **Note**
> We highly recommend to read [Core Concepts](#core-concepts) below before diving into details
> We highly recommend reading the [Core Concepts](#core-concepts) below before diving into the details.

* [How to use Bulker as HTTP Service](./.docs/server-config.md)
* [Server Configuration](./.docs/server-config.md)
* [HTTP API](./.docs/http-api.md)
* How to use bulker as Go-lib *(coming soon)*
* How to use Bulker as a Go library *(coming soon)*

## Core Concepts

### Destinations

Bulker operates with destinations. Destination is database or
storage service (e.g. S3, GCS). Each destination has an ID and configuration
which is represented by JSON object.
Bulker operates with destinations. A destination is a database or storage service (e.g., S3, GCS). Each destination has an ID and configuration represented by a JSON object.

Bulker exposes HTTP API to load data into destinations, where those
destinations are referenced by their IDs.
Bulker exposes an HTTP API to load data into destinations, where those destinations are referenced by their IDs.

If destination is a database, you'll need to provide a destination table name.
If the destination is a database, you'll need to provide a destination table name.

### Event

The main unit of data in Bulker is an *event*. Event is a represented JSON-object
The main unit of data in Bulker is an *event*. An event is represented as a JSON object.

### Batching and Streaming (aka Destination Mode)

Bulker can send data to database in two ways:
* **Streaming**. Bulker sends evens to destinaion one by one. It is useful when number of events is low (less than 10 events per second for most DBs).
* **Batching**. Bulker accumulates events in batches and sends them periodically once batch is full or timeout is reached. Batching is more efficient for large amounts of events. Especially for cloud data-warehouses
(e.g. Postgres, Clickhouse, BigQuery).
Bulker can send data to databases in two ways:
* **Streaming**. Bulker sends events to the destination one by one. This is useful when the number of events is low (less than 10 events per second for most databases).
* **Batching**. Bulker accumulates events in batches and sends them periodically once the batch is full or a timeout is reached. Batching is more efficient for large amounts of events, especially for cloud data warehouses (e.g., PostgreSQL, ClickHouse, BigQuery).

<p align="center">
<img src="./.docs/assets/stream-batch.excalidraw.png" width="600" />
</p>

### Primary Keys and Deduplication

Optionally, Bulker can deduplicate events by primary key. It is useful when you same event can be sent to Bulker multiple times.
If available, Bulker uses primary keys, but for some data warehouses alternative strategies are used.
Optionally, Bulker can deduplicate events by primary key. This is useful when the same event can be sent to Bulker multiple times. If available, Bulker uses primary keys, but for some data warehouses, alternative strategies are used.

>[Read more about deduplication »](./.docs/db-feature-matrix.md)