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

docs(quickstart): add default password to quickstart #2875

Merged
merged 1 commit into from
Jul 13, 2021
Merged

docs(quickstart): add default password to quickstart #2875

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
35 changes: 17 additions & 18 deletions docs/quickstart.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,68 +2,67 @@

## Deploying DataHub

To deploy a new instance of DataHub, perform the following steps.
To deploy a new instance of DataHub, perform the following steps.

1. Install [docker](https://docs.docker.com/install/) and [docker-compose](https://docs.docker.com/compose/install/) (if using Linux). Make sure to allocate enough hardware resources for Docker engine. Tested & confirmed config: 2 CPUs, 8GB RAM, 2GB Swap area.


2. Launch the Docker Engine from command line or the desktop app.

2. Launch the Docker Engine from command line or the desktop app.

3. Install the DataHub CLI

a. Ensure you have Python 3.6+ installed & configured. (Check using `python3 --version`)

b. Run the following commands in your terminal

b. Run the following commands in your terminal

```
python3 -m pip install --upgrade pip wheel setuptools
python3 -m pip uninstall datahub acryl-datahub || true # sanity check - ok if it fails
python3 -m pip install --upgrade acryl-datahub
datahub version
```
If you see "command not found", try running cli commands with the prefix 'python3 -m' instead: `python3 -m datahub version`

If you see "command not found", try running cli commands with the prefix 'python3 -m' instead: `python3 -m datahub version`

4. To deploy DataHub, run the following CLI command from your terminal

```
datahub docker quickstart
datahub docker quickstart
```
Upon completion of this step, you should be able to navigate to the DataHub UI at [http://localhost:9002](http://localhost:9002) in your browser. You can sign in using `datahub` as username and any password (no password validation by default).

Upon completion of this step, you should be able to navigate to the DataHub UI at [http://localhost:9002](http://localhost:9002) in your browser. You can sign in using `datahub` as both the username and password.

5. To ingest the sample metadata, run the following CLI command from your terminal
```
datahub docker ingest-sample-data
```

That's it! To start pushing your company's metadata into DataHub, take a look at the [Metadata Ingestion Framework](../metadata-ingestion/README.md).

That's it! To start pushing your company's metadata into DataHub, take a look at the [Metadata Ingestion Framework](../metadata-ingestion/README.md).

## Resetting DataHub

To cleanse DataHub of all of it's state (e.g. before ingesting your own), you can use the CLI `nuke` command.
To cleanse DataHub of all of it's state (e.g. before ingesting your own), you can use the CLI `nuke` command.

```
datahub docker nuke
```


## Troubleshooting

### Command not found: datahub

If running the datahub cli produces "command not found" errors inside your terminal, your system may be defaulting to an older
If running the datahub cli produces "command not found" errors inside your terminal, your system may be defaulting to an older
version of Python. Try prefixing your `datahub` commands with `python3 -m`:

```
python3 -m datahub docker quickstart
```

### Miscellaneous Docker issues

There can be misc issues with Docker, like conflicting containers and dangling volumes, that can often be resolved by
pruning your Docker state with the following command. Note that this command removes all unused containers, networks, images (both dangling and unreferenced),
There can be misc issues with Docker, like conflicting containers and dangling volumes, that can often be resolved by
pruning your Docker state with the following command. Note that this command removes all unused containers, networks, images (both dangling and unreferenced),
and optionally, volumes.

```
docker system prune
```
```