README.md

# go-ds-flatfs

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> A datastore implementation using sharded directories and flat files to store data

`go-ds-flatfs` is used by `go-ipfs` to store raw block contents on disk. It supports several sharding functions (prefix, suffix, next-to-last/*).

## Lead Maintainer

[Jakub Sztandera](https://github.com/kubuxu)

## Table of Contents

- [Install](#install)
- [Usage](#usage)
- [Contribute](#contribute)
- [License](#license)

## Install

`go-ds-flatfs` can be used like any Go module:


```
import "github.com/ipfs/go-ds-flatfs"
```

`go-ds-flatfs` uses [`Gx`](https://github.com/whyrusleeping/gx) and [`Gx-go`](https://github.com/whyrusleeping/gx-go) to handle dependendencies. Run `make deps` to download and rewrite the imports to their fixed dependencies.

## Usage

Check the [GoDoc module documentation](https://godoc.org/github.com/ipfs/go-ds-flatfs) for an overview of this module's
functionality.

### DiskUsage and Accuracy

This datastore implements the [`PersistentDatastore`](https://godoc.org/github.com/ipfs/go-datastore#PersistentDatastore) interface. It offers a `DiskUsage()` method which strives to find a balance between accuracy and performance. This implies:

* The total disk usage of a datastore is calculated when opening the datastore
* The current disk usage is cached frequently in a file in the datastore root (`diskUsage.cache` by default). This file is also
written when the datastore is closed.
* If this file is not present when the datastore is opened:
  * The disk usage will be calculated by walking the datastore's directory tree and estimating the size of each folder.
  * This may be a very slow operation for huge datastores or datastores with slow disks
  * The operation is time-limited (5 minutes by default).
  * Upon timeout, the remaining folders will be assumed to have the average of the previously processed ones.
* After opening, the disk usage is updated in every write/delete operation.

This means that for certain datastores (huge ones, those with very slow disks or special content), the values reported by
`DiskUsage()` might be reduced accuracy and the first startup (without a `diskUsage.cache` file present), might be slow.

If you need increased accuracy or a fast start from the first time, you can manually create or update the
`diskUsage.cache` file.

The file `diskUsage.cache` is a JSON file with two fields `diskUsage` and `accuracy`.  For example the JSON file for a
small repo might be:

```
{"diskUsage":6357,"accuracy":"initial-exact"}
```

`diskUsage` is the calculated disk usage and `accuracy` is a note on the accuracy of the initial calculation.  If the
initial calculation was accurate the file will contain the value `initial-exact`.  If some of the directories have too
many entries and the disk usage for that directory was estimated based on the first 2000 entries, the file will contain
`initial-approximate`.  If the calculation took too long and timed out as indicated above, the file will contain
`initial-timed-out`.

If the initial calculation timed out the JSON file might be:
```
{"diskUsage":7589482442898,"accuracy":"initial-timed-out"}

```

To fix this with a more accurate value you could do (in the datastore root):

    $ du -sb .
    7536515831332    .
    $ echo -n '{"diskUsage":7536515831332,"accuracy":"initial-exact"}' > diskUsage.cache

## Contribute

PRs accepted.

Small note: If editing the README, please conform to the [standard-readme](https://github.com/RichardLitt/standard-readme) specification.

## License

MIT © Protocol Labs, Inc.