82 lines
5.9 KiB
Markdown
82 lines
5.9 KiB
Markdown
+++
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title="Doc Post"
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date=2018-08-20
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# Related work
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## Context
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Data storage is critical: it can lead to data loss if done badly and/or on hardware failure.
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Filesystems + RAID can help on a single machine but a machine failure can put the whole storage offline.
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Moreover, it put a hard limit on scalability. Often this limit can be pushed back far away by buying expensive machines.
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But here we consider non specialized off the shelf machines that can be as low powered and subject to failures as a raspberry pi.
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Distributed storage may help to solve both availability and scalability problems on these machines.
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Many solutions were proposed, they can be categorized as block storage, file storage and object storage depending on the abstraction they provide.
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## Overview
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Block storage is the most low level one, it's like exposing your raw hard drive over the network.
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It requires very low latencies and stable network, that are often dedicated.
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However it provides disk devices that can be manipulated by the operating system with the less constraints: it can be partitioned with any filesystem, meaning that it supports even the most exotic features.
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We can cite [iSCSI](https://en.wikipedia.org/wiki/ISCSI) or [Fibre Channel](https://en.wikipedia.org/wiki/Fibre_Channel).
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Openstack Cinder proxy previous solution to provide an uniform API.
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File storage provides a higher abstraction, they are one filesystem among others, which means they don't necessarily have all the exotic features of every filesystem.
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Often, they relax some POSIX constraints while many applications will still be compatible without any modification.
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As an example, we are able to run MariaDB (very slowly) over GlusterFS...
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We can also mention CephFS (read [RADOS](https://ceph.com/wp-content/uploads/2016/08/weil-rados-pdsw07.pdf) whitepaper), Lustre, LizardFS, MooseFS, etc.
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OpenStack Manila proxy previous solutions to provide an uniform API.
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Finally object storages provide the highest level abstraction.
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They are the testimony that the POSIX filesystem API is not adapted to distributed filesystems.
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Especially, the strong concistency has been dropped in favor of eventual consistency which is way more convenient and powerful in presence of high latencies and unreliability.
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We often read about S3 that pioneered the concept that it's a filesystem for the WAN.
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Applications must be adapted to work for the desired object storage service.
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Today, the S3 HTTP REST API acts as a standard in the industry.
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However, Amazon S3 source code is not open but alternatives were proposed.
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We identified Minio, Pithos, Swift and Ceph.
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Minio/Ceph enforces a total order, so properties similar to a (relaxed) filesystem.
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Swift and Pithos are probably the most similar to AWS S3 with their consistent hashing ring.
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However Pithos is not maintained anymore. More precisely the company that published Pithos version 1 has developped a second version 2 but has not open sourced it.
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Some tests conducted by the [ACIDES project](https://acides.org/) have shown that Openstack Swift consumes way more resources (CPU+RAM) that we can afford. Furthermore, people developing Swift have not designed their software for geo-distribution.
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There were many attempts in research too. I am only thinking to [LBFS](https://pdos.csail.mit.edu/papers/lbfs:sosp01/lbfs.pdf) that was used as a basis for Seafile. But none of them have been effectively implemented yet.
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## Existing software
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**[MinIO](https://min.io/):** MinIO shares our *Self-contained & lightweight* goal but selected two of our non-goals: *Storage optimizations* through erasure coding and *POSIX/Filesystem compatibility* through strong consistency.
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However, by pursuing these two non-goals, MinIO do not reach our desirable properties.
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Firstly, it fails on the *Simple* property: due to the erasure coding, MinIO has severe limitations on how drives can be added or deleted from a cluster.
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Secondly, it fails on the *Internet enabled* property: due to its strong consistency, MinIO is latency sensitive.
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Furthermore, MinIO has no knowledge of "sites" and thus can not distribute data to minimize the failure of a given site.
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**[Openstack Swift](https://docs.openstack.org/swift/latest/):**
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OpenStack Swift at least fails on the *Self-contained & lightweight* goal.
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Starting it requires around 8GB of RAM, which is too much especially in an hyperconverged infrastructure.
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We also do not classify Swift as *Simple*.
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**[Ceph](https://ceph.io/ceph-storage/object-storage/):**
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This review holds for the whole Ceph stack, including the RADOS paper, Ceph Object Storage module, the RADOS Gateway, etc.
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At its core, Ceph has been designed to provide *POSIX/Filesystem compatibility* which requires strong consistency, which in turn
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makes Ceph latency-sensitive and fails our *Internet enabled* goal.
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Due to its industry oriented design, Ceph is also far from being *Simple* to operate and from being *Self-contained & lightweight* which makes it hard to integrate it in an hyperconverged infrastructure.
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In a certain way, Ceph and MinIO are closer together than they are from Garage or OpenStack Swift.
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**[Pithos](https://github.com/exoscale/pithos):**
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Pithos has been abandonned and should probably not used yet, in the following we explain why we did not pick their design.
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Pithos was relying as a S3 proxy in front of Cassandra (and was working with Scylla DB too).
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From its designers' mouth, storing data in Cassandra has shown its limitations justifying the project abandonment.
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They built a closed-source version 2 that does not store blobs in the database (only metadata) but did not communicate further on it.
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We considered there v2's design but concluded that it does not fit both our *Self-contained & lightweight* and *Simple* properties. It makes the development, the deployment and the operations more complicated while reducing the flexibility.
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**[Riak CS](https://docs.riak.com/riak/cs/2.1.1/index.html):**
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*Not written yet*
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**[IPFS](https://ipfs.io/):**
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*Not written yet*
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## Specific research papers
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*Not yet written*
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