165 lines
8.3 KiB
Markdown
165 lines
8.3 KiB
Markdown
+++
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title = "Design draft"
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weight = 25
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**WARNING: this documentation is a design draft which was written before Garage's actual implementation.
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The general principle are similar, but details have not been updated.**
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#### Modules
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- `membership/`: configuration, membership management (gossip of node's presence and status), ring generation --> what about Serf (used by Consul/Nomad) : https://www.serf.io/? Seems a huge library with many features so maybe overkill/hard to integrate
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- `metadata/`: metadata management
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- `blocks/`: block management, writing, GC and rebalancing
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- `internal/`: server to server communication (HTTP server and client that reuses connections, TLS if we want, etc)
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- `api/`: S3 API
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- `web/`: web management interface
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#### Metadata tables
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**Objects:**
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- *Hash key:* Bucket name (string)
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- *Sort key:* Object key (string)
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- *Sort key:* Version timestamp (int)
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- *Sort key:* Version UUID (string)
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- Complete: bool
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- Inline: bool, true for objects < threshold (say 1024)
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- Object size (int)
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- Mime type (string)
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- Data for inlined objects (blob)
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- Hash of first block otherwise (string)
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*Having only a hash key on the bucket name will lead to storing all file entries of this table for a specific bucket on a single node. At the same time, it is the only way I see to rapidly being able to list all bucket entries...*
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**Blocks:**
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- *Hash key:* Version UUID (string)
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- *Sort key:* Offset of block in total file (int)
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- Hash of data block (string)
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A version is defined by the existence of at least one entry in the blocks table for a certain version UUID.
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We must keep the following invariant: if a version exists in the blocks table, it has to be referenced in the objects table.
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We explicitly manage concurrent versions of an object: the version timestamp and version UUID columns are index columns, thus we may have several concurrent versions of an object.
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Important: before deleting an older version from the objects table, we must make sure that we did a successfull delete of the blocks of that version from the blocks table.
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Thus, the workflow for reading an object is as follows:
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1. Check permissions (LDAP)
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2. Read entry in object table. If data is inline, we have its data, stop here.
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-> if several versions, take newest one and launch deletion of old ones in background
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3. Read first block from cluster. If size <= 1 block, stop here.
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4. Simultaneously with previous step, if size > 1 block: query the Blocks table for the IDs of the next blocks
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5. Read subsequent blocks from cluster
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Workflow for PUT:
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1. Check write permission (LDAP)
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2. Select a new version UUID
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3. Write a preliminary entry for the new version in the objects table with complete = false
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4. Send blocks to cluster and write entries in the blocks table
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5. Update the version with complete = true and all of the accurate information (size, etc)
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6. Return success to the user
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7. Launch a background job to check and delete older versions
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Workflow for DELETE:
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1. Check write permission (LDAP)
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2. Get current version (or versions) in object table
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3. Do the deletion of those versions NOT IN A BACKGROUND JOB THIS TIME
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4. Return succes to the user if we were able to delete blocks from the blocks table and entries from the object table
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To delete a version:
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1. List the blocks from Cassandra
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2. For each block, delete it from cluster. Don't care if some deletions fail, we can do GC.
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3. Delete all of the blocks from the blocks table
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4. Finally, delete the version from the objects table
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Known issue: if someone is reading from a version that we want to delete and the object is big, the read might be interrupted. I think it is ok to leave it like this, we just cut the connection if data disappears during a read.
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("Soit P un problème, on s'en fout est une solution à ce problème")
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#### Block storage on disk
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**Blocks themselves:**
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- file path = /blobs/(first 3 hex digits of hash)/(rest of hash)
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**Reverse index for GC & other block-level metadata:**
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- file path = /meta/(first 3 hex digits of hash)/(rest of hash)
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- map block hash -> set of version UUIDs where it is referenced
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Usefull metadata:
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- list of versions that reference this block in the Casandra table, so that we can do GC by checking in Cassandra that the lines still exist
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- list of other nodes that we know have acknowledged a write of this block, usefull in the rebalancing algorithm
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Write strategy: have a single thread that does all write IO so that it is serialized (or have several threads that manage independent parts of the hash space). When writing a blob, write it to a temporary file, close, then rename so that a concurrent read gets a consistent result (either not found or found with whole content).
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Read strategy: the only read operation is get(hash) that returns either the data or not found (can do a corruption check as well and return corrupted state if it is the case). Can be done concurrently with writes.
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**Internal API:**
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- get(block hash) -> ok+data/not found/corrupted
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- put(block hash & data, version uuid + offset) -> ok/error
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- put with no data(block hash, version uuid + offset) -> ok/not found plz send data/error
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- delete(block hash, version uuid + offset) -> ok/error
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GC: when last ref is deleted, delete block.
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Long GC procedure: check in Cassandra that version UUIDs still exist and references this block.
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Rebalancing: takes as argument the list of newly added nodes.
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- List all blocks that we have. For each block:
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- If it hits a newly introduced node, send it to them.
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Use put with no data first to check if it has to be sent to them already or not.
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Use a random listing order to avoid race conditions (they do no harm but we might have two nodes sending the same thing at the same time thus wasting time).
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- If it doesn't hit us anymore, delete it and its reference list.
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Only one balancing can be running at a same time. It can be restarted at the beginning with new parameters.
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#### Membership management
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Two sets of nodes:
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- set of nodes from which a ping was recently received, with status: number of stored blocks, request counters, error counters, GC%, rebalancing%
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(eviction from this set after say 30 seconds without ping)
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- set of nodes that are part of the system, explicitly modified by the operator using the web UI (persisted to disk),
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is a CRDT using a version number for the value of the whole set
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Thus, three states for nodes:
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- healthy: in both sets
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- missing: not pingable but part of desired cluster
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- unused/draining: currently present but not part of the desired cluster, empty = if contains nothing, draining = if still contains some blocks
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Membership messages between nodes:
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- ping with current state + hash of current membership info -> reply with same info
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- send&get back membership info (the ids of nodes that are in the two sets): used when no local membership change in a long time and membership info hash discrepancy detected with first message (passive membership fixing with full CRDT gossip)
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- inform of newly pingable node(s) -> no result, when receive new info repeat to all (reliable broadcast)
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- inform of operator membership change -> no result, when receive new info repeat to all (reliable broadcast)
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Ring: generated from the desired set of nodes, however when doing read/writes on the ring, skip nodes that are known to be not pingable.
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The tokens are generated in a deterministic fashion from node IDs (hash of node id + token number from 1 to K).
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Number K of tokens per node: decided by the operator & stored in the operator's list of nodes CRDT. Default value proposal: with node status information also broadcast disk total size and free space, and propose a default number of tokens equal to 80%Free space / 10Gb. (this is all user interface)
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#### Constants
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- Block size: around 1MB ? --> Exoscale use 16MB chunks
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- Number of tokens in the hash ring: one every 10Gb of allocated storage
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- Threshold for storing data directly in Cassandra objects table: 1kb bytes (maybe up to 4kb?)
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- Ping timeout (time after which a node is registered as unresponsive/missing): 30 seconds
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- Ping interval: 10 seconds
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- ??
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#### Links
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- CDC: <https://www.usenix.org/system/files/conference/atc16/atc16-paper-xia.pdf>
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- Erasure coding: <http://web.eecs.utk.edu/~jplank/plank/papers/CS-08-627.html>
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- [Openstack Storage Concepts](https://docs.openstack.org/arch-design/design-storage/design-storage-concepts.html)
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- [RADOS](https://doi.org/10.1145/1374596.1374606) [[pdf](https://ceph.com/assets/pdfs/weil-rados-pdsw07.pdf)]
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