Move design draft to separate file; write about GC in internals
This commit is contained in:
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ad7ab31411
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3 changed files with 247 additions and 147 deletions
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@ -29,6 +29,7 @@
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- [Design](./design/index.md)
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- [Related Work](./design/related_work.md)
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- [Internals](./design/internals.md)
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- [Design draft](./design/design_draft.md)
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- [Development](./development/index.md)
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- [Setup your environment](./development/devenv.md)
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162
doc/book/src/design/design_draft.md
Normal file
162
doc/book/src/design/design_draft.md
Normal file
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@ -0,0 +1,162 @@
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# Design draft
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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://ceph.com/wp-content/uploads/2016/08/weil-rados-pdsw07.pdf)
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@ -1,158 +1,95 @@
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**WARNING: this documentation is more a "design draft", which was written before Garage's actual implementation. The general principle is similar but details have not yet been updated.**
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# Internals
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#### Modules
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## Overview
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|
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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
|
||||
- `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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TODO: write this section
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#### Metadata tables
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- The Dynamo ring
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**Objects:**
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- CRDTs
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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)
|
||||
- *Sort key:* Version UUID (string)
|
||||
- Complete: bool
|
||||
- Inline: bool, true for objects < threshold (say 1024)
|
||||
- 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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- Consistency model of Garage tables
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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...*
|
||||
|
||||
**Blocks:**
|
||||
|
||||
- *Hash key:* Version UUID (string)
|
||||
- *Sort key:* Offset of block in total file (int)
|
||||
- Hash of data block (string)
|
||||
|
||||
A version is defined by the existence of at least one entry in the blocks table for a certain version UUID.
|
||||
We must keep the following invariant: if a version exists in the blocks table, it has to be referenced in the objects table.
|
||||
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.
|
||||
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.
|
||||
|
||||
Thus, the workflow for reading an object is as follows:
|
||||
|
||||
1. Check permissions (LDAP)
|
||||
2. Read entry in object table. If data is inline, we have its data, stop here.
|
||||
-> if several versions, take newest one and launch deletion of old ones in background
|
||||
3. Read first block from cluster. If size <= 1 block, stop here.
|
||||
4. Simultaneously with previous step, if size > 1 block: query the Blocks table for the IDs of the next blocks
|
||||
5. Read subsequent blocks from cluster
|
||||
|
||||
Workflow for PUT:
|
||||
|
||||
1. Check write permission (LDAP)
|
||||
2. Select a new version UUID
|
||||
3. Write a preliminary entry for the new version in the objects table with complete = false
|
||||
4. Send blocks to cluster and write entries in the blocks table
|
||||
5. Update the version with complete = true and all of the accurate information (size, etc)
|
||||
6. Return success to the user
|
||||
7. Launch a background job to check and delete older versions
|
||||
|
||||
Workflow for DELETE:
|
||||
|
||||
1. Check write permission (LDAP)
|
||||
2. Get current version (or versions) in object table
|
||||
3. Do the deletion of those versions NOT IN A BACKGROUND JOB THIS TIME
|
||||
4. Return succes to the user if we were able to delete blocks from the blocks table and entries from the object table
|
||||
|
||||
To delete a version:
|
||||
|
||||
1. List the blocks from Cassandra
|
||||
2. For each block, delete it from cluster. Don't care if some deletions fail, we can do GC.
|
||||
3. Delete all of the blocks from the blocks table
|
||||
4. Finally, delete the version from the objects table
|
||||
|
||||
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.
|
||||
|
||||
("Soit P un problème, on s'en fout est une solution à ce problème")
|
||||
|
||||
#### Block storage on disk
|
||||
|
||||
**Blocks themselves:**
|
||||
|
||||
- file path = /blobs/(first 3 hex digits of hash)/(rest of hash)
|
||||
|
||||
**Reverse index for GC & other block-level metadata:**
|
||||
|
||||
- file path = /meta/(first 3 hex digits of hash)/(rest of hash)
|
||||
- map block hash -> set of version UUIDs where it is referenced
|
||||
|
||||
Usefull metadata:
|
||||
|
||||
- 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
|
||||
- list of other nodes that we know have acknowledged a write of this block, usefull in the rebalancing algorithm
|
||||
|
||||
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).
|
||||
|
||||
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.
|
||||
|
||||
**Internal API:**
|
||||
|
||||
- get(block hash) -> ok+data/not found/corrupted
|
||||
- put(block hash & data, version uuid + offset) -> ok/error
|
||||
- put with no data(block hash, version uuid + offset) -> ok/not found plz send data/error
|
||||
- delete(block hash, version uuid + offset) -> ok/error
|
||||
|
||||
GC: when last ref is deleted, delete block.
|
||||
Long GC procedure: check in Cassandra that version UUIDs still exist and references this block.
|
||||
|
||||
Rebalancing: takes as argument the list of newly added nodes.
|
||||
|
||||
- List all blocks that we have. For each block:
|
||||
- If it hits a newly introduced node, send it to them.
|
||||
Use put with no data first to check if it has to be sent to them already or not.
|
||||
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).
|
||||
- If it doesn't hit us anymore, delete it and its reference list.
|
||||
|
||||
Only one balancing can be running at a same time. It can be restarted at the beginning with new parameters.
|
||||
|
||||
#### Membership management
|
||||
|
||||
Two sets of nodes:
|
||||
|
||||
- set of nodes from which a ping was recently received, with status: number of stored blocks, request counters, error counters, GC%, rebalancing%
|
||||
(eviction from this set after say 30 seconds without ping)
|
||||
- set of nodes that are part of the system, explicitly modified by the operator using the web UI (persisted to disk),
|
||||
is a CRDT using a version number for the value of the whole set
|
||||
|
||||
Thus, three states for nodes:
|
||||
|
||||
- healthy: in both sets
|
||||
- missing: not pingable but part of desired cluster
|
||||
- unused/draining: currently present but not part of the desired cluster, empty = if contains nothing, draining = if still contains some blocks
|
||||
|
||||
Membership messages between nodes:
|
||||
|
||||
- ping with current state + hash of current membership info -> reply with same info
|
||||
- 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)
|
||||
- inform of newly pingable node(s) -> no result, when receive new info repeat to all (reliable broadcast)
|
||||
- inform of operator membership change -> no result, when receive new info repeat to all (reliable broadcast)
|
||||
|
||||
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.
|
||||
The tokens are generated in a deterministic fashion from node IDs (hash of node id + token number from 1 to K).
|
||||
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)
|
||||
See this presentation (in French) for some first information:
|
||||
<https://git.deuxfleurs.fr/Deuxfleurs/garage/src/branch/main/doc/talks/2020-12-02_wide-team/talk.pdf>
|
||||
|
||||
|
||||
#### Constants
|
||||
## Garbage collection
|
||||
|
||||
- Block size: around 1MB ? --> Exoscale use 16MB chunks
|
||||
- Number of tokens in the hash ring: one every 10Gb of allocated storage
|
||||
- Threshold for storing data directly in Cassandra objects table: 1kb bytes (maybe up to 4kb?)
|
||||
- Ping timeout (time after which a node is registered as unresponsive/missing): 30 seconds
|
||||
- Ping interval: 10 seconds
|
||||
- ??
|
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A faulty garbage collection procedure has been the cause of
|
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[critical bug #39](https://git.deuxfleurs.fr/Deuxfleurs/garage/issues/39).
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This precise bug was fixed in the code, however there are potentially more
|
||||
general issues with the garbage collector being too eager and deleting things
|
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too early. This has been the subject of
|
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[PR #135](https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/135).
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||||
This section summarizes the discussions on this topic.
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|
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#### Links
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||||
Rationale: we want to ensure Garage's safety by making sure things don't get
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deleted from disk if they are still needed. Two aspects are involved in this.
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### 1. Garbage collection of table entries (in `meta/` directory)
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The `Entry` trait used for table entries (defined in `tables/schema.rs`)
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defines a function `is_tombstone()` that returns `true` if that entry
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represents an entry that is deleted in the table. CRDT semantics by default
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keep all tombstones, because they are necessary for reconciliation: if node A
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has a tombstone that supersedes a value `x`, and node B has value `x`, A has to
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keep the tombstone in memory so that the value `x` can be properly deleted at
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node `B`. Otherwise, due to the CRDT reconciliation rule, the value `x` from B
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would flow back to A and a deleted item would reappear in the system.
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Here, we have some control on the nodes involved in storing Garage data.
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Therefore we have a garbage collector that is able to delete tombstones UNDER
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CERTAIN CONDITIONS. This garbage collector is implemented in `table/gc.rs`. To
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delete a tombstone, the following condition has to be met:
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- All nodes responsible for storing this entry are aware of the existence of
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the tombstone, i.e. they cannot hold another version of the entry that is
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superseeded by the tombstone. This ensures that deleting the tombstone is
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safe and that no deleted value will come back in the system.
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Garage makes use of Sled's atomic operations (such as compare-and-swap and
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transactions) to ensure that only tombstones that have been correctly
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propagated to other nodes are ever deleted from the local entry tree.
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This GC is safe in the following sense: no non-tombstone data is ever deleted
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from Garage tables.
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|
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**However**, there is an issue with the way this interacts with data
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rebalancing in the case when a partition is moving between nodes. If a node has
|
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some data of a partition for which it is not responsible, it has to offload it.
|
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However that offload process takes some time. In that interval, the GC does not
|
||||
check with that node if it has the tombstone before deleting the tombstone, so
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perhaps it doesn't have it and when the offload finally happens, old data comes
|
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back in the system.
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||||
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**PR 135 mostly fixes this** by implementing a 24-hour delay before anything is
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garbage collected in a table. This works under the assumption that rebalances
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that follow data shuffling terminate in less than 24 hours.
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|
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**However**, in distributed systems, it is generally considered a bad practice
|
||||
to make assumptions that information propagates in a certain time interval:
|
||||
this consists in making a synchrony assumption, meaning that we are basically
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||||
assuming a computing model that has much stronger properties than otherwise. To
|
||||
maximize the applicability of Garage, we would like to remove this assumption,
|
||||
and implement a system where time does not play a role. To do this, we would
|
||||
need to find a way to safely disable the GC when data is being shuffled around,
|
||||
and safely detect that the shuffling has terminated and thus the GC can be
|
||||
resumed. This introduces some complexity to the protocol and hasn't been
|
||||
tackled yet.
|
||||
|
||||
### 2. Garbage collection of data blocks (in `data/` directory)
|
||||
|
||||
Blocks in the data directory are reference-counted. In Garage versions before
|
||||
PR #135, blocks could get deleted from local disk as soon as their reference
|
||||
counter reached zero. We had a mechanism to not trigger this immediately at the
|
||||
rc-reaches-zero event, but the cleanup could be triggered by other means (for
|
||||
example by a block repair operation...). PR #135 added a safety measure so that
|
||||
blocks never get deleted in a 10 minute interval following the time when the RC
|
||||
reaches zero. This is a measure to make impossible race conditions such as #39.
|
||||
We would have liked to use a larger delay (e.g. 24 hours), but in the case of a
|
||||
rebalance of data, this would have led to the disk utilization to explode
|
||||
during the rebalancing, only to shrink again after 24 hours. The 10-minute
|
||||
delay is a compromise that gives good security while not having this problem of
|
||||
disk space explosion on rebalance.
|
||||
|
||||
- CDC: <https://www.usenix.org/system/files/conference/atc16/atc16-paper-xia.pdf>
|
||||
- Erasure coding: <http://web.eecs.utk.edu/~jplank/plank/papers/CS-08-627.html>
|
||||
- [Openstack Storage Concepts](https://docs.openstack.org/arch-design/design-storage/design-storage-concepts.html)
|
||||
- [RADOS](https://ceph.com/wp-content/uploads/2016/08/weil-rados-pdsw07.pdf)
|
||||
|
|
|
|||
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Reference in a new issue