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complete blog post on nlnet task3

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content/blog/2023-12-preserving-read-after-write-consistency/index.md
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@ -1,5 +1,5 @@
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title="Maintaining Read-after-Write consistency in all circumstances"
title="Maintaining read-after-write consistency in all circumstances"
date=2023-12-25
+++
@ -10,8 +10,8 @@ of read and write quorums to guarantee read-after-write consistency, the only
consistency guarantee it provides. However, as of Garage v0.9.0, this guarantee
is not maintained when the composition of a cluster is updated and data is
moved between storage nodes. As part of our current NLnet-funded project, we
are developping a solution to this problem, that is briefly explained in this
blog post.*
are developing a solution to this problem. This blog post proposes a
high-level overview of the proposed solution.*
<!-- more -->
@ -19,16 +19,28 @@ blog post.*
Garage provides mainly one consistency guarantee, read-after-write for objects, which can be described as follows:
**Read-after-write consistency.** *If a client A writes an object x (e.g. using PutObject) and receives a `HTTP 200 OK` response, and later a client B tries to read object x (e.g. using GetObject), then B will read the version written by A, or a more recent version.*
**Read-after-write consistency.** *If a client A writes an object x (e.g. using
PutObject) and receives a `HTTP 200 OK` response, and later a client B tries to
read object x (e.g. using GetObject), then B will read the version written by
A, or a more recent version.*
The consistency guarantee offered by Garage is slightly more general than this
simplistic formulation, as it also applies to other S3 endpoints such as
ListObjects, which are always guaranteed to reflect the latest version of
objects inserted in a bucket.
objects inserted in a bucket. Note that Amazon calls this guarantee [*strong*
read-after-write consistency](https://aws.amazon.com/s3/consistency/) (they
also have it on AWS), to differentiate it from [another definition of
read-after-write
consistency](https://avikdas.com/2020/04/13/scalability-concepts-read-after-write-consistency.html)
that only applies to data that is read by the same client that wrote it. Since
that weaker form is also called
[read-your-writes](https://jepsen.io/consistency/models/read-your-writes), I
will always be referring to the strong version when using the term
"read-after-write consistency".
This consistency guarantee at the level of objects in the S3 API is in fact a
reflection of read-after-write consistency in the internal metadata engine of
Garage (which is a distributed key/value store with CRDT values). Reads and
In Garage, this consistency guarantee at the level of objects in the S3 API is
in fact a reflection of read-after-write consistency in the internal metadata
engine (which is a distributed key/value store with CRDT values). Reads and
writes to metadata tables use quorums of 2 out of 3 nodes for each operation,
ensuring that if operation B starts after operation A has completed, then there
is at least one node that is handling both operation A and B. In the case where
@ -36,16 +48,18 @@ A is a write (an update) and B is a read, that node will have the opportunity
to return the value written in A to the reading client B. A visual depiction
of this process can be found in [this
presentation](https://git.deuxfleurs.fr/Deuxfleurs/garage/src/commit/a8b0e01f88b947bc34c05d818d51860b4d171967/doc/talks/2023-09-20-ocp/talk.pdf)
on slide 32 (pages 57-64), and the algorithm is written down on slide 33 (page 54).
on slide 32 (pages 57-64), and the algorithm is written down on slide 33 (page
54).
Note that read-after-write guarantees [are broken and have always
been](https://git.deuxfleurs.fr/Deuxfleurs/garage/issues/147) for the bucket
and access key tables, which might not be something we can fix due to different
requirements on the quorums.
been](https://git.deuxfleurs.fr/Deuxfleurs/garage/issues/147) for metadata
related to buckets and access keys, which might not be something we can fix due
to different requirements on the quorums for the related metadata tables.
## Current issues with Read-after-Write consistency
Maintaining Read-after-Write consistency depends crucially on the intersection
## Current issues with read-after-write consistency
Maintaining read-after-write consistency depends crucially on the intersection
of the quorums being non-empty. There is however a scenario where these quorums
may be empty: when the set of nodes affected to storing some entries changes,
for instance when nodes are added or removed and data is being rebalanced
@ -70,111 +84,136 @@ A, B and C for a long time even after everyone else is aware of the new layout.
Moreover, data will be progressively moved from nodes B and C to nodes D and E,
which can take a long time depending on the quantity of data. This creates a
period of uncertainty as to where exactly the data is stored in the cluster.
Overall, this basically means that there is no way to guarantee the
intersection-of-quorums property, which is necessary for read-after-write, with
such a simplistic scheme.
Overall, this basically means that this simplistic scheme gives us no way to
guarantee the intersection-of-quorums property, which is necessary for
read-after-write.
Concretely, here is a very simple scenario in which read-after-write is broken:
1. A write operation is directed to nodes A, B and C (the old layout), and
receives OK responses from nodes B and C, forming a quorum, so the write
completes successfully. The written data sent to node A is lost or delayed
for a long time.
completes successfully. The written data then arrives to node A as well.
2. The new layout version is introduced in the cluster.
3. Before nodes D and E have had the chance to retrieve the data that was
stored on nodes B and C, a read operation for the same key is directed to
nodes A, D and E. This request receives OK responses from nodes D and E,
both containing no data but still forming a quorum of 2 responses. So the
read returns a null value instead of the value that was written before, even
though the write operation reported a success.
nodes A, D and E. D and E both return an OK response with no data (a null
value), because they is not yet up-to-date. An answer from node A is not
received in time. The two responses from nodes D and E, that contain no
data, still form a quorum, so the read returns a null value instead of the
value that was written before, even though the write operation reported a
success.
### Evidencing the issue with Jepsen testing
The first thing that I had to do for the NLnet project was to develop a testing
framework to show that read-after-write consistency issues could in fact arise
in Garage when the cluster layout was updated.
in Garage when the cluster layout was updated. To make such tests, I chose to
use the [Jepsen](https://jepsen.io/) testing framework, which helps us put
distributed software in complex adverse scenarios and verify whether they
respect some claimed consistency guarantees or not.
To make such tests, I chose to use the Jepsen testing framework, which helps us
put distributed software in complex adverse scenarios and verify whether they
respect some claimed consistency guarantees or not. I will not enter into too
much detail on the testing procedure, but suffice to say that issues were
found. More precisely, I was able to show that Garage did guarantee
read-after-write in a variety of adverse scenarios such as network partitions,
node crashes and clock scrambling, but that it was unable to do so as soon as
regular layout updates were introduced.
I will not enter into too much detail on the testing procedure, but suffice to
say that issues were found. More precisely, I was able to show that Garage
*did* guarantee read-after-write in a variety of adverse scenarios such as
network partitions, node crashes and clock scrambling, but that it was unable
to do so as soon as regular layout updates were introduced.
The progress of the Jepsen testing work is tracked in [PR
#544](https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/544)
## Fixing Read-after-Write consistency when layouts change
## Fixing read-after-write consistency when layouts change
To solve this issue, we will have to keep track of several information in the cluster.
We will also have to adapt our data transfer strategy and our quorums to make sure that
data can be found when it is requested.
To solve this issue, we will have to keep track of several pieces of
information in the cluster. We will also have to adapt our read/write quorums
and our data transfer strategy during rebalancing to make sure that data can be
found when it is requested.
Basically, here is how we will make sure that read-after-write is guaranteed:
First of all, we adapted Garage's code to be able to handle *several versions
of the cluster layout* that can be live in the cluster at the same time, to
keep track of multiple possible locations for data that is currently being
transferred between nodes. When multiple cluster layout versions are live,
write operations are directed to all of the nodes responsible for storing the
data in all the live versions. This ensures that the nodes in the oldest live
layout version always have an up-to-date view of the data, and that a read
quorum among those nodes is always a safe way to ensure read-after-write
consistency.
- Several versions of the cluster layout can be live in the cluster at the same time.
Nodes will progressively synchronize data so that the nodes in the newest live
layout version will catch up with data stored by nodes in the older live layout
version. Once nodes in the newer layout versions also have an up-to-date view
of the data, read operations will progressively start using a quorum of nodes
in the new layout version instead of the old one.
- When multiple cluster layout versions are live, the writes are directed to
all of the live versions.
Once all nodes are reading from newer layout versions, the oldest live versions
can be pruned. This means that writes will stop being directed to those nodes,
and the nodes will delete the data they were storing. Obviously, in the (very
common) case where some nodes are both in the old and new layout versions,
those nodes will not delete their data and they will continue to receive
writes.
- Nodes will synchronize data so that the nodes in the newest live layout
version will catch up with the older live layout versions.
### Performance impacts
- Reads are initially directed to the oldest live layout version, but will
progressively be moved to the newer versions once the synchronizations are
complete.
When multiple layout versions are live, writes are sent to all nodes
responsible for the partition of the requested key in all live layout
versions, and will return OK only when they receive a quorum of OK responses
for each of the live layout versions. This means that writes could be a bit
slower when a layout change is being synchronized in the cluster. Typically if
only one node is changing between the old and the new layout version, the write
operation will await for 3 responses among 4 requests, instead of the classical
2 responses among 3 requests.
- Once all nodes are reading from newer layout versions, the oldest live versions
can be pruned and the corresponding data deleted.
Concerning reads, they are still sent to only three nodes. Indeed, they are
sent to the nodes of the newest live layout version for which nodes have
completed a sync to catch up on existing data, and they only expect a quorum of
2 responses among the three nodes of that layout version. This way, reads
always stay as performant as when no layout change is being processed.
### Ensuring that new nodes are up-to-date
More precisely, the following modifications are made to how quorums are used in
read/write operations and how the sync is made:
An additional coordination mechanism is necessary for the data synchronization
procedure, to ensure that it is not started too early and that after it
completes, the nodes in the new layout indeed contains an up-to-date view of
the data.
- Writes are sent to all nodes responsible for the paritition in all live
layout versions, and will return OK only when they receive a quorum of OK
responses for each of the live layout versions. This means that writes could
be a bit slower whan a layout change is being synchronized in the cluster.
Indeed, imagine the following adverse scenario, which we want to avoid: a new
layout version is introduced in the cluster, and nodes immediately start
copying the data to the new nodes. However, some write operations that were
initiated before the new layout was introduced (or that were handled by a node
not yet aware of the layout) could be delayed, and the written data was not yet
received by the old nodes when they sent their copy of everything. When the
sync reports completion, and read operations start being directed to nodes of
the new layout, the written data might be missing from the nodes handling the
read, and read-after-write consistency could be violated.
- Reads are sent to the newest live layout version for which all nodes have
completed a sync to catch up on existing data, and only expect a quorum of 2
responses among the three nodes of that layout version. This way, reads
always stay as performant as when no layout update is in progress.
To avoid this situation, the synchronization operation is not initiated until
all cluster nodes have reported an "acknowledge" of the new layout version,
indicating that they have received the new layout version, and that they are no
longer processing write operations that were only addressed to nodes of the
previous layout versions. This makes sure that no data will be missed by the
sync: once the sync has started, no more data can be written only to old layout
versions. All of the writes will also be directed to the new nodes. More
exactly: all data that the source nodes of the sync does not yet contain when
the sync starts, is written by a write operation that is also directed at a
quorum of nodes among the new ones. This means that at the end of the sync, a
read quorum among the new nodes will necessarily return an up-to-date copy of
all of the data.
- A sync for a new layout version is not initiated until all cluster nodes have
acknowledged receiving that version and having finished all write operations
that were only addressed to previous layout versions. This makes sure that no
data will be missed by the sync: once the sync has started, no more data can
be written only to old layout versions. All of the writes will also be
directed to the new nodes (more exactly: all data that the source nodes of
the sync does not yet contain when the sync starts, is written by a write
operation that is also directed at a quorum of nodes among the new ones),
meaning that at the end of the sync, a read quorum among the new nodes will
necessarily return an up-to-date copy of all of the data.
- The oldest live layout version can be pruned once all nodes have completed a
sync to a newer version AND all nodes have acknowleged that fact, signaling
that they are no longer reading from that old version and are now reading
from a newer version instead. After being pruned, the old layout version is
no longer live, and nodes that are no longer designated to store data in the
newer layout versions can simply delete the data that they were storing.
### Details on update trackers
As you can see, the previous algorithm needs to keep track of a lot of
information in the cluster. Ths information is kept in three "layout update trackers",
information in the cluster. This information is kept in three "layout update trackers",
which keep track of the following information:
- The `ack` layout tracker keeps track of nodes receiving the latest layout
versions. A node will not "ack" (acknowledge) a new layout version while it
still has outstanding write operations that were not directed to the nodes
included in that version. Once all nodes have acknowledged a new version, we
know that all write operations that are made in the cluster are directed to
the nodes that were added in this layout version.
versions and indicating that they are no longer processing writes addressed
only to older layout versions. Once all nodes have acknowledged a new
version, we know that all in-progress and future write operations that are
made in the cluster are directed to the nodes that were added in this layout
version as well.
- The `sync` layout tracker keeps track of nodes finishing a full metadata table
sync, that was started after all nodes `ack`'ed the new layout version.
@ -185,6 +224,58 @@ which keep track of the following information:
more nodes are reading from an old version, at which point the corresponding
data can be deleted.
In the simplest scenario, only two layout versions are live, and these trackers
therefore can only have the values `n` (the new layout version) and `n-1` (the
old one). However this mechanism handles the general case where several
successive layout updates are being processed and more than two layout versions
are live simultaneously. The layout update trackers can take as values the
version numbers of any currently live layout version.
### What about dead nodes?
In this post I have used many times the phrases "once all nodes have
acknowledged a new layout version", or "once all nodes have completed a sync".
This obviously means that if some nodes are dead or unresponsive, the
processing of the layout update can be delayed indefinitely, and nodes in the
old layout versions will keep receiving writes and storing unnecessary data.
This is an unfortunate fact with the method proposed here. To cover for these
situations, the following workarounds can be made:
- A layout change is generally a supervised operation, meaning that a system
administrator may manually intervene to inform the cluster that certain nodes
are dead and that their layout tracker values should not be taken into
account.
- For the `sync` update tracker, we don't actually need to wait for all of the
synchronizations to terminate, quorums can be used instead as they should be
sufficient to ensure that the copied data is up-to-date.
- For the `ack` and `sync_ack` update trackers, we can automatically increase
them for all nodes (even dead ones) after a certain time delay, as there is
no reason for the changes taking more than e.g. 10 minutes to propagate in
regular conditions. We might not enable this behaviour by default, though,
due to its possible impacts on consistency.
## Current status and future work
The work described in this blog post is currently almost complete but it still
needs to be ironed out. I have made a first run of Jepsen testing on the new
code that showed that the changes seem to be fixing the issue. I will be
running longer and more intensive runs of Jepsen testing once the code is
finished, to make sure everything is fine. The changes will require a major
update of Garage: this will be the v0.10.0 release, which will probably be
finished in January or February of 2024. This update will be a very safe and
transparent update, as only the layout data structure is changed and nothing
related to object storage itself is touched.
If I had the time to do so, I would write the algorithm described in this post
in a formal way, in the form of a scientific paper. I believe such a paper
would be worthy of presenting at a scientific conference or journal, especially
given the fact that it is motivated by a very concrete use case and has been
validated quite thoroughly (with Jepsen). Unfortunately, this is not my
highest priority at the moment.
---
Written by [Alex Auvolat](https://adnab.me).