chore: a large number of typo corrections in markdown files

doc/book/design/_index.md

@@ -10,7 +10,7 @@ perspective.  It will allow you to understand if Garage is a good fit for
 you, how to better use it, how to contribute to it, what can Garage could
 and could not do, etc.
 
-- **[Goals and use cases](@/documentation/design/goals.md):** This page explains why Garage was concieved and what practical use cases it targets.
+- **[Goals and use cases](@/documentation/design/goals.md):** This page explains why Garage was conceived and what practical use cases it targets.
 
 - **[Related work](@/documentation/design/related-work.md):** This pages presents the theoretical background on which Garage is built, and describes other software storage solutions and why they didn't work for us.
 
@@ -31,5 +31,3 @@ We love to talk and hear about Garage, that's why we keep a log here:
   - [(en, 2021-04-28) Distributed object storage is centralised](https://git.deuxfleurs.fr/Deuxfleurs/garage/src/commit/b1f60579a13d3c5eba7f74b1775c84639ea9b51a/doc/talks/2021-04-28_spirals-team/talk.pdf)
 
   - [(fr, 2020-12-02) Garage : jouer dans la cour des grands quand on est un hébergeur associatif](https://git.deuxfleurs.fr/Deuxfleurs/garage/src/commit/b1f60579a13d3c5eba7f74b1775c84639ea9b51a/doc/talks/2020-12-02_wide-team/talk.pdf)
-
-

doc/book/design/benchmarks/index.md

@@ -15,14 +15,14 @@ The more a user request will require intra-cluster requests to complete, the mor
 This is especially true for sequential requests: requests that must wait the result of another request to be sent.
 We designed Garage without consensus algorithms (eg. Paxos or Raft) to minimize the number of sequential and parallel requests.
 
-This serie of benchmarks quantifies the impact of this design choice.
+This series of benchmarks quantifies the impact of this design choice.
 
 ### On a simple simulated network
 
 We start with a controlled environment, all the instances are running on the same (powerful enough) machine.
 
-To control the network latency, we simulate the network with [mknet](https://git.deuxfleurs.fr/trinity-1686a/mknet) (a tool we developped, based on `tc` and the linux network stack).
-To mesure S3 endpoints latency, we use our own tool [s3lat](https://git.deuxfleurs.fr/quentin/s3lat/) to observe only the intra-cluster latency and not some contention on the nodes (CPU, RAM, disk I/O, network bandwidth, etc.).
+To control the network latency, we simulate the network with [mknet](https://git.deuxfleurs.fr/trinity-1686a/mknet) (a tool we developed, based on `tc` and the linux network stack).
+To measure S3 endpoints latency, we use our own tool [s3lat](https://git.deuxfleurs.fr/quentin/s3lat/) to observe only the intra-cluster latency and not some contention on the nodes (CPU, RAM, disk I/O, network bandwidth, etc.).
 Compared to other benchmark tools, S3Lat sends only one (small) request at the same time and measures its latency.
 We selected 5 standard endpoints that are often in the critical path: ListBuckets, ListObjects, GetObject, PutObject and RemoveObject.
 
@@ -32,7 +32,7 @@ In this first benchmark, we consider 5 instances that are located in a different
 
 Compared to garage, minio latency drastically increases on 3 endpoints: GetObject, PutObject, RemoveObject.
 
-We suppose that these requests on minio make transactions over Raft, involving 4 sequential requests: 1) sending the message to the leader, 2) having the leader dispatch it to the other nodes, 3) waiting for the confirmation of followers and finally 4) commiting it. With our current configuration, one Raft transaction will take around 400 ms. GetObject seems to correlate to 1 transaction while PutObject and RemoveObject seems to correlate to 2 or 3. Reviewing minio code would be required to confirm this hypothesis.
+We suppose that these requests on minio make transactions over Raft, involving 4 sequential requests: 1) sending the message to the leader, 2) having the leader dispatch it to the other nodes, 3) waiting for the confirmation of followers and finally 4) committing it. With our current configuration, one Raft transaction will take around 400 ms. GetObject seems to correlate to 1 transaction while PutObject and RemoveObject seems to correlate to 2 or 3. Reviewing minio code would be required to confirm this hypothesis.
 
 Conversely, garage uses an architecture similar to DynamoDB and never require global cluster coordination to answer a request.
 Instead, garage can always contact the right node in charge of the requested data, and can answer in as low as one request in the case of GetObject and PutObject. We also observed that Garage latency, while often lower to minio, is more dispersed: garage is still in beta and has not received any performance optimization yet.
@@ -50,7 +50,7 @@ We plot a similar graph as before:
 
 This new graph is very similar to the one before, neither minio or garage seems to benefit from this new topology, but they also do not suffer from it.
 
-Considering garage, this is expected: nodes in the same DC are put in the same zone, and then data are spread on different zones for data resiliency and availaibility.
+Considering garage, this is expected: nodes in the same DC are put in the same zone, and then data are spread on different zones for data resiliency and availability.
 Then, in the default mode, requesting data requires to query at least 2 zones to be sure that we have the most up to date information.
 These requests will involve at least one inter-DC communication.
 In other words, we prioritize data availability and synchronization over raw performances.

doc/book/design/internals.md

@@ -94,7 +94,7 @@ delete a tombstone, the following condition has to be met:
 
 - All nodes responsible for storing this entry are aware of the existence of
   the tombstone, i.e. they cannot hold another version of the entry that is
-  superseeded by the tombstone. This ensures that deleting the tombstone is
+  superseded by the tombstone. This ensures that deleting the tombstone is
   safe and that no deleted value will come back in the system.
 
 Garage uses atomic database operations (such as compare-and-swap and
@@ -141,4 +141,3 @@ 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.
-

doc/book/design/related-work.md

@@ -37,7 +37,7 @@ However, Amazon S3 source code is not open but alternatives were proposed.
 We identified Minio, Pithos, Swift and Ceph.
 Minio/Ceph enforces a total order, so properties similar to a (relaxed) filesystem.
 Swift and Pithos are probably the most similar to AWS S3 with their consistent hashing ring.
-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.
+However Pithos is not maintained anymore. More precisely the company that published Pithos version 1 has developed a second version 2 but has not open sourced it.
 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.
 
 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.
@@ -63,7 +63,7 @@ Due to its industry oriented design, Ceph is also far from being *Simple* to ope
 In a certain way, Ceph and MinIO are closer together than they are from Garage or OpenStack Swift.
 
 **[Pithos](https://github.com/exoscale/pithos):**
-Pithos has been abandonned and should probably not used yet, in the following we explain why we did not pick their design.
+Pithos has been abandoned and should probably not used yet, in the following we explain why we did not pick their design.
 Pithos was relying as a S3 proxy in front of Cassandra (and was working with Scylla DB too).
 From its designers' mouth, storing data in Cassandra has shown its limitations justifying the project abandonment.
 They built a closed-source version 2 that does not store blobs in the database (only metadata) but did not communicate further on it.