February 2020 - Dev - lists.ceph.io

by Sage Weil

Here's what I'm currently thinking. First we should get a few things out of the way, like - removing deepsea and ansible https://github.com/ceph/ceph/pull/33126 - ceph orchestrator ... -> ceph orch ... https://github.com/ceph/ceph/pull/33131 - your rename PR, if the underlying bug is resolved Next I think we need to fix the shape of the CLI to resolve the service group vs service/daemon ambiguity. Here's my proposal: https://pad.ceph.com/p/orchestrator_cli Then I think we can proceed more quickly in parallel with adding the additional services (monitoring, nfs, etc.) and improving the cephadm internals. On the internals side, the core problem I see is _get_services(), which has basically two users: - callers in cephadm that need a recent view in order to make decisions about scheduling, placement - serve() and 'service ls --refresh', which need to trigger an actual scrape of the remote hosts. The serve() one is the most important, IMO: we need it to (1) be parallel, (2) gracefully handle errors for each host and raise appropriate health alerts, and (3) update the cache as appropriate. For the CLI case, whether it triggers the scrape synchrnously or somehow kicks serve() and waits is an probably-not-so-important detail. On the other hand, the remaining internal _get_services() callers should I think all just use the latest cached state. Right now the way the code is structured makes it very confusing which path is used for which, and the use of the async_map_completion help (currently, at least) makes it hard to tell which host failed. As for additional services (monitoring, nfs, etc.), I think that can proceed more quickly once we have the CLI and add/remove/update issues sorted out. I may start with a RFC PR on that, but I would really like some feedback on whether the proposal makes sense. Thanks! sage

4 years, 2 months

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Docubetter Meeting -- 2020 Feb 12

by John Zachary Dover

Docubetter Meeting -- 2020 Feb 12 Hi everyone. The next DocuBetter meeting is scheduled for: 12 Feb 2020 0930 PST 12 Feb 2020 1730 UTC 13 Feb 2020 0330 AEST Etherpad: https://pad.ceph.com/p/Ceph_Documentation Meeting: https://bluejeans.com/908675367 Agenda: 1. Crisp Getting Started Guide PR -- finalizing it 2. PG Repair Bug -- finalizing it 3. Other bugs -- removing very stale bugs 4. RADOS documentation -- the next big project 5. Documentation Hotline protocols Thanks, everyone. Zac Dover

4 years, 2 months

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PRs for the next nautilus point release

by Abhishek Lekshmanan

Current PRs in flight that will be tested are labelled with nautilus-batch-1. https://github.com/ceph/ceph/pulls?q=is%3Aopen+is%3Apr+label%3Anautilus-bat… Can the leads make sure that the critically necessary prs for nautilus are already labelled. Also in order to shorten the queue, please remove any prs that don't require immediate merge, so that we can start the final QE suites as soon as possible. -- Abhishek Lekshmanan SUSE Software Solutions Germany GmbH GF: Felix Imendörffer, Mary Higgins, Sri Rasiah, HRB 21284 (AG Nürnberg)

4 years, 2 months

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Ceph install overwrites python3.7/site-packages/easy-install.pth

by wmwsagara＠gmail.com

Hi I noted, when compile Ceph from source and install, the "make install" overwrites the /usr/local/lib/python3.7/site-packages/easy-install.pth. Before install Ceph, /usr/local/lib/python3.7/site-packages/easy-install.pth: import sys; sys.__plen = len(sys.path) import sys; new=sys.path[sys.__plen:]; del sys.path[sys.__plen:]; p=getattr(sys,'__egginsert',0); sys.path[p:p]=new; sys.__egginsert = p+len(new) After install Ceph, /usr/local/lib/python3.7/site-packages/easy-install.pth: ./ceph_volume-1.0.0-py3.7.egg ./ceph_volume_zfs-0.1.0-py3.7.egg ./ceph-1.0.0-py3.7.egg The source is git master (v15.1.0). Are you sure this overwrite is right? Did you all mean to append ceph stuff? Btw, I'm not a Python programmer, but as a sys admin it's quite scaring my previous content gone. Regards Sagara

4 years, 2 months

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Ceph Release Branch Management

by Patrick Donnelly

So we've started doing hotfix releases (v14.2.6 & v14.2.7), yay! Unfortunately, this process is involving some kind of rebase which is wrecking the release commit history. Exhibit A: https://github.com/ceph/ceph/pull/32602 I'm in the process of fixing my backports but for those looking after I'm done, my backport PR now has 156 commits including merges where only 2 should exist. This must not continue. Asking backporters to rebase every backport PR whenever we do a hotfix is a non-starter, IMHO. There's been discussions about using some various branching strategies to make this work better but that would complicate the existing release process and/or muck up upgrade tests. (In particular, it's been suggested that the release branch _only_ point to a tagged release.) Without entering into a drawn out discussion on how we could fix the release process, I'm going to suggest the following git workflow to avoid rebases within the current limitation of the release branch always pointing to the cutting edge state: (1) Hotfix need identified: save current branch state: git checkout $release git branch $release-save git push upstream $release-save # push branch save to GitHub for disaster recovery (2) In GitHub, restrict branch push rights on $release. (3) Hard reset to last tagged release: git reset --hard vX.Y.Z git push --force upstream HEAD:$release (4) Do hotfix merges. (5) Tag/test release. (6) Merge the saved branch onto the release branch: git merge $release-save git push upstream $release # no --force should be necessary! No history is lost. The commit history reflects the reality of what happened. No backport rebasing. -- Patrick Donnelly, Ph.D. He / Him / His Senior Software Engineer Red Hat Sunnyvale, CA GPG: 19F28A586F808C2402351B93C3301A3E258DD79D

4 years, 2 months

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Build Mgr Dashboard Frontend develop errors (git master, VERSION 15.1.0)

by Sagara Wijetunga

Hi When build git master, VERSION 15.1.0, develop following errors: Collecting nodeenv Downloading nodeenv-1.3.5-py2.py3-none-any.whl (21 kB) Installing collected packages: nodeenv Successfully installed nodeenv-1.3.5 * Install prebuilt node (10.18.1) . Traceback (most recent call last): File "src/pybind/mgr/dashboard/node-env/bin/nodeenv", line 8, in <module> sys.exit(main()) File "src/pybind/mgr/dashboard/node-env/lib/python3.7/site-packages/nodeenv.py", line 1113, in main create_environment(env_dir, opt) File "src/pybind/mgr/dashboard/node-env/lib/python3.7/site-packages/nodeenv.py", line 936, in create_environment install_node(env_dir, src_dir, opt) File "src/pybind/mgr/dashboard/node-env/lib/python3.7/site-packages/nodeenv.py", line 700, in install_node install_node_wrapped(env_dir, src_dir, opt) File "src/pybind/mgr/dashboard/node-env/lib/python3.7/site-packages/nodeenv.py", line 723, in install_node_wrapped download_node_src(node_url, src_dir, opt, prefix) File "src/pybind/mgr/dashboard/node-env/lib/python3.7/site-packages/nodeenv.py", line 552, in download_node_src dl_contents = io.BytesIO(urlopen(node_url).read()) File "src/pybind/mgr/dashboard/node-env/lib/python3.7/site-packages/nodeenv.py", line 580, in urlopen return urllib2.urlopen(req) File "/usr/local/lib/python3.7/urllib/request.py", line 222, in urlopen return opener.open(url, data, timeout) File "/usr/local/lib/python3.7/urllib/request.py", line 531, in open response = meth(req, response) File "/usr/local/lib/python3.7/urllib/request.py", line 641, in http_response 'http', request, response, code, msg, hdrs) File "/usr/local/lib/python3.7/urllib/request.py", line 569, in error return self._call_chain(*args) File "/usr/local/lib/python3.7/urllib/request.py", line 503, in _call_chain result = func(*args) File "/usr/local/lib/python3.7/urllib/request.py", line 649, in http_error_default raise HTTPError(req.full_url, code, msg, hdrs, fp) urllib.error.HTTPError: HTTP Error 404: Not Found gmake[2]: *** [src/pybind/mgr/dashboard/CMakeFiles/mgr-dashboard-nodeenv.dir/build.make:63: src/pybind/mgr/dashboard/node-env/bin/npm] Error 1 gmake[1]: *** [CMakeFiles/Makefile2:5982: src/pybind/mgr/dashboard/CMakeFiles/mgr-dashboard-nodeenv.dir/all] Error 2 gmake[1]: *** Waiting for unfinished jobs.... I had to use -DWITH_MGR_DASHBOARD_FRONTEND=OFF to continue to build without Mgr Dashboard Frontend. Once fix, I can compile and test again. Regards Sagara

4 years, 2 months

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Cephalocon Seoul is canceled

by Sage Weil

Hi everyone, We are sorry to announce that, due to the recent coronavirus outbreak, we are canceling Cephalocon for March 3-5 in Seoul. More details will follow about how to best handle cancellation of hotel reservations and so forth. Registrations will of course be refunded--expect an email with details in the next day or two. We are still looking into whether it makes sense to reschedule the event for later in the year. Thank you to everyone who has helped to plan this event, submitted talks, and agreed to sponsor. It makes us sad to cancel, but the safety of our community is of the utmost importance, and it was looking increasing unlikely that we could make this event a success. Stay tuned...

4 years, 2 months

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CDM is tomorrow (new EMEA time!)

by Sage Weil

Tomorrow, Feb 5, 1600 - 1800 UTC at https://bluejeans.com/908675367/ Note that this is 2 hours earlier than we've done in the past in order to be more friendly for Europe. The agenda is here: https://tracker.ceph.com/projects/ceph/wiki/CDM_05-FEB-2020 There are currently two items: - Octopus defaults. https://pad.ceph.com/p/octopus-rados-defaults Revisit default settings for the balancer, pg autoscaler, and rocksdb for the upcoming release. - cephadm update: what's done and what's left. There's plenty of time to add any other topics! Thanks, sage

4 years, 2 months

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Attempt to rethink log-based replication in Ceph on fast IO path

by Roman Penyaev

Hi Sage and all, I would like to share some of my scattered thoughts regarding IO performance improvements in Ceph. I touch on topics such as replicated log model, atomicity and full journaling on the objectstore side and how these constraints can be relaxed on IO path in order to squeeze maximum from a hard drive, at the same time still respecting the strong data consistency between replicas. Would be great to have a chance to discuss this in Seoul. Proposal in a few lines (for time saving) ----------------------------------------- 1. IO and management requests follow different paths: 1. Slow path for management requests (object creation/deletion, locks, etc) involves replicated log and primary-copy model, as it is right now. 2. Fast IO path (read, write requests) bypasses replicated log and makes clients responsible for sending data to each replica in a group (PG), I reference it as hybrid client-driven replication further in the text. 2. For the sake of simplicity client takes fast IO path only when group of replicas (PG) is in active and healthy state (active+client in terms of Ceph), otherwise client falls back to the slow path. Thus, client is not involved in the disaster recovery process (that's why hybrid). 3. Fast IO path (is taken only if group of replicas (PG) is in active and healthy state): 1. Reads of objects happen from different primaries in a group (PG), in order to spread the read load. 2. Writes are distributed by clients themselves to all replicas in a group (PG), avoiding one extra network hop. 4. Objectstore becomes journal-less and behaves similar to RAID 1, sacrificing atomicity and erasure coding (due to the "write hole" problem) for write performance, which becomes close to the write performance of a bare hard drive, but still keeping strong data consistency between replicas. !!!!! CAUTION: many letters !!!!! Introduction ------------ The most impressive IO performance numbers among products which provide data redundancy is certainly delivered by RAID and that is not a surprise: you just write a block to several disks in parallel, nothing more. Perfect. Then why Ceph can't achieve the same performance as RAID 1 on IO path, doing plain old write-in-parallel thing, but supplemented with scalability, CRUSH object placement and all other beloved things which make Ceph so powerful? The answer to this question lies in the replication design used by Ceph, namely log-based replication (PG log in Ceph terms), which is commonly used in storage and database systems and well described in many publications [1, 2, 3, 4]). Sequential replicated log indeed resolves issues related to atomicity, consistency, isolation and durability (this famous ACID buzzword acronym), especially in DBMS world where transactions are involved. But Ceph is a storage, not a database. Modern hard drives do not provide atomicity, strict writes ordering or durability. Hardware breaks all the rules and relax the constraints for the sake of performance, and software has to deal with it. Every modern filesystem can survive crashes (journals, COW, soft-updates, etc approaches are used), each application which cares about data consistency and can't rely on weak guarantees of POSIX file API (any DBMS for example) uses same methods (journals, COW, etc) to avoid inconsistency. Why then a storage solution, which works below a filesystem and on top of a hardware, should provide such strong ACID properties for IO? So what is a log-based replication which is a core of Ceph? Very simple: duplication (mirroring) of a log which consist of records describing storage operations. In order to duplicate something we have to be sure that an object which we duplicate has not been changed on the way, i.e. actually copy-paste does what it is supposed to do without nasty surprises. When we have a log of storage operations we need not only to deliver a recent record to a replica without corruption (and wrong order of operations in a log is a corruption) but also invoke this operation, which has been described in a log record. Not only that: new record in a log and operation, which is described by that record, should happen in one transaction, atomically, thus either we see a record and operation was performed, either no updates in a log and no operation consequences is visible. Having such a wonderful log (which obviously can survive a crash or even a nuclear strike) we can very easily answer The Most Important question for a high scalable, self-healing storage product: what has happened on other replicas while one of them took a nap (crashed). In other words we need such a complicated mechanism only for one thing: just to tell the exact data difference (e.g. in blocks of data and operations), what has been changed since a crash. Short quiz: can Сeph stay Ceph, but without any log-based replication? Yes, throw it away, no need to know the difference, just copy the whole bunch of data between replicas on any disaster recovery. Very slow on resyncing but fast on doing operations and simple. I'm, of course, exaggerating, but that is not so far from the truth. Talking about log-based replication I've never mentioned why it actually impacts the IO performance. Here is why: 1. Each update operation on a group of replicas (PG in terms of Ceph) has to be strictly ordered (log should stay in the same order on all replicas) i.e. no any other operation is allowed till an operation, which has been just started, reaches a hard drive with all cache flushes involved (who poked the code knows the notorious pglock, which protects the replication log from corruption and makes it be equal on all other replicas). 2. Severe restrictions on how actually the objectstore should be implemented: data and metadata should be fully journaled, because a log record of a replicated operation is stored along with the data in atomic manner. And what is wrong with journal in terms of performance? Nothing is wrong, it is just incredibly slow. (As a hint for the curious: enable "data=journal" option for ext4 filesystem, or "allow_cow" for xfs or just run any load which performs random writes on BTRFS (cow is always enabled), and yes, I promise, you feel the difference immediately). 3. Communication with a group of replicas (PG) happens only through a primary one, where a primary is responsible for keeping log in sync with other replicas. This primary-copy model [7] increases IO latency adding one extra network hop. All of the above about log-based replication in Ceph is about strong consistency, but not about performance. But can we have both? And what is actually a strong consistency? According to formal description "The protocol is said to support strong consistency if all accesses are seen by all parallel processes (or nodes, processors, etc.) in the same order (sequentially)" [6]. In simple words: one thread produces data to the same offset of a file, other threads read from the same offset, thus if strong consistency is respected readers never observe writes in a wrong order, e.g.: Writer Reader 1 Reader 2 Reader 3 -------------- -------------- -------------- --------------- write(file, "A"); b0 = read(file); b0 = read(file); b1 = read(file); write(file, "B"); b0 = read(file); b1 = read(file); b1 = read(file); In this example, readers are able to see a block of a file in the following states: Reader 1 Reader 2 Reader 3 --------- ---------- ---------- b0 == "A" b0 == "A" b0 == "B" b1 == "B" b1 == "A" b1 == "B" But what should never be observed and can be treated as a corruption is the following: b0 == "B" b1 == "A" Having that we can develop a simple rule: writes are not blocked and can come at any order, once a reader observes a change in a block, further reads to this block see the change. Most likely I describe here a wheel, which has some vivid name and was deeply buried in some publication in early 70th, but I do not care and call the rule as READ RECENT. The READ RECENT rule contains one splendid property: if read has never happened and order of writes is unknown, writes can be reordered regardless of their actual order. Practical example: there is a distributed storage where writes are unordered by nature; a full outage has happened and now replicas contain different blocks of data on the same offsets; read of the block has never happened after it was updated, thus we are able to bring replicas in sync choosing block from *any* replica. Marvelous. I will return to this highly important property when I start describing a new replication model. It turns out that strong consistency is not about strict requests ordering in a whole group of replicas (PG) it is only about order of reads against writes to the same block of data. That's it. So the question is left unanswered: can we have both strong consistency and high performance of IO? In order to answer this question, it is necessary to once again state the main requirements for the IO replication model, which remove the strict restrictions imposed by the log-based replication, positively affect performance and do not contradict strong consistency: 1. Get rid of extra hop on fast IO path by doing client-driven replication, at least when group of replicas (PG) is in active state and not doing any sort of disaster recovery. 2. Write requests are not strictly ordered to different offsets of an object and can be submitted to a hard drive in parallel. 3. Get rid of any journaling on fast IO path on objectstore side if possible. These are major requirements of constraint relaxation which can bring storage IO performance close to a bare hard drive bandwidths keeping strong consistency. In the next chapter I will cover all the requirements and will show how it can be implemented in practice. Separation of management requests from IO requests -------------------------------------------------- Requests in a storage cluster can be divided into two parts: management requests, which are object creation/deletion/listing, attributes setting/getting, lock management, etc; and IO requests, which do read or modify content of an object. The most prevailing requests are IO read and write requests, whose latency affect the overall performance of the entire cluster. The proposed requests separation has also a clear separation on metadata and data, which should be treated differently on the objectstore side, namely no journal should be involved in data modification (this will be discussed in detail below). Separating IO from management requests makes it possible to have a different primary replicas in a group of replicas (PG). For example in order to distribute a read load, primary can be chosen by an object id using persistent hashing (e.g. hash ring as proposed here [8]) instead of having a single primary. Write requests require replication involved and as was mentioned earlier client-driven replication model can be chosen in order to minimize latencies of an extra hop. Having different paths for several types of requests can be summarised as the following: 1. Management requests go through a persistent primary replica and log-based replication keeps strict ordering of operations in the entire group (PG). There are no changes to original Ceph architecture are proposed. 2. Read object requests are sent to different primaries according to some persistent hashing rule [8]. 3. Write requests can follow two IO paths, where on one of the paths client is fully responsible for request replication and delivers requests to all replicas in a group (PG). This path is always taken when group of replicas (PG) is in active and healthy state. The second IO path always lies through a primary, thus all requests are equally ordered (like it is right now implemented in Ceph). This path is taken in order to simplify all corner cases when a group of replicas (PG) is not in an active state and performs resynchronization process. Hybrid client-driven replication -------------------------------- Client-driven replication is quite self-descriptive: client is responsible for data delivery to replicas in a group (PG) avoiding one extra network hop, which exists in a primary-copy model. If client communicates with replicas in a group (PG) directly there is no longer the central point of synchronization, thus the one possible issue that needs to be considered is the possibility of write requests from several clients to come to replicas in different order: Client 1 Client 2 -------- -------- sends A to replica1 sends B to replica1 sends A to replica2 sends B to replica2 Replica 1 Replica 2 --------- --------- A B B A ** replica1 writes B to the disk and then overwrites B with A replica2 writes A to the disk and then overwrites A with B In this example both clients access the same block of the same object, but write requests become reordered differently because of the network. If this happened, then replicas can contain different data and they are out of sync. This issue has very bad consequences: future reads are undefined, especially if one of replicas crashed and clients start reading from another. This is definitely a data corruption. In order to solve the issue the order of writes to the same offset should be synchronized between replicas. Synchronization can be performed by marking each request with a timestamp, taking into consideration, that time flows equally on clients. Sub-microsecond time synchronization is not a problem [9], especially when there is the central point for all members - distributed state machine cluster, namely cluster of monitors in terms of Ceph. Having time synchronized, client marks write requests with a timestamp. According to the Thomas Write Rule [10] outdated requests are discarded by timestamp-based concurrency control keeping the correct order. However, in the case of the client-driven replication it is impossible to discard a change on all replicas simultaneously. Instead outdated request should be repeated for all replicas in a group (PG), here is an example: Client 1 Client 2 -------- -------- marks A with stamp 1 marks B with stamp 2 sends A to replica1 sends B to replica1 sends A to replica2 sends B to replica2 Replica 1 Replica 2 --------- -------------- -- on disk -- A B -- on disk -- B A ** replica1 writes B to the disk and rejects A with RETRY replica2 writes A to the disk and then overwrites A with B Request A is not written by replica1, because it is older then B request, which has been applied recently. Instead replica1 replied on A request with the RETRY error, which forces the client to mark request A with a new timestamp and resend it again to the whole group of replicas (PG): Client 1 -------- marks A with stamp 3 sends A to replica1 sends A to replica2 Replica 1 Replica 2 --------- --------- A A -- on disk -- B -- on disk -- B A ** replica1 overwrites B with A replica2 overwrites B with A The next question rises: why do we need to repeat write operation on all replicas, but not only on those who have just replied with the error? Let's consider overlapping data (here I do not consider the reason why clients send overlapping write requests concurrently, obviously this is a bug on a client side, but nevertheless we keep replicas in sync): Client 1 marks AAAA with stamp 1 Client 2 marks BBB with stamp 2 Client 3 marks C with stamp 3 Replica 1 Replica 2 --------- --------- C 3 BBB 2 C 3 AAAAA 1 AAAAA 1 --------- --------- below the line is the state on disk ABCBA AACAA ** replica1 writes AAAA, then overwrites the middle with BBB and then with C replica2 writes AAAA and overwrites the middle with C; BBB request is rejected Client2 receives the RETRY error and has to repeat BBB: Client 2 marks BBB with stamp 4 Replica 1 Replica 2 --------- --------- BBB 4 BBB 4 -- on disk -- C 3 -- on disk -- BBB 2 C 3 AAAAA 1 AAAAA 1 -------------- -------------- below the line is the state on disk ABBBA ABBBA On a second retry blocks on replicas become synchronized and write request is considered as completed and persistent on a drive. Since concurrent writes should never happen on well written client software (I expect distributed filesystems use lock primitives in order to prevent concurrent access) I do not expect any performance degradation because of frequent write retries, so current algorithm acts as a protection which keeps replicas in sync. I would like to emphasize several points obtained from the description of the algorithm described above: 1. Time synchronization on clients does not need to be very accurate, even hundreds of millisecond accuracy is enough. Described time-based algorithm does not depend on actual physical time, also it does not depend on real (absolute) order of writes sent by concurrent clients. Instead algorithm forces distributed cluster members to have a single view on requests order, which is enough to decide should request be rejected or executed. 2. Hybrid client-driven replication does not involve clients into data recovery process, thus leaving this job to replicas (exactly as it is right now). Here I describe the fast IO path only when group of replicas (PG) is in active and fully synchronized state. When group of replicas (PG) is in resynchronization state or client sends management type of requests (not IO), then for the sake of simplicity all communication goes through the main primary replica, as it is right now implemented in Ceph. Crash of a client in the middle of a replication ------------------------------------------------ Client-driven replication in contrast with a primary-copy model has another major issue which has to be considered in detail: crash of a client in the middle of a replication, that is, when client sends write request to one of replicas in a group (PG) and then crashes, leaving other replicas out of sync. Since there is no central point which controls the whole replication process, group of replicas have no information, was the write request confirmed by a whole group or something has happened to a client and special action should be performed. The issue can be solved by immediate confirmation of a successfully completed write request sent by non-primary replicas to a primary one. As was mentioned earlier, each object has its own primary which serves reads, thus write confirmations of a modified object are sent to a particular primary, which accounts number of confirmations for the modified object. If the confirmation did not come from all replicas in a group, then after the timeout elapsed a primary can start synchronizing an object on its own. In order to be sure that confirmations are accounted correctly, each of them is marked with a timestamp taken from a source write request, thus reordered and outdated confirmations can be discarded. I would like to stress the point, that accounting of confirmations happens in RAM only at runtime and does not involve any disk operations. If one of replicas crashes after a client crash then the whole group of replicas (PG) changes its state, further write requests take another path and forwarded to the main primary in a group and objects synchronization would be started anyway. The READ RECENT rule in action ------------------------------ In client-driven replication the order in which replicas receive requests and update blocks is out of control, thus readers can receive inconsistent data (also called weak consistency model [8]). To ensure that a once read block will never return to its previous value (READ RECENT rule mentioned earlier) all attempts to read non consistent (not confirmed from all replicas) block has to be delayed or rejected with an explicit error. Consider this scenario: 1. Client1 fans out write requests to all replicas. 2. Write request reaches only the primary one, client1 crashes. 3. Concurrent client2 reads the same block, since primary replica has applied write request from client1, client2 reads the requested block successfully and observes the change made by client1. 4. Primary replica crashes and new primary is selected, resync process in a group (PG) is started. 5. Concurrent client2 reads again the same block from a new primary, but since no one else observes the change written to previous primary made by client1, client2 receives old data. This scenario breaks the READ RECENT rule and introduces data corruption. In order to guarantee that block is consistent, read from line 3. should be delayed or rejected till all replicas confirm, that block is persistent. This can be achieved by the same confirmation mechanism described earlier: block is treated as inconsistent if it was modified and not all confirmations are received. I do not expect any performance degradation for generic read loads and can quote Sage here: “Reads in shared read/write workloads are usually unaffected by write operations. However, reads of uncommitted data can be delayed until the update commits. This increases read latency in certain cases, but maintains a fully consistent behavior for concurrent read and write operations in the event that all OSDs in the placement group simultaneously fail and the write ack is not delivered” [7]. Another issue, which has to be investigated in detail, is a full outage of a group of replicas (PG). When replicas are come back some of the objects can be out of sync, and without versioned log there is no any possibility to distinguish what blocks were updated recently and thus have a higher timestamp. As was mentioned earlier READ RECENT rule has one property: if block has not been read since the modification, then it is irrelevant what version of a block can be taken as a master copy. Thus, if read of an inconsistent block is delayed till it is persistent on all replicas, any replica in a group may be a “donor” of this particular block. From the statement above it follows that non-atomic or partial writes can happen. Indeed, from a filesystem or an application which acts as a cluster client, this requires special handling of a write failure, such as log replay, for dealing with inconsistencies left behind. But it will assume that each block on all replicas in a group (PG) has just one value, so in this way group remains synchronized and future reads won’t report different content in case of a change of the primary replica which serves reads. Journal-less objectstore for IO path ------------------------------------ Write atomicity requires certain support from an underlying hardware or software. Different techniques like journals, COW or soft-updates can be used in order to guarantee atomicity, but for no doubts this feature comes with a high price of the IO performance degradation. Since each modern filesystem or a DBMS application itself can take care of data inconsistency, atomicity constraint for the objectstore can be relaxed. In order to provide performance close to a bare hard drive, each replica has to submit writes to an underlying hardware immediately and without any requirements for request ordering or data atomicity. As was mentioned earlier, the reading delay mechanism of non-persistent blocks makes synchronization of objects after a complete shutdown possible without knowing where is the recent update. However, full synchronization can take days if an object is big enough. The problem of long resync can be solved by bookkeeping a bitmap of a possibly-out-of-sync blocks of an object. The algorithm can be described in just one sentence: “Bits are set before a write commences, and are cleared when there have been no writes for a while.” [11]. In highly distributed storage solution where a group of replicas (PG) can consist of different replicas during the whole life of a cluster and members in a group constantly changing, it is important not only to track out-of-sync blocks of an object, but also to keep versions of such changes, so that to answer the following question with minimal computational cost: what has changed in the object between versions N and M? Having versions of block changes in mind, one important modification to bitmap algorithm can be proposed: bitmap of out-of-sync blocks is stored in a file with a timestamp of a first write request in the name; after a certain number of changed blocks each replica does a rotation of the bitmap file, so that new bitmap file is created with a timestamp of a first write request which initiated the bitmap rotation process. The major difference to what RAID 1 does is, that bits are never cleared, but new bitmap file is created instead. Bits of modified but not yet persistent blocks (not all confirmations are received from replicas) migrate to a new bitmap file, in order to guarantee that block synchronization will take place in the future in case of possible replica failure. As timestamps of client write requests always change forward and the time on clients is synchronized (major requirement of proposed hybrid client-driven replication model), each replica will have similar view of block changes, so the question about data difference between the two versions can be easily answered. Proposed rotation of bitmap files can occupy extra disk space on a replica, especially when new files are created and never deleted. Deletion of bitmap files of old versions is not a problem: N files can be kept, rotation process spawns new bitmap file with a recent timestamp, meanwhile a file with the oldest timestamp is removed. If for some reason there will be a need to restore changes of an object version, which bitmap file was removed, then full object resynchronization should be performed. Summary ------- Proposed changes should relax a lot of constraints on fast IO path in current Ceph implementation keeping strong data consistency between replicas and bring performance of a single client to almost bare hard drive bandwidths. Even if there are logical holes in the model described above (there are), I still believe that eliminating them will not be impossible. PS. And yes, because of the “write hole” problem [12] erasure coding replication won’t survive full group outage without atomicity guarantees on the objectstore side. Sorry for that. -- Roman [1] R. Golding, Weak-consistency group communication and membership, PhD thesis, University of California, Santa Cruz, 1992. [2] Petersen et al., “Flexible Update Propagation for weakly consistent replication”, Proc. of the 16th ACM Symposium on Operating Systems Principles (SOSP), 1997. [3] M. Rabinovich. N. Gehani. A. Kononov, “Scalable Update Propagation in Epidemic Replicated Databases”,Advances in Database Technology - EDBT'96, Lecture Notes in Computer Science Vol. 1057, Springer, pp. 207-222. [4] G. Wuu, A Bernstein. „Efficient Solutions to the Replicated Log and Dictionary Problems”, Proceedings of the third ACM Symposium on Principles of Distributed Computing, August 1984, pp. 233-242. [6] https://en.wikipedia.org/wiki/Strong_consistency [7] Sage A. Weil. Ceph: Reliable, Scalable, And High-Performance Distributed Storage, PhD thesis, University of California, Santa Cruz, 2007. [8] Jiayuan Zhang, Yongwei Wu†, Yeh-Ching Chung. PROAR: A Weak Consistency Model For Ceph [9] Precision Time Protocol (PTP/IEEE-1588) [10] R. H. Thomas, “A majority consensus approach to concurrency control for multiple copy databases,” ACM Trans. Database Syst., vol. 4, no. 2, pp. 180–209, 1979. [11] Cluster support for MD/RAID 1, https://lwn.net/Articles/674085/ [12] https://en.wikipedia.org/wiki/RAID

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v14.2.7 Nautilus released

by Abhishek Lekshmanan

This is the seventh update to the Ceph Nautilus release series. This is a hotfix release primarily fixing a couple of security issues. We recommend that all users upgrade to this release. Notable Changes --------------- * CVE-2020-1699: Fixed a path traversal flaw in Ceph dashboard that could allow for potential information disclosure (Ernesto Puerta) * CVE-2020-1700: Fixed a flaw in RGW beast frontend that could lead to denial of service from an unauthenticated client (Or Friedmann) Blog Link: https://ceph.io/releases/v14-2-7-nautilus-released/ Getting Ceph ------------ * Git at git://github.com/ceph/ceph.git * Tarball at http://download.ceph.com/tarballs/ceph-14.2.7.tar.gz * For packages, see http://docs.ceph.com/docs/master/install/get-packages/ * Release git sha1: 3d58626ebeec02d8385a4cefb92c6cbc3a45bfe8 -- Abhishek Lekshmanan SUSE Software Solutions Germany GmbH

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