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machi/doc/cluster-of-clusters/name-game-sketch.org
Scott Lystig Fritchie e197df68e2 cluster-of-clusters WIP
2015-06-17 12:03:09 +09:00

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Machi cluster-of-clusters "name game" sketch

-- mode: org; --

1. "Name Games" with random-slicing style consistent hashing

Our goal: to distribute lots of files very evenly across a cluster of Machi clusters (hereafter called a "cluster of clusters" or "CoC").

2. Assumptions

Basic familiarity with Machi high level design and Machi's "projection"

The Machi high level design document contains all of the basic background assumed by the rest of this document.

Familiarity with the Machi cluster-of-clusters/CoC concept

This isn't yet well-defined (April 2015). However, it's clear from the Machi high level design document that Machi alone does not support any kind of file partitioning/distribution/sharding across multiple small Machi clusters. There must be another layer above a Machi cluster to provide such partitioning services.

The name "cluster of clusters" orignated within Basho to avoid conflicting use of the word "cluster". A Machi cluster is usually synonymous with a single Chain Replication chain and a single set of machines (e.g. 2-5 machines). However, in the not-so-far future, we expect much more complicated patterns of Chain Replication to be used in real-world deployments.

"Cluster of clusters" is clunky and long, but we haven't found a good substitute yet. If you have a good suggestion, please contact us! ^_^

Using the cluster-of-clusters quick-and-dirty prototype as an architecture sketch, let's now assume that we have N independent Machi clusters. We wish to provide partitioned/distributed file storage across all N clusters. We call the entire collection of N Machi clusters a "cluster of clusters", or abbreviated "CoC".

Continue CoC prototype's assumption: a Machi cluster is unaware of CoC

Let's continue with an assumption that an individual Machi cluster inside of the cluster-of-clusters is completely unaware of the cluster-of-clusters layer.

We may need to break this assumption sometime in the future? It isn't quite clear yet, sorry.

Analogy: "neighborhood : city :: Machi : cluster-of-clusters"

Analogy: The word "machi" in Japanese means small town or neighborhood. As the Tokyo Metropolitan Area is built from many machis and smaller cities, therefore a big, partitioned file store can be built out of many small Machi clusters.

The reader is familiar with the random slicing technique

I'd done something very-very-nearly-identical for the Hibari database 6 years ago. But the Hibari technique was based on stuff I did at Sendmail, Inc, so it felt old news to me. {shrug}

The Hibari documentation has a brief photo illustration of how random slicing works, see Hibari Sysadmin Guide, chain migration

For a comprehensive description, please see these two papers:

Reliable and Randomized Data Distribution Strategies for Large Scale Storage Systems Alberto Miranda et al. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.226.5609 (short version, HIPC'11)

Random Slicing: Efficient and Scalable Data Placement for Large-Scale Storage Systems Alberto Miranda et al. DOI: http://dx.doi.org/10.1145/2632230 (long version, ACM Transactions on Storage, Vol. 10, No. 3, Article 9, 2014)

We use random slicing to map CoC file names -> Machi cluster ID/name

We will use a single random slicing map. This map (called Map in the descriptions below), together with the random slicing hash function (called rs_hash() below), will be used to map:

CoC client-visible file name -> Machi cluster ID/name/thingie

Machi cluster ID/name management: TBD, but, really, should be simple

The mapping from:

Machi CoC member ID/name/thingie -> ???

… remains To Be Determined. But, really, this is going to be pretty simple. The ID/name/thingie will probably be a human-friendly, printable ASCII string, and the "???" will probably be a single Machi cluster projection data structure.

The Machi projection is enough information to contact any member of that cluster and, if necessary, request the most up-to-date projection information required to use that cluster.

It's likely that the projection given by this map will be out-of-date, so the client must be ready to use the standard Machi procedure to request the cluster's current projection, in any case.

3. A simple illustration

I'm borrowing an illustration from the HibariDB documentation here, but it fits my purposes quite well. (And I originally created that image, and the use license is OK.)

/greg/machi/media/commit/44c22bf752a891edf6478c927bd94106c6cbe5b9/doc/cluster-of-clusters/migration-4.png

Illustration of 'Map', using four Machi clusters

Assume that we have a random slicing map called Map. This particular Map maps the unit interval onto 4 Machi clusters:

Hash range Cluster ID
0.00 - 0.25 Cluster1
0.25 - 0.33 Cluster4
0.33 - 0.58 Cluster2
0.58 - 0.66 Cluster4
0.66 - 0.91 Cluster3
0.91 - 1.00 Cluster4

Then, if we had CoC file name "foo", the hash SHA("foo") maps to about 0.05 on the unit interval. So, according to Map, the value of rs_hash("foo",Map) = Cluster1. Similarly, SHA("hello") is about 0.67 on the unit interval, so rs_hash("hello",Map) = Cluster3.

4. An additional assumption: clients will want some control over file placement

We will continue to use the 4-cluster diagram from the previous section.

When a client wishes to append data to a Machi file, the Machi server chooses the file name & byte offset for storing that data. This feature is why Machi's eventual consistency operating mode is so nifty: it allows us to merge together files safely at any time because any two client append operations will always write to different files & different offsets.

Our new assumption: client control over initial file placement

The CoC management scheme may decide that files need to migrate to other clusters. The reason could be for storage load or I/O load balancing reasons. It could be because a cluster is being decomissioned by its owners. There are many legitimate reasons why a file that is initially created on cluster ID X has been moved to cluster ID Y.

However, there are also legitimate reasons for why the client would want control over the choice of Machi cluster when the data is first written. The single biggest reason is load balancing. Assuming that the client (or the CoC management layer acting on behalf of the CoC client) knows the current utilization across the participating Machi clusters, then it may be very helpful to send new append() requests to under-utilized clusters.

Cool! Except for a couple of problems…

If the client wants to store some data on Cluster2 and therefore sends an append("foo",CoolData) request to the head of Cluster2 (which the client magically knows how to contact), then the result will look something like {ok,"foo.s923.z47",ByteOffset}.

Therefore, the file name "foo.s923.z47" must be used by any Machi CoC client in order to retrieve the CoolData bytes.

Problem #1: "foo.s923.z47" doesn't always map via random slicing to Cluster2

… if we ignore the problem of "CoC files may be redistributed in the future", then we still have a problem.

In fact, the value of ps_hash("foo.s923.z47",Map) is Cluster1.

Problem #2: We want CoC files to move around automatically

If the CoC client stores two pieces of information, the file name "foo.s923.z47" and the Cluster ID Cluster2, then what happens when the cluster-of-clusters system decides to rebalance files across all machines? The CoC manager may decide to move our file to Cluster66.

How will a future CoC client wishes to retrieve CoolData when Cluster2 no longer stores the required file?

When migrating the file, we could put a "pointer" on Cluster2 that points to the new location, Cluster66.

This scheme is a bit brittle, even if all of the pointers are always created 100% correctly. Also, if Cluster2 is ever unavailable, then we cannot fetch our CoolData, even though the file moved away from Cluster2 several years ago.

The scheme would also introduce extra round-trips to the servers whenever we try to read a file where we do not know the most up-to-date cluster ID for.

We could store a pointer to file "foo.s923.z47"'s location in an LDAP database!

Or we could store it in Riak. Or in another, external database. We'd rather not create such an external dependency, however. Furthermore, we would also have the same problem of updating this external database each time that a file is moved/rebalanced across the CoC.

5. Proposal: Break the opacity of Machi file names, slightly

Assuming that Machi keeps the scheme of creating file names (in response to append() and sequencer_new_range() calls) based on a predictable client-supplied prefix and an opaque suffix, e.g.,

append("foo",CoolData) -> {ok,"foo.s923.z47",ByteOffset}.

… then we propose that all CoC and Machi parties be aware of this naming scheme, i.e. that Machi assigns file names based on:

ClientSuppliedPrefix ++ "." ++ SomeOpaqueFileNameSuffix

The Machi system doesn't care about the file name – a Machi server will treat the entire file name as an opaque thing. But this document is called the "Name Game" for a reason!

What if the CoC client could peek inside of the opaque file name suffix in order to remove (or add) the CoC location information that we need?

The details: legend

  • T = the target CoC member/Cluster ID chosen at the time of append()
  • p = file prefix, chosen by the CoC client (This is exactly the Machi client-chosen file prefix).
  • s.z = the Machi file server opaque file name suffix (Which we happen to know is a combination of sequencer ID plus file serial number. This implementation may change, for example, to use a standard GUID string (rendered into ASCII hexadecimal digits) instead.)
  • K = the CoC placement key

We use a variation of rs_hash(), called rs_hash_with_float(). The former uses a string as its 1st argument; the latter uses a floating point number as its 1st argument. Both return a cluster ID name thingie.

%% type specs, Erlang style
-spec rs_hash(string(), rs_hash:map()) -> rs_hash:cluster_id().
-spec rs_hash_with_float(float(), rs_hash:map()) -> rs_hash:cluster_id().

NOTE: Use of floating point terms is not required. For example, integer arithmetic could be used, if using a sufficiently large interval to create an even & smooth distribution of hashes across the expected maximum number of clusters.

For example, if the maximum CoC cluster size would be 4,000 individual Machi clusters, then a minimum of 12 bits of integer space is required to assign one integer per Machi cluster. However, for load balancing purposes, a finer grain of (for example) 100 integers per Machi cluster would permit file migration to move increments of approximately 1% of single Machi cluster's storage capacity. A minimum of 19 bits of hash space would be necessary to accomodate these constraints.

The details: CoC file write

  1. CoC client chooses p and T (i.e., the file prefix & target cluster)
  2. CoC client requests @ cluster T: append(p,...) -> {ok,p.s.z,ByteOffset}
  3. CoC client knows the CoC Map
  4. CoC client calculates a value K such that rs_hash_with_float(K,Map) = T
  5. CoC stores/uses the file name p.s.z.K.

The details: CoC file read

  1. CoC client knows the file name p.s.z.K and parses it to find K's value.
  2. CoC client knows the CoC Map
  3. Coc calculates rs_hash_with_float(K,Map) = T
  4. CoC client requests @ cluster T: read(p.s.z,...) -> … success!

The details: calculating 'K', the CoC placement key

  1. We know Map, the current CoC mapping.
  2. We look inside of Map, and we find all of the unit interval ranges that map to our desired target cluster T. Let's call this list MapList = [Range1=(start,end],Range2=(start,end],...].
  3. In our example, T=Cluster2. The example Map contains a single unit interval range for Cluster2, [(0.33,0.58]].
  4. Choose a uniformally random number r on the unit interval.
  5. Calculate placement key K by mapping r onto the concatenation of the CoC hash space range intervals in MapList. For example, if r=0.5, then K = 0.33 + 0.5*(0.58-0.33) = 0.455, which is exactly in the middle of the (0.33,0.58] interval.
  6. If necessary, encode K in a file name-friendly manner, e.g., convert it to hexadecimal ASCII digits to create file name p.s.z.K.

The details: calculating 'K', an alternative method

If the Law of Large Numbers and our random number generator do not create the kind of smooth & even distribution of files across the CoC as we wish, an alternative method of calculating K follows.

If each server in each Machi cluster keeps track of the CoC Map and also of all values of K for all files that it stores, then we can simply ask a cluster member to recommend a value of K that is least represented by existing files.

6. File migration (aka rebalancing/reparitioning/redistribution)

What is "file migration"?

As discussed in section 5, the client can have good reason for wanting to have some control of the initial location of the file within the cluster. However, the cluster manager has an ongoing interest in balancing resources throughout the lifetime of the file. Disks will get full, hardware will change, read workload will fluctuate, etc etc.

This document uses the word "migration" to describe moving data from one CoC cluster to another. In other systems, this process is described with words such as rebalancing, repartitioning, and resharding. For Riak Core applications, the mechanisms are "handoff" and "ring resizing". See the Hadoop file balancer for another example.

A simple variation of the Random Slicing hash algorithm can easily accomodate Machi's need to migrate files without interfering with availability. Machi's migration task is much simpler due to the immutable nature of Machi file data.

Change to Random Slicing

The map used by the Random Slicing hash algorithm needs a few simple changes to make file migration straightforward.

  • Add a "generation number", a strictly increasing number (similar to a Machi cluster's "epoch number") that reflects the history of changes made to the Random Slicing map
  • Use a list of Random Slicing maps instead of a single map, one map per possibility that files may not have been migrated yet out of that map.

As an example:

/greg/machi/media/commit/44c22bf752a891edf6478c927bd94106c6cbe5b9/doc/cluster-of-clusters/migration-3to4.png

Illustration of 'Map', using four Machi clusters

And the new Random Slicing map might look like this:

Generation number 7
SubMap 1
Hash range Cluster ID
0.00 - 0.33 Cluster1
0.33 - 0.66 Cluster2
0.66 - 1.00 Cluster3
SubMap 2
Hash range Cluster ID
0.00 - 0.25 Cluster1
0.25 - 0.33 Cluster4
0.33 - 0.58 Cluster2
0.58 - 0.66 Cluster4
0.66 - 0.91 Cluster3
0.91 - 1.00 Cluster4

When a new Random Slicing map contains a single submap, then its use is identical to the original Random Slicing algorithm. If the map contains multiple submaps, then the access rules change a bit:

  • Write operations always go to the latest/largest submap.

  • Read operations attempt to read from all unique submaps.

    • Skip searching submaps that refer to the same cluster ID.

      • In this example, unit interval value 0.10 is mapped to Cluster1 by both submaps.
    • Read from latest/largest submap to oldest/smallest submap.
    • If not found in any submap, search a second time (to handle races with file copying between submaps).
    • If the requested data is found, optionally copy it directly to the latest submap (as a variation of read repair which really simply accelerates the migration process and can reduce the number of operations required to query servers in multiple submaps).

The cluster-of-clusters manager is responsible for:

  • Managing the various generations of the CoC Random Slicing maps, including distributing them to CoC clients.
  • Managing the processes that are responsible for copying "cold" data, i.e., files data that is not regularly accessed, to its new submap location.
  • When migration of a file to its new cluster is confirmed successful, delete it from the old cluster.

In example map #7, the CoC manager will copy files with unit interval assignments in (0.25,0.33], (0.58,0.66], and (0.91,1.00] from their old locations in cluster IDs Cluster1/2/3 to their new cluster, Cluster4. When the CoC manager is satisfied that all such files have been copied to Cluster4, then the CoC manager can create and distribute a new map, such as:

Generation number 8
SubMap 1
Hash range Cluster ID
0.00 - 0.25 Cluster1
0.25 - 0.33 Cluster4
0.33 - 0.58 Cluster2
0.58 - 0.66 Cluster4
0.66 - 0.91 Cluster3
0.91 - 1.00 Cluster4

One limitation of HibariDB that I haven't fixed is not being able to perform more than one migration at a time. The trade-off is that such migration is difficult enough across two submaps; three or more submaps becomes even more complicated.

Fortunately for Machi, its file data is immutable and therefore can easily manage many migrations in parallel, i.e., its submap list may be several maps long, each one for an in-progress file migration.

Acknowledgements

The source for the "migration-4.png" and "migration-3to4.png" images come from the HibariDB documentation.