Sharding

This is an in-depth description of how sharding works. For an overview of running a sequins cluster, including notes on how it should behave, see Running A Distributed Cluster.

The algorithm described here is effectively isolated between databases. Therefore, this document speaks to the operations on a single sequins database, rather than everything at once.

Design Principles

  • Sequins should be able to scale horizontally by just adding nodes to an existing cluster. This should require as little shared state (in Zookeeper) as possible, and that shared state should be considered ephemeral. Specifically, sequins should absolutely not require shared state in the read path; meaning that any node must be able to continue serving requests without any shared state available.

  • To minimize implementation complexity, sharding should be as static as possible. A cluster must reshard a database once at startup, and once whenever a new version is available, but should not dynamically correct over- or underreplication.

  • Since sequins is a more-or-less static, stateless store, and since writes are async and affect the entire database at once, we don't have to think too hard about consistency. But one important property to try to guarantee is monotonic reads; if you talk to a single sequins instance during a version upgrade, you get only records from version (N-1) until the node upgrades, and then only records from version N.

Shared State in Zookeeper

Zookeeper is used for shared state. To minimize complexity and stay robust to Zookeeper failure, the code only supports two operations:

  • Publishing ephemeral keys into a znode
  • Listing a znode's children and caching the state locally

All state must be considered possibly minutes, hours or years stale. All read operations read the cache; the syncing process happens separately.

All znodes described below are prefixed under a root which includes the cluster name and a protocol number (currently v1).

Properties of a Version

A version is an immutable map of keys to values, existing in the source root as a collection of N files. Keys can be duplicated in the map, but only one value will be loaded (which one is picked is undefined).

In addition, each version is sharded into N partitions. Each partition K contains the keyspace where hashCode(key) % N == K.

We use java's hashCode algorithm as the hashing function, and the number of files as N, because that allows us to treat many datasets as pre-sharded by Hadoop's shuffle step. In particular, any Hadoop job with the default Partitioner will produce a version where each file has the keyspace of one and only one partition.

However, this isn't a guarantee, so we treat this only as a possible optimization.

Upgrades

This is the meatiest part of the distributed algorithm: how nodes discover and load new versions.

On startup (or when reconnecting to zookeeper), a node

  1. Creates an ephemeral znode under /nodes for itself

  2. Starts watching /nodes. On startup, it waits for these to remain stable for some period before continuing.

When it sees a new version (this also occurs at startup, and applies to both existing and new databases), it

  1. Watches the children of partitions/<version>, and continually mirrors that state locally. This is the map of where partitions live on the cluster.

  2. Waits until every partition represented in the cluster at least once; in other words, until the minimum replication factor is at least 1.

In the background, it decides which partitions it is responsible for and backfills them. To do that, it

  1. Decides which partitions it is responsible for offline, by:

    a. Putting all the nodes it knows about, including itself, on a partition ring

    b. For a given partition, placing that on the partition ring and picking the two (or whatever the replication factor is) nodes closest to that point, counter clockwise.

    c. If it itself is one of those nodes, then it is responsible for that partition.

  2. Starts loading and preparing those partitions. As they become available, it writes an ephemeral node to the partition map, at /partitions/<version>/<partition>@<hostname>.

Note that the ring is only used as a way to stably, fairly and deterministically pick partitions without actually needing to read current state of the cluster (which could be racy). Once the partition map is built, that is used as the actual state of the cluster going forward.

Once a node has the data locally, it can respond to peers that specifically ask that version, but it won't upgrade to clients until it sees that a version is complete across the cluster. Note that this switch to clients, which is the actual upgrade, can happen before or after the local partitions are finished loading; it's decoupled from that process. This is important if, for example, a node is starting up to join an existing cluster, and wants to be able to service requests immediately.

Even once it does upgrade (again, to clients), it keeps the old version around for a period of time. Specifically, it starts a 10 minute timer, and every time it receives a request for the old version, it resets the timer. Only at the end of the timer does it clear the old version.

That means that a cluster effectively has two versions during an upgrade. In this way, individual nodes can lag behind and still present a consistent picture to the clients they talk to.

Finally, when responding to a request, the node

  • Looks to see if it has the partition of the key locally. If so, it responds immediately
  • If not, determines a list of peers by consulting the cache of /partitions/<version>/
  • Picks a node at random and tries it (?proxy=true is added to the querystring to indicate that it shouldn't be proxied further).
  • Every duration of proxy_stage_timeout, it starts a request to a new node in parallel.
  • Returns the first request that succeeds, or bails after proxy_timeout.

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