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Partitioning by Hash of Key

Because of this risk of skew and hot spots, many distributed datastores use a hash function to determine the partition for a given key.

A good hash function takes skewed data and makes it uniformly distributed. Say you have a 32-bit hash function that takes a string. Whenever you give it a new string, it returns a seemingly random number between 0 and 232 - 1. Even if the input strings are very similar, their hashes are evenly distributed across that range of numbers.

For partitioning purposes, the hash function need not be cryptographically strong: for example, Cassandra and MongoDB use MD5, and Voldemort uses the Fowler- Noll-Vo function. Many programming languages have simple hash functions built in (as they are used for hash tables), but they may not be suitable for partitioning: for example, in Java’s Object.hashCode() and Ruby’s Object#hash, the same key may have a different hash value in different processes [6].

Once you have a suitable hash function for keys, you can assign each partition a range of hashes (rather than a range of keys), and every key whose hash falls within a partition’s range will be stored in that partition. This is illustrated in Figure 6-3.

Partitioning by hash of key

Figure 6-3. Partitioning by hash of key.

This technique is good at distributing keys fairly among the partitions. The partition boundaries can be evenly spaced, or they can be chosen pseudorandomly (in which case the technique is sometimes known as consistent hashing).

Consistent Hashing

Consistent hashing, as defined by Karger et al. [7], is a way of evenly distributing load across an internet-wide system of caches such as a content delivery network (CDN). It uses randomly chosen partition boundaries to avoid the need for central control or distributed consensus. Note that consistent here has nothing to do with replica consistency (see Chapter 5) or ACID consistency (see Chapter 7), but rather describes a particular approach to rebalancing.

As we shall see in “Rebalancing Partitions” on page 209, this particular approach actually doesn’t work very well for databases [8], so it is rarely used in practice (the documentation of some databases still refers to consistent hashing, but it is often inaccurate). Because this is so confusing, it’s best to avoid the term consistent hashing and just call it hash partitioning instead.

Unfortunately however, by using the hash of the key for partitioning we lose a nice property of key-range partitioning: the ability to do efficient range queries. Keys that were once adjacent are now scattered across all the partitions, so their sort order is lost. In MongoDB, if you have enabled hash-based sharding mode, any range query has to be sent to all partitions [4]. Range queries on the primary key are not supported by Riak [9], Couchbase [10], or Voldemort.

Cassandra achieves a compromise between the two partitioning strategies [11, 12, 13]. A table in Cassandra can be declared with a compound primary key consisting of several columns. Only the first part of that key is hashed to determine the partition, but the other columns are used as a concatenated index for sorting the data in Cassandra’s SSTables. A query therefore cannot search for a range of values within the first column of a compound key, but if it specifies a fixed value for the first column, it can perform an efficient range scan over the other columns of the key.

The concatenated index approach enables an elegant data model for one-to-many relationships. For example, on a social media site, one user may post many updates. If the primary key for updates is chosen to be (user_id, update_timestamp), then you can efficiently retrieve all updates made by a particular user within some time interval, sorted by timestamp. Different users may be stored on different partitions, but within each user, the updates are stored ordered by timestamp on a single partition.

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