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Cassandra Indexing Guidelines from CassandraSF2011

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Ed Anuff of usergrid presented on indexing techniques at Cassandra SF 2011. While Cassandra 0.7 and later have built-in secondary indexes, Anuff said they don't work well for high cardinality values, require at least one equality comparison and return unsorted results. Anuff presented patterns for alternative indexing including wide rows and tables that use Cassandra 0.81's new composite comparator operators to overcome these limitations, as well as cautioning against the use of super-columns.

Anuff said that in Cassandra it's an anti-pattern to iterate over keys to find records, instead you should be using alternative indexes. He listed the following examples of alternative indexes:
  • Native secondary indexes
  • Wide rows used for lookup and grouping
  • Custom secondary indexes 


Anuff said that in early versions of Cassandra, super-columns were typically used for alternative indexing, but says to "use with caution" noting that many projects have moved away form super-columns because of performance issues and issues like not sorting the subcolumn and not being able to doubly nest super-columns. He observed that column families in Cassandra have sort orders and comparators because they have been used as a way to implement secondary indexing.

Native Secondary Indexes

Anuff explained that native secondary indexes are implemented with each index as a separate hidden Column Family. Nodes index the rows that they store, and when you issue a query it gets sent to all the nodes, distributing the work. He said that Cassandra 0.8.1 uses indexes for equality operations, and that range operations are performed in memory by the coordinator node. These characteristics limit their application, also limiting their use to data types that Cassandra understands natively.

Wide Rows 

Anuff said that newcomers to Cassandra often wonder why a row would need up to 2 billion columns. He argued that columns are the basis of indexing, organizing, and relating items in Cassandra, and that "if your data model has no rows with over a hundred columns, you're either doing something wrong or you shouldn't be using Cassandra." Wide rows can be used to model fairly large collections, such as recording a table of departments in a company like so:

departments = {
    "Engineering" : {"137acd" : null, "e245116" : null, ... },
    "Sales" : { "334762" : null, "17a632" : null, ... },

Anuff pointed out these advantages to using wide rows:

  • column operations are fast
  • column slices can be retrieved by range
  • results are always sorted
  • if your target key is a time-based UUID you get both grouping and sorting by timestamp 

Composite Columns

However, the wide row approach only works for keeping primary keys, rather than providing a lookup mechanism. In general, Anuff said that wide rows are limited to use for 1:1 mappings (i.e., where each value appears only once in a row). For example, consider having a column family for groups that's indexed entries by last name. Anuff recommended using composite keys, which have built in support in Cassandra 0.8.1 through two new comparators. CompositeType is a base comparator, with one column family per index in which the user specifies the specific types and order for each type. The DynamicCompositeType dynamic comparator supports other cases, where users want to use just one column family with many different indexes, with every row potentially holding a different index with different orderings of different values. Anuff noted that the DynamicCompositeType is used for generated indexes in the JPA implementation in the Hector project, which is one of the Java clients for Cassandra, and one that Anuff contributes to.

Composite keys can look like this:

User_Keys_By_Last_Name = {
  "Engineering" : {"anderson", 1 : "ac1263", "anderson", 2 : "724f02", ... }, 
  "Sales" : { "adams", 1 : "b32704", "alden", 1 : "1553bd", ... }, 

Anuff noted that it's easy to query these composite indexes, but that updating them is tricky because you need to remove old values and insert the the new values.  In general, he said that reading before writing can be an issue with Cassandra. Rather than doing locking (e.g., with ZooKeeper), Anuff presented a technique that uses three Column Families. For example, in a table with a users Column Family and an indexes Column Family, there will be a third Column Family Users_Index_Entries. Updates first read the previous index values from this column family to avoid concurrency issues and both it and Users use timestamped columns to avoid the need for locking. Sample code for how to implement this technique can be found in Anuff's github project CassandraIndexedCollections as well as in the slides for this presentation.

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