| Query Types |
Ad-hoc queries composed of an arbitrary number of Map phases and Reduce phases |
SOLR style queries supporting combinations of free-text, wildcards, proximity, and boolean operators |
Equality and range query support |
| Index Locality |
N/A |
Indexes for terms are replicated to N vnodes (i.e. term-based partitioning) and stored in a merge index backend, regardless of the Riak KV backend used |
Indexes are located on the same vnodes as the object (i.e. document-based partitioning) and stored in the LevelDB backend along with the document |
| Vnodes Queried |
Depends on input 1 per term queried; 1/N for trailing wildcard |
1/N of all KV vnodes per a request |
|
| Supported Data Types |
Any datatype with Erlang MapReduce functions; valid UTF8 JSON with Javascript functions. Links per the specification |
Integer, Date, and Text |
Binary and Integer |
| Extraction |
Map phases can be used to extract data for later Map and Reduce phases |
Tokenization performed by one of the provided analyzers (Whitespace, Standard, Integer, and No-Op) or a Custom Analyzer |
Indexed values are submitted as metadata on the object, thus the application is responsible for tokenization |
| Anti-Entropy / Fault Tolerance |
N/A |
If a Search partition is lost or corrupted, the “repair” feature in Riak Console will rebuild partition data from adjacent indexes. |
Anti-entropy is carried over from KV; if a partition is lost, secondary indexes will be rebuilt along side the KV data by read repair |
| Limitations |
MapReduce operations are performed in memory and must be completed within the timeout period |
Querying low cardinality terms could tax a small subset of vnodes; composite queries are expensive; documents must be structured (JSON or XML) or plain text |
Only available with the LevelDB backend; no composite queries |
| Suggested Use Cases |
Performing calculations based on a known set of bucket-key pairs |
Searching objects with full-text data |
Retrieving all objects tagged with a particular term |
| Poor Use Cases |
Performing complex operations on large numbers of objects (e.g. analyzing every object in a bucket) |
Searching for common (low cardinality) terms in documents |
Searching prosaic text |
