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Building search indexes

Google’s original use of MapReduce was to build indexes for its search engine, which was implemented as a workflow of 5 to 10 MapReduce jobs [1]. Although Google later moved away from using MapReduce for this purpose [43], it helps to understand MapReduce if you look at it through the lens of building a search index. (Even today, Hadoop MapReduce remains a good way of building indexes for Lucene/Solr [44].)

We saw briefly in “Full-text search and fuzzy indexes” on page 88 how a full-text search index such as Lucene works: it is a file (the term dictionary) in which you can efficiently look up a particular keyword and find the list of all the document IDs containing that keyword (the postings list). This is a very simplified view of a search index—in reality it requires various additional data, in order to rank search results by relevance, correct misspellings, resolve synonyms, and so on—but the principle holds.

If you need to perform a full-text search over a fixed set of documents, then a batch process is a very effective way of building the indexes: the mappers partition the set of documents as needed, each reducer builds the index for its partition, and the index files are written to the distributed filesystem. Building such document-partitioned indexes (see “Partitioning and Secondary Indexes” on page 206) parallelizes very well.

Since querying a search index by keyword is a read-only operation, these index files are immutable once they have been created.

If the indexed set of documents changes, one option is to periodically rerun the entire indexing workflow for the entire set of documents, and replace the previous index files wholesale with the new index files when it is done. This approach can be computationally expensive if only a small number of documents have changed, but it has the advantage that the indexing process is very easy to reason about: documents in, indexes out.

Alternatively, it is possible to build indexes incrementally. As discussed in Chapter 3, if you want to add, remove, or update documents in an index, Lucene writes out new segment files and asynchronously merges and compacts segment files in the background. We will see more on such incremental processing in Chapter 11.

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