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Materialized views and caching

A full-text search index is a good example: the write path updates the index, and the read path searches the index for keywords. Both reads and writes need to do some work. Writes need to update the index entries for all terms that appear in the document. Reads need to search for each of the words in the query, and apply Boolean logic to find documents that contain all of the words in the query (an AND operator), or any synonym of each of the words (an OR operator).

If you didn’t have an index, a search query would have to scan over all documents (like grep), which would get very expensive if you had a large number of documents. No index means less work on the write path (no index to update), but a lot more work on the read path.

On the other hand, you could imagine precomputing the search results for all possible queries. In that case, you would have less work to do on the read path: no Boolean logic, just find the results for your query and return them. However, the write path would be a lot more expensive: the set of possible search queries that could be asked is infinite, and thus precomputing all possible search results would require infinite time and storage space. That wouldn’t work so well.™

Another option would be to precompute the search results for only a fixed set of the most common queries, so that they can be served quickly without having to go to the index. The uncommon queries can still be served from the index. This would generally be called a cache of common queries, although we could also call it a materialized

iii. Less facetiously, the set of distinct search queries with nonempty search results is finite, assuming a finite corpus. However, it would be exponential in the number of terms in the corpus, which is still pretty bad news.

view, as it would need to be updated when new documents appear that should be included in the results of one of the common queries.

From this example we can see that an index is not the only possible boundary between the write path and the read path. Caching of common search results is possible, and grep-like scanning without the index is also possible on a small number of documents. Viewed like this, the role of caches, indexes, and materialized views is simple: they shift the boundary between the read path and the write path. They allow us to do more work on the write path, by precomputing results, in order to save effort on the read path.

Shifting the boundary between work done on the write path and the read path was in fact the topic of the Twitter example at the beginning of this book, in “Describing Load” on page 11. In that example, we also saw how the boundary between write path and read path might be drawn differently for celebrities compared to ordinary users. After 500 pages we have come full circle!

 
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