Question

Can I optimize a Core Data query when searching for matching words in a text? (This question also pertains to the wisdom of custom SQL versus Core Data on an iPhone.)

I’m working on a new (iPhone) app that is a handheld reference tool for a scientific database. The main interface is a standard searchable table view and I want as-you-type response as the user types new words. Words matches must be prefixes of words in the text. The text is composed of 100,000s of words.

In my prototype I coded SQL directly. I created a separate “words” table containing every word in the text fields of the main entity. I indexed words and performed searches along the lines of

SELECT id, * FROM textTable 
  JOIN (SELECT DISTINCT textTableId FROM words 
         WHERE word BETWEEN 'foo' AND 'fooz' ) 
    ON id=textTableId
 LIMIT 50

This runs very fast. Using an IN would probably work just as well, i.e.

SELECT * FROM textTable
 WHERE id IN (SELECT textTableId FROM words 
               WHERE word BETWEEN 'foo' AND 'fooz' ) 
 LIMIT 50

The LIMIT is crucial and allows me to display results quickly. I notify the user that there are too many to display if the limit is reached. This is kludgy.

I’ve spent the last several days pondering the advantages of moving to Core Data, but I worry about the lack of control in the schema, indexing, and querying for an important query.

Theoretically an NSPredicate of textField MATCHES '.*\bfoo.*' would just work, but I’m sure it will be slow. This sort of text search seems so common that I wonder what is the usual attack? Would you create a words entity as I did above and use a predicate of “word BEGINSWITH ‘foo'”? Will that work as fast as my prototype? Will Core Data automatically create the right indexes? I can’t find any explicit means of advising the persistent store about indexes.

I see some nice advantages of Core Data in my iPhone app. The faulting and other memory considerations allow for efficient database retrievals for tableview queries without setting arbitrary limits. The object graph management allows me to easily traverse entities without writing lots of SQL. Migration features will be nice in the future. On the other hand, in a limited resource environment (iPhone) I worry that an automatically generated database will be bloated with metadata, unnecessary inverse relationships, inefficient attribute datatypes, etc.

Should I dive in or proceed with caution?

Answer 1

I made a workaround solution. I think it’s similar to this post. I added the amalgamation source code to my Core Data project, then created a full-text search class that was not a managed object subclass. In the FTS class I #import "sqlite3.h" (the source file) instead of the sqlite framework. The FTS class saves to a different .sqlite file than the Core Data persistent store.

When I import my data, the Core Data object stores the rowid of the related FTS object as an integer attribute. I have a static dataset, so I don’t worry about referential integrity, but the code to maintain integrity should be trivial.

To perform FTS, I MATCH query the FTS class, returning a set of rowids. In my managed object class, I query for the corresponding objects with [NSPredicate predicateWithFormat:@"rowid IN %@", rowids]. I avoid traversing any many-to-many relationships this way.

The performance improvement is dramatic. My dataset is 142287 rows, comprising 194MB (Core Data) and 92MB (FTS with stopwords removed). Depending on the search term frequency, my searches went from several seconds to 0.1 seconds for infrequent terms (<100 hits) and 0.2 seconds for frequent terms (>2000 hits).

I’m sure there are myriad problems with my approach (code bloat, possible namespace collisions, loss of some Core Data features), but it seems to be working.