# Scalability

People often want to know how Xapian will scale. The short answer is "very well" - an early version of the software powered the (now defunct) Webtop search engine, which offered a search over around 500 million web pages (around 1.5 terabytes of database files). Searches took less than a second.

In terms of current deployments, gmane indexes and searches nearly 100 million mail messages on a single server at the time of writing (2012), and we've had user reports of systems with more than 250 million documents.

# Benchmarking

One effect to be aware of when designing benchmarks is that queries will be a lot slower when nothing is cached. So the first few queries on a database which hasn't been searched recently will be unrepresentatively slow compared to the typical case.

In real use, pretty much all the non-leaf blocks from the B-trees being used for the search will be cached pretty quickly, as well as many commonly used leaf blocks.

# General Scalability Considerations

In a large search application, I/O will end up being the limiting factor. So you want a RAID setup optimised for fast reading, lots of RAM in the box so the OS can cache lots of disk blocks (the access patterns typically mean that you only need to cache a few percent of the database to eliminate most disk cache misses).

It also means that reducing the database size is usually a win. The Chert backend compresses the information in the tables in ways which work well given the nature of the data but aren't too expensive to unpack (e.g. lists of sorted docids are stored as differences with smaller values encoded in fewer bytes). There is further potential for improving the encodings used.

Another way to reduce disk I/O is to run databases through xapian-compact. The Btree manager usually leaves some spare space in each block so that updates are more efficient (though there are heuristics which will fill blocks fuller when they detect a long sequence of sequential insertions, which means adding documents to the end of an empty database will produce fairly compact tables, apart from the postlist table). Compacting makes all blocks as full as possible, and so reduces the size of the database. It also produces a database with revision 1 which is inherently faster to search. The penalty is that updates will be slow for a while, as they'll result in a lot of block splitting when all blocks are full.

Splitting the data over several databases is generally a good strategy. Once each has finished being updated, compact it to make it small and faster to search.

A multiple-database scheme works particularly well if you want a rolling web index where the contents of the oldest database can be rechecked and live links put back into a new database which, once built, replaces the oldest database. It's also good for a news-type application where older documents should expire from the index.

# Size Limits in Xapian

The chert backend (which is currently the default and recommended backend) stores the indexes in several files containing Btree tables. If you're indexing with positional information (for phrase searching) the term positions table is usually the largest.

The current limits are:

- Xapian uses unsigned 32-bit ints for document ids, so you're limited to just over 4 billion documents in a database. The other limits will cut in first for a single database, but searches over multiple databases are done by interleaving the document ids, so this might start to matter (especially if one database is much larger than the others). This interleaving technique could be changed fairly easily if it proves problematic.

- If you search many databases concurrently, you may hit the per-process file-descriptor limit - each chert database uses between 3 and 7 fds depending which tables are present. Some Unix-like OSes allow this limit to be raised. Another way to avoid it (and to spread the search load) is to use the remote backend to search databases on a cluster of machines. Chert could be made to not open fds for tables which aren't being used during search (values and positions may not be), or to juggle fds - the record table is typically only used for results, while the posting table is typically only used during matching.

- If the OS has a filesize limit, that obviously applies to Xapian (a 2GB limit used to be common for older operating systems). The xapian-core configure script will attempt to detect and automatically enable support for "LARGE FILES" where possible.

  So what is the limit for a modern OS? Taking Linux 2.6 as an example, ext4 allows files up to 16TB and filesystems up to 1EB, while btrfs allows files and filesystems up to 16EB (figures from Wikipedia).

- The B-trees use a 32-bit unsigned block count. The default blocksize is 8K which limits you to 32TB tables. You can increase the blocksize if this is a problem, but it's best to do it before you create the database as otherwise you need to use xapian-compact to make a compacted copy of the database with the new blocksize, and that will take a while for such a large database. The maximum blocksize currently allowed is 64K, which limits you to 256TB tables.

- Flint stores the total length (i.e. number of terms) of all the documents in a database so it can calculate the average document length. This is currently stored as an unsigned 64-bit quantity so it's not likely to be a limit you'll hit. It's listed here for completeness.

If you've further questions about scalability, ask on the mailing lists - people using Xapian to search large databases may be able to make further suggestions.