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Rethinking full text search

02 July 2012

In January 2009 I was wondering whether it is possible to build a full-text search engine that could handle the search for Twitter. At that time the search tool they provided was barely working. Later Twitter acquired Summize to solve this problem.

A search engine for Twitter presents a unique set of constraints. Traditional full-text search engines assume that index once created won't be changed, updating is expensive by design. Twitter use case requires something opposite - adding items to the index must happen instantly and cheaply.

I wondered if it's possible to have:

At that time available open-source search engines weren't really horizontally scalable and updating indexes was expensive. Scaling was usually "solved" by serving the same index from multiple machines. As updating index was quite complex, it was usually easier to create a separate index for recent updates. But that resulted in a large number of small indexes - which is very inefficient. To avoid that an occasional merge was required.

Scaling required distributing very large indexes to the multiple machines, while updating indexes means rewriting them. These two behaviours don't play together nicely.

Being disappointed with available solutions and feeling the wave of NoSQL I went for a quest:

Is it possible to build a full-text search engine on a distributed key-value store?

Suffering the not-invented-here syndrome, I jumped straight into coding.

Let the experiment begin

Writing the basic lexer and indexer wasn't all that hard.

I run a few experiments, mostly trying to index the huge Wikipedia dump, and I was surprised to see the indexer, written in not-that-fast Python, was not the biggest bottleneck.

Although initially it was indeed the slowest part of the system, when the indexes grew to a certain size the storage layer started to ruin the performance of the whole system.

I repeated the tests with both Tokyo Cabinet and Berkeley DB. Neither behaved nicely when the dataset outgrew available memory.

It's not that surprising. Both databases are tuned for common usage patterns. Storing data for a full-text search engine is definitely not one of them.

What I needed is a key-value storage layer optimised for:

huge number of small objects
Full-text search index is composed of a huge number of mostly small items, at least one for every indexed word. Value size follows a logarithmic distribution, with most items having only a few bytes, followed by a long tail of much larger items.
large data sets
Stored data will be significantly larger than RAM size.
retrieval speed
Accessing every key should require at most one disk seek.
write speed
Writes should be bulked and done only to a single file at a time. That ensures an average write cost is close to zero disk seeks.

Again, at the time there wasn't a decent database layer having these features. Recently alternatives started arriving - for example things like LevelDB and BitCask promise similar set of properties.

Naive database layer

I decided to write my own database, specially tuned for my specific use case.

My first attempt was a rather simple database with an append-only log architecture. In order to locate a particular key on a disk I stored key metadata in-memory. Concretely the relationship stored in memory:

key -> (log_file, data_offset, data_size)

I decided to use a simplest data structure for this - a binary tree.

The implementation went smoothly and soon I was able to squeeze more performance than from previously tested databases. But then I hit another problem.

It's all about memory, Luke

The custom database performed well until it stopped with an out-of-memory error. Ruling out implementation issues like memory leaks, I realised that the memory cost of a single key was non-negligible - specially when you try to store millions of keys.

For every stored key, my database needed to have an in-memory object containing:

size of key          : 2 bytes
key                  : 9 bytes on average (may vary)

log file number      : 4 bytes
value offset in file : 8 bytes
value size           : 4 bytes

binary tree overhead : 24 bytes (3 pointers)

An average key length in my experiments was around 9 bytes, in total that gives about 51 bytes per key. In theory in 4GiB RAM one could store 84 million keys.

I clearly wanted to store more keys.

Beating trees

We can surely do better - by far the biggest contribution to the memory usage is the binary tree overhead. Almost all binary tree implementations use three pointers per node (to left, right and parent nodes). Counting 8 bytes per pointer on a 64 bit architecture gives a constant overhead of 24 bytes per key.

It's pretty disappointing when you realise that half of the nodes on the tree have NULL in two out of three pointers - the leafs and have no childs - they store an empty value in left and right!

What can be done to reduce memory footprint of a key in such database?

A lot. Later I was able to reduce the memory usage to about 33 bytes per key.

But let's not jump ahead of the story. First, we need to find a more memory efficient data structure than a stupid binary tree.

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