On e-Manifest, C2, and Open Data Maker, we pair a full-text search tool with a storage tool, as is a common technique for information management systems. For example, we keep the canonical data in a database and also push it to a search tool; all queries are directed first at the much-faster search tool.

The technique is so common that modern databases often include an integrated full-text search component, which can be very useful for simple data models where you want to, for example, search blog posts or a few large text columns in a table or two. Sometimes, though, your data model is complex enough or the user experience demands features that the built-in search component does not offer. Then it’s time to consider a standalone search tool, like Solr or Elasticsearch.

A database gives us safety: redundancy, validations, referential integrity checks, and transactions. With safety comes tradeoffs: it can be difficult and expensive to match a single word from multiple columns of many words, and all those data integrity features take time and CPU cycles.

A full-text search tool gives us speed; an inverted index makes finding a needle amidst a haystack of data a very fast operation. As with the storage tool, there are tradeoffs: the data pushed into the search tool must be de-normalized, and then kept in sync with the canonical storage tool.

The experience for the user can be lovely: near-instantaneous search results, even for really large datasets. The experience for the developer can be … less than lovely. Keeping multiple tools in sync, especially for large datasets, can require a lot of both time and effort. For anything beyond the simplest of models, the stored data is likely normalized across multiple tables and columns, so creating the searched data can make re-assembling the data into its de-normalized state a costly exercise. In a typical use case, the time spent follows the Pareto principle: 80 percent of the time is spent marshalling the data from storage and sending it to the search tool, while the search indexing process itself usually takes about 20 percent of the total time.

Example

Take this example. We have a database with these tables:

reports

attachments

users

groups

user_groups

Each report may have zero or more associated attachments (one-to-many) and zero or more associated users (one-to-many). Each user may have zero or more associated groups (many-to-many).

Say we want to find all the reports that belong to user Jane and that have attachments that contain the word “apple”. Searching the inverted index can be a query as simple as:

user:janedoe@example.com attachments:apple

But to make that simple query possible, we must de-normalize a report and its associated objects into a single “document” object, which might look like this:

{ "id" : 123 , "title" : "my report" , "description" : "all the apples fit to eat" , "user" : { "id" : 456 , "first_name" : "Jane" , "last_name" : "Doe" , "email" : "janedoe@example.com" , "groups" : [ "SomeGroup" , "AnotherGroup" ] }, "attachments" : [ { "id" : "abc123" , "filename" : "list-of-apple-orchards.csv" , "content" : "honeycrisp yummy, macintosh tasty, apple cores by the bushel" } ] }

Building that de-normalized object from the database requires multiple SQL queries:

SELECT * FROM reports WHERE id=123 SELECT * FROM attachments WHERE report_id=123 SELECT * FROM users WHERE id=456 SELECT name FROM groups WHERE groups.id IN (SELECT group_id FROM user_groups WHERE user_id=456)

That’s a lot of work to make the user query so simple.

Problem: Wall-clock time

Performed once, those SQL queries probably take, cumulatively, less than a second. But if you have a million reports to index in your search tool, that adds up to many millions of SQL queries and a lot of wall-clock time. And if your search index configuration needs to change, you probably need to re-index everything again. During development, especially, you may re-index large data sets many, many times.

Solution: Divide and conquer

Fortunately, as an impatient developer you can take advantage of the fact that, even though the marshaling phase of the synchronization process takes up the majority of the wall-clock time, the problem lends itself to the MapReduce pattern; you can split the problem into smaller pieces and run them in parallel. Modern computers, even the laptop on which I’m composing this blog post, have multiple processor cores, each of which can run multiple threads. A lot of the time, one or more cores are sitting idle. Using all the resources in the machine can decrease the total wait time significantly.

Parallel play

Here’s a real example. I work on multiple Ruby on Rails projects that depend upon PostgreSQL and Elasticsearch. My laptop has four cores, so during the data synchronization process, one core will be dedicated to Elasticsearch, and one core to PostgreSQL, leaving two cores free to run Ruby code that will read from PostgreSQL (marshaling) and write to Elasticsearch (indexing). I wanted to simulate a production dataset, so I created 100,000 dummy reports. Using the Elasticsearch Rails gem it was very easy to index my reports:

bundle exec rake environment elasticsearch:import:model CLASS='Report'

That task took about an hour to complete (or about 27 reports per second). If we believe the 80/20 rule of thumb, that meant that about 48 minutes were spent marshaling data from PostgreSQL, and Elasticsearch took 12 minutes to parse and index the reports.

But wait! Only one of the two available cores was busy during that time. That’s because the Rake task runs as a single process. It grabs a batch of Reports (1,000 at a time by default), marshals them all into JSON “documents” and sends them to Elasticsearch using the bulk API.

That lonely, idle processor core, twiddling its thumbs while its neighbor is working so hard. Well, that core can now share in the fun too:

bundle exec rake environment elasticsearch:ha:import \ NPROCS=2 CLASS='Report' BATCH=200

We wrote some extensions to the basic Elasticsearch Rails gem that can take advantage of multiple cores. Now my 100,000 Reports can finish in 36 minutes (48/2 + 12) instead of 60. That increased speed is not without a cost, though. Since we fork new processes instead of using threads, it takes twice as much memory to run twice as fast. That’s why I explicitly set the BATCH size to be smaller than the default 1,000, so that the memory used by each process is smaller.

We call these extensions “high availability” because this approach means that re-indexing a production system can happen much faster, reducing downtime for our users. We’re limited less by wall-clock time and more by available cores and memory.

Zero downtime

We also included a “staging” feature, so that when we re-index an entire system, we create the new index alongside the existing index, and then “promote” the new index when it’s finished.

bundle exec rake environment elasticsearch:ha:stage \ elasticsearch:ha:promote NPROCS=2 CLASS='Report' BATCH=200

That means zero downtime for our users, and gives us the ability to rollback the changes if we need to. In a production environment, where there are already separate, dedicated servers for the database and Elasticsearch, a single 8-core machine can churn through a re-indexing task very quickly. That means, as a developer, I can spend more time developing and less time waiting for the machine to do its work. Lovely.

Adding this to your project

If you’re already using Ruby on Rails and Elasticsearch, check out our replacement Rake tasks for the Elasticsearch Rails gem.