Eugene 1.0.1-alpha.19

Project Status

THIS PROJECT IS NEW AND NOT YET READY TO BE USED.

About Eugene

Eugene is a data structure peristence library for .NET projects. It gives you access to traditional in-memory data structures such as arrays, linked lists, array lists, hash tables, binary trees, trie trees, etc., in a format that is continuously persisted to disk as you modify the data structure.

Of course there are many options for persisting data to disk. The world has SQL databases, NoSQL databases, text files such as CSV, XML, JSON, and YAML, and all manner of other ways of persisting data to disk. Why do we need yet another way of persisting data?

While each of the solutions above have their place, there are times where we need a hand-crafted data structure consisting of multiple interconnected collections to achieve high performance retrieval of the data.

One such use case, and the use case that was at the forefront of our minds when we started Eugene, was to build a high performance trigram text indexing library that can be used to quickly search for regular expressions across a very large corpus of code repositories. To do that, we need a way to build up a customized, specialized index that can store trigram text indexes for fast retrieval.

That's just one sample use case, however. Eugene is a general purpose library that can find broad applicability in many domains of work. Any time you need fine grained control over a specialized data structure that doesn't fit neatly into other persisting solutions, Eugene may be a good solution for your project.

Sister Projects

Eugene is one part of a series of projects designed to deliver high speed code searching capabilities.

• Eugene - This project, is a Nuget package that provides general purpose persistent data structures
• Spinach - Spinach is a Nuget packages that builds on top of Eugene and provides an API and indexing file format for performing regular expression text searching
• Popeye - Popeye is an installable code searching engine that can be installed in various ways, including Docker, Kubernetes, etc. Essentially, Popeye aims to be a C# version of Zoekt that uses similar approaches to indexing the text repositories as does Zoekt. Popeye is not a direct, line-for-line port of Zoekt, but is based on similar ideas.

Popeye uses the Spinach package, which in turn uses Eugene.

Motivation

To understand the motivation for Eugene, one should start by understanding what Zoekt is.

Zoekt is an open source code searching engine written in Go. Being written in Go, it wasn't overly useful for our projects here at OpenSquiggly, being that our codebase is written in C#. We wanted a C# version of Zoekt, and so we went about reading the Zoekt codebase to consider how we might access it from C# or perhaps port it to C# line-by-line.

Unfortunately, due to the many philosophical differences between Go and C#, directly porting a Go project to C# seemed quite difficult.

We thought about using Zoekt in a separate container and accessing it using its REST API, but that just seemed like a gigantic hassle. We really wanted a pure C# solution that could be tightly and properly integrated with OpenSquiggly.

Next, we thought about whether we should port our entire product to Go, but we decided not to do that.

Finally, we wondered if we could write a new C# project from scratch that would follow the same ideas as Zoekt.

Let's take the time to understand at a very high level what Zoekt does. Suppose we are running a regular expression search for:

quickly.*browning.*foxhound

Admittedly this is a bit of a contrived example, but it's useful to illustrate the point.

First, Zoekt extracts the three known literals from the string, getting:

• quickly
• browning
• foxhound

and then goes about finding all the files in the corpus that contain these three literals. Once it finds a file with all three of these string literals, it runs a regex search over the file to see if it matches the full regex.

Okay, but how does it quickly find those matching files? Aha! Now we're getting to the heart of the matter with this problem.

In a nutshell, Zoekt builds up an index of trigrams that it can quickly read off of disk to search for string literals using their trigrams. Zoekt doesn't document their file format in very much detail - their design document describes it in broad brush strokes, but the details are sketchy.

What we needed was a way to do the same thing in C#, except we thought it would be nice if in the process we abstracted out the reusable, general purpose data structures that we needed into a separate library. Doing so would make the Spinach and Popeye code much cleaner and easier to improve it over time. Thus, Eugene was born.

More About How Zoekt Works

Zoekt looks for literals using trigrams. Trigrams are sequences of three letters.

• The trigrams for "quickly" are: qui, uic, ick, ckl, kly
• The trigrams for "browning" are: bro, row, own, wni, nin, ing
• The trigrams for "foxhound" are: fox, oxh, xho, hou, oun, und

Suppose we're looking for all documents that contain the string literal "quickly".

First we get a list of all the documents that contain the leading trigram, "qui". Then we get another list of all the documents containing the trailing trigram, "kly" and see if the trigram "kly" exists at position qui + 4. Then we perform the interesection of these two lists. Finally, we check the document to see if it truly contains the full literal "quickly" at position qui.

In actuality we don't have to search for specifically leading and trailing trigrams, we can search for any two trigrams of our choosing within the literal. As long as we have two lists of trigrams, we can intersect the list and hopefully wind up with a relatively small list of matching documents. This observation allows for some optimizations - we can search for the least frequently occurring trigrams to minimize the number of documents we need to search.

If we carefully arrange the documents so that they always come back ordered by sequential file ids, then intersecting the two lists can be very fast because we can skip over large numbers of unmatched documents as we perform the intersection.

This is why we need a custom file format that we have fine grained control over. We want to iterate over the indexes in very specific ways, with some strategically applied optimizations, to return results back to the user very quickly.

So that's what Zoekt does. It builds up these indexes and then does all the other necessary work to use them to look up literals and run regex searches on candidate documents.

The problem comes when we start thinking about how big these indexes might be. Suppose we are trying to index 1,000,000 repositories on GitHub. Disk space is cheap these days (well, not as cheap in the cloud, but still pretty cheap), the real problem is how long it takes to look up a trigram in a huge index, and how many disk accesses will be needed. Zoekt is aiming to provide lightning fast code searching that can return thousands of results in millisecond timeframes, so disk access times become significant on those scales.

In order to reduce disk accesses, Zoekt performs some vaguely documented magic. The documentation is a little fuzzy on whether it does or does not cache the indexes in memory, and whether it does or does not use a lot of memory. According to some documents released by Zoekt's current maintainer, memory usage in Zoekt is potentially problematic. If the Zoekt instance is not given enough memory, it may produce out-of-memory errors.

Zoekt also imposes some annoying limitations to limit its memory usage. It breaks up the index into shards, with one repo per shard, and the sets the maximum size of a shard to 1GB.

In the world of cloud providers, as of this writing in the year 2023, memory is still quite expensive and adds up quickly. If you need a VM with 4GB of memory, Azure or AWS will give that to you for around $70/month or so. But if you want a 32GB VM, that might run you $350/month.

So to run Zoekt, you have to make some calculations and some tradeoffs. Do you want to overpay by spinning up a VM with more memory than you'll probably need, or do you want to save money and run the risk of running out of memory. Running out of memory means that someone is probably waking up a 3am to go fix the problem, and no one wants to do that. We all want to sleep easy at night.

That's the problem we're trying to address with Eugene, Spinach, and Popeye.

We wanted a way to build these trigram indexes with clean, flexible, easy to modify C# code, and also have a way to put an LRU cache in front of the data structures so that the amount of memory used can be controlled more carefully. The goal is to make the indexing run very fast even on small memory VM instances, and to never produce out of memory errors.

What about ElasticSearch or Lucene.NET?

An astute reader might wonder if what we just described doesn't sound a whole lot like what ElasticSearch and Lucene.NET already do. Couldn't we use one of those to accomplish what we're describing?

Yes, in theory, ElasticSearch and Lucene.NET do similar things, and should, at least in theory, work for what we need. In practice though, it just doesn't work well. Those solutions are too heavy-weight and are not optimized enough to do the kind of high volume, large repository regex code searching that we desire.

In fact, our initial implementation for OpenSquiggly's code search was based on using ElasticSearch to build trigram indexes. It did work, but the performance was lackluster, required a lot of memory, and it was clear that the solution just wasn't going to scale as we needed it to. When it comes to code searching, customers are looking to index thousands, or perhaps even hundreds of thousands of code repositories. A more general purpose solution like ElasticSearch just can't cope with that kind of scale.

Where Did the Names Come From?

Zoekt is a Dutch word that means "seek".

The creator of Zoekt used the following tag line in his documentation:

"Zoekt, en gij zult spinazie eten" - Jan Eertink

("seek, and ye shall eat spinach" - My primary school teacher)

Here in America, everyone knows that Popeye the Sailor Man gets strong by eating spinach, and so our names are based on this theme. Eugene is named after the character Eugene the Jeep in the comic series.

We also like that the name Popeye bids a friendly salute to the now discontinued Atlassian code search tool named FishEye. We always wondered why Atlassian didn't keep investing in FishEye; perhaps it was just too far ahead of its time. In today's environment, with a resurgence of interest in internal developer portals and platforms, and the strong need to find ways to cope with the ever increasing complexity of software projects, we think good code searching engines might be poised for a comeback.