Tag Archives: graph

Improving Performance with Flame Graphs

Yes, I’m slowly but surely getting on the generative AI bandwagon. The eye catching image above was generated in Lexica, it’s not perfect but our mind tricks us into accepting it. I am not a fan of asking these new AI systems questions and getting answers that only look like correct answers… but we’re not talking about that today. Instead we’ll be looking at improving the performance of RageDB using “perf” and “FlameGraphs“. Which really should have been called “FlameCharts” since it’s a chart not a graph but let’s not go there either.

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Counting Triangles

Our last post was about a Triangle Count query. It referenced another blog post from Kuzu where they explained their use of a Multi Way Join Algorithm to count 41 million triangles in 1.62 seconds. Using only binary joins it would take them 51.17 seconds to achieve the same result. My attempts to run the query using Lua on Rage landed at 9.5 seconds one node at a time and 5.6 seconds using the batch queries. So that got me thinking, how about Neo4j?

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Bullshit Graph Database Performance Benchmarks

Hey HackerNews, let me just drop my mixtape, checkout my soundcloud and “Death Row” is the label that pays me.

How is the Graph Database category supposed to grow when vendors keep spouting off complete bullshit? I wrote a bit about the ridiculous benchmark Memgraph published last month hoping they would do the right thing and make an attempt at a real analysis. Instead these clowns put it on a banner on top of their home page. So let’s tear into it.

At first I considered replicating it using their own repository, but it’s about 2000 lines of Python and I don’t know Python. Worse still, the work is under a “Business Source License” which states:

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Death Star Queries in Graph Databases

Star Wars: Episode IV - A New Hope Death Star

In Cypher, we call any unbounded star query a “Death Star” query. You’ll recognize it if you see a star between two brackets in any part of the query:

-[*]-

the deadly pattern of a death star query

The “star” in Cypher means “keep going”, and when it is not bound by a path length -[*..3]- or relationship type(s) -[:KNOWS|FRIENDS*]- it tends to blow up Alderaaning servers. It’s hard to find a valid reason for this query, but its less deadly cousins are very important in graph workloads.

For example when looking at fraud, we may start with a Customer node and ask, which known Fraudulent nodes are within 4 hops away? A Customer HAS an Account that was ACCESSED by a Device that ACCESSED another Account that BELONGS_TO a known Fraudster. A Customer HAS a mailing Address that is very SIMILAR to an Address that BELONGS_TO a Business that is partially OWNED by a known Fraudster. These are just two out of many valid patterns in our graph. Graph databases were designed to handle these kind of queries. The trick is that every node KNOWS its relationships, every node KNOWS how it is connected.

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KHop Baby One More Time

Some of the most beloved songs by main stream artists were written by Max Martin. The song “Baby One More Time” came out in 1999 and sold over 10m copies. It propelled Britney Spears into pop stardom. If we were to look at the graph above, with Max Martin in the center, then one hop away are the songs he wrote which would become #1s on the Billboards Hot 100. Two hops away are the Artists that performed those #1 songs. It is beyond question that Max Martin knows how to write good pop songs. I wish I had his talent. I only know how to write half way decent KHop implementations.

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Dark Mode

I tend to only find time to work on RageDB at night. Staring at code in CLion using the “Darcula” theme works great. But like a vampire exposed to direct sunlight, things go horribly wrong when I try to test what I am working on using the RageDB front-end. You see, besides the code-editor, the rest of the interface is very bright. Blindingly so:

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Observations of the LDBC SNB Benchmark

RageDB Mascot running

I’m still trying to figure out the right look for the language folks will use to talk to RageDB. Instead of waiting until I have it figured out, I decided I should write all the queries for the LDBC SNB Benchmark to prepare for a full run in the next few months. Now that we added “stored procedures” to RageDB, the benchmark code is trivial. I send a post request to the Lua url with the name of the query plus any parameters it may need which are in a CSV file. Here is Short Query 4 for example and they all look like this besides the different parameters:

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Watch your Language

I was watching The Marvelous Mrs. Maisel and one of the more jarring issues of the first two episodes is that Midge keeps getting arrested for the things she says. It reminded me of the song “Me So Horny” from 2 Live Crew that landed the hip hop group in jail charged with obscenity. Record store owners were getting arrested for selling CDs to undercover cops. How insane does that all sound? But it strikes the point that language matters. The things we say and how we say them are powerful. They convey meaning and emotion, language can be pleasant or it can be foul.

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Let’s build something Outrageous – Part 25: Dates in C++ and Faster Imports 

Back in February, we added the ability to load a CSV file and alter the contents while importing it. We also added Date support to RageDB using a Lua library. This was a masterful job of copy and paste and got us lots of functionality very quickly. When we timed the import for LDBC SNB SF10 it came in at 28 minutes. Which wasn’t bad, but wasn’t great. Let’s try to speed that up today.

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Let’s build something Outrageous – Part 24: Permissions and Multiple Graphs

Typically we want to Reduce, Reuse and Recycle to help the environment. But today we are going to Reduce, Reuse and Recycle the Lua Sandbox Environment to give us two additional sets of permissions. The first is “Read Write” in which a user can read and write to the database but cannot create new types of nodes or relationships or data types. The second is “Read Only” which does what it sounds like.

While we’re here, we’re going to one graph, two graph, rage graph, blue graph our way to multi database support. Let’s jump in:

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