graphql-jit VS Neo4j

In the end like all performance questions you'll have to measure it. Persisted queries, and even more extreme approaches, like a GraphQL JIT (https://github.com/zalando-incubator/graphql-jit) can remove a lot of the overhead.

We faced something similar. To improve GraphQL performance, we use graphql-jit. We turned off all other tracing that datadog turns on by default. Then, we then wrote a custom tracer to connect graphql-jit to dd-trace. Hopefully this same pattern works for you!

GraphQL JIT – GraphQL execution using a JIT compiler\ (0 comments)

https://github.com/zalando-incubator/graphql-jit#compile-a-q...

so I guess the difference is instead of having totally generic functions that parse the query every time, instead you can call a factory function up front to get a specialised function for a particular query

and V8 JIT can make that faster

JS has always been special & excellent because it allows multiphase programming. That one coild just add more code has been unique versus the world of statuc languages, where maybe youcd have some tool to generate stubs for Thrift or gRPC, then compule that in to your program. Which works ok, if and only if you know ahead of time what schemas you might need. Im these cases, you have to give up on stub code & start using more interprtted systems.

The advantages in js have been colossal, often an easy order of magnitude win to compile selectors & queries & other fast inner loop bits into jit compiled code versus having userland interpretters. Some of thd rete engines enjoy this. Just today there's graphql-jit. https://github.com/zalando-incubator/graphql-jit/blob/main/s...

Everything you say is full of doubt & scorn & skepticism. I just cant imagine living im such a world where each system had to know fully well ahead of time each type of data it might want to interact with, might have to have that precompiled & baked in. Thatcs a shitty miserable world that looks like the hell JS plucked us out of, by being a flexible, dynamic language that could load in routines & change it's behavior over time. Screw that dark hell world.

By using useParserCache we make sure to parse every unique operation only once. By using useValidationCache we make sure to validate every unique operation only once. By using useGraphQLJit we replace the default execute function with a just-in-time implementation.

One thing to understand is that, if you are using Node.js graphql-js would typically be the underlying implementation of all libraries and ultimately everything would get converted to JS/TS objects typically an AST ultimately making all these as abstractions on top of the existing way to define schemas. Note that the implementation can differ a bit in other languages or even within Node.js if you are using other ways of implementation like graphql-jit

Could you provide more context? the GraphQLJit plugin is a lightweight wrapper around graphql-jit. Could you share your previous datadog/graphql setup? Would love to dig in and find a proper solution!

Customizable runtime. Use custom GraphQL implementation such as graphql-jit or rolling our own for performance and cutting-edge features.

Neo4j: An ACID-compliant graph database with a high-performance distributed architecture. Ideal for complex relationship and pattern analysis in domains like social networks.

After spending millions fighting the committer of ONgDB who removed the commons clause from the AGPL branded license, it looks like the Free Software Foundation got involved and forced them to remove the commons clause or change the license to their own proprietary license.

https://github.com/neo4j/neo4j/commit/b6237ca4e31706b1efbd0f...

The GenAI Stack came about through a collaboration between Docker, Neo4j, LangChain, and Ollama. The goal of the collaboration was to create a pre-built GenAI stack of best-in-class technologies that are well integrated, come with sample applications, and make it easy for developers to get up and running. The goal of the collaboration was to create a pre-built GenAI stack of best-in-class technologies that are well integrated, come with sample applications, and make it easy for developers to get up and running.

Neo4j (GraphQL)

NoSQL is a term that we have become very familiar with in recent times and it is used to describe a set of databases that don't make use of SQL when writing & composing queries. There are loads of different types of NoSQL databases ranging from key-value databases like the Reddis to document-oriented databases like MongoDB and Firestore to graph databases like Neo4J to multi-paradigm databases like FaunaDB and Cassandra.

this thread on this github issue could be useful.

Recently I have taken an interest in big data. https://neo4j.com/ , https://cassandra.apache.org/ , https://clickhouse.com/, https://www.elastic.co/ - are all databases I have experience with. Neo4j and Cassandra only as a hobby, but Clickhouse I have used in production, and Elasticsearch I have used for some 7 years now.

For organizations and their applications that are designed to detect fraud, like International Consortium of Investigative Journalists, or try to improve customer experience via personalization, as in the case of Tourism Media, a NoSQL graph database like Neo4j is a good match. In these kinds of use cases, the quantity of data we're dealing with is enormous, and the pattern we're searching for in the data is often complex.

First, you need to choose a specific graph database platform to work with, such as Neo4j, OrientDB, JanusGraph, Arangodb or Amazon Neptune. Once you have selected a platform, you can then start working with graph data using the platform's query language.

Welcome to the world of graph databases! When it comes to modelling complex and highly connected data, graph databases have proven to be an efficient and intuitive solution. And one of the most popular graph databases out there is Neo4j, which uses a query language called Cypher.