node-redis VS ann-benchmarks

Probably redis.

Did you have an overdose of theory? Let us now taste some code that can help us apply some concepts. This example focuses on the text search. Redis provides us with a straightforward command line interface, along with useful SDK modules in most common languages. Below is a JavaScript code that uses Node Redis module to communicate with the Redis Server. Along with the JavaScript code, we can see the corresponding CLI commands. We need a text-rich dataset to save in our database and demonstrate the search functionality. For this, we will use a dump of poems obtained from Kaggle. The JSON chunk can be found on this link.

You need to call and await the connect method on your clients before you can send commands. For an example, see the sample usage code in the project README.

First thing to deal with is getting the data (using the GeoLite2 free geolocation data from MaxMind) into Redis so can actually query it. This was easier said than done. I used the node-redis lib and well, all the geo stuff in this lib are broken af (mildly speaking).

30 maximum connections may not seem like an issue as long as you are not building an application that has specific requirements for concurrency. This statement could be true if we are establishing connections between a Node server and a Redis cache since it is recommended that only one or two Redis client would be instantiated then reused in the Node server. In this case, there is a limited number of connections (clients are connections in Redis) needed when the server is running and communicating with Redis.

> Sure, the c++ is going to require you to do some sanitizing as you force your data into v8

it's not just sanitizing, there's a lot more to the object creation inside v8 itself. but, even if it were just sanitizing, that mechanism has become a lot more complicated than it ever was in v8 3.1 (timeframe around node 0.4) or 3.6 (timeframe around node 0.6). when interacting with c++, v8 makes no assumptions, whereas when interacting with javascript, a large number of assumptions can be made (e.g. which context and isolate is it being executed in, etc).

> but as we noted that's inevitable no matter how you slice it.

yes, from c++ to javascript and back, but when you need to make that trip multiple times, instead of once, that interchange adds up to quite a bit of extra code executed, values transformed, values checked, etc. sure, banging your head against a wall might not hurt once, but do it 40 times in a row and you're bound to be bloodied.

> Now maybe in some cases the v8 internals offer some advantages the generic c++ api can't access

by a fairly large margin, as it turns out, especially as v8 has evolved from the early 3.1 days to the current 9.8: 11 years. there has been significant speedup to javascript dealing with javascript objects compared to c++ dealing with javascript objects. see below.

> My memories of the redis client is different than yours so I'd be quite interested to see those conversations / benchmarks.

super easy to find, all of that was done in public: https://github.com/redis/node-redis/pull/242 - there are multiple benchmarks done by multiple people, and the initial findings were 15-20% speedup, but were improved upon. the speedup was from the decoding of the binary packet, which was passed as a single buffer, as opposed to parsing it externally and passing in each object through the membrane.

> As a simple thought experiment, in the scenario you're describing we should see a javascript implementation of a JSON parser to beat the pants off the v8 engine implementation, but this doesn't seem to the case.

that's a bit of a straw man argument. especially given that JSON.parse() is a single call and does not require any additional tooling/isolates/contexts to execute, it's just straight c++ code with very fast access into the v8 core:

    Local result = Local::New(isolate, JSON.Parse(jsonString));

I actually found 2 resources which might be useful to help me in setting the ttl expire period for the key: Redis-doc and issue-100 and I wil be dig in to it in couple days to figure it out

The easiest way to connect Redis with Upstash is to use the redis-client as described here.

After filling out the project details, cd into your project and install redis, a Node.js client for Redis, and dotenv, an environment variable loader.

On top of my head, pgvector only supports 2 indexes, those are running in memory only. They don't support GPU indexing, nor Disk based indexing, they also don't have separation of query and insertions.

Also with different people I've talked to, they struggle with scale past 100K-1M vector.

You can also have a look yourself from a performance perspective: https://ann-benchmarks.com/

https://ann-benchmarks.com/ is a good resource covering those libraries and much more.

We utilized the ANN Benchmarks methodology, a standard for benchmarking vector databases. Our tests used the dbpedia dataset of 1,000,000 OpenAI embeddings (1536 dimensions) and inner product distance metric for both Pinecone and pgvector.

Data warehouses are columnar stores. They are very different from row-oriented databases - like Postgres, MySQL. Operations on columns - e.g., aggregations (mean of a column) are very efficient.

Most vector databases use one of a few different vector indexing libraries - FAISS, hnswlib, and scann (google only) are popular. The newer vector dbs, like weaviate, have introduced their own indexes, but i haven't seen any performance difference -

Reference: https://ann-benchmarks.com/

(Blog author here). Thanks for the question. In this case the index for both DiskANN and pgvector HNSW is small enough to fit in memory on the machine (8GB RAM), so there's no need to touch the SSD. We plan to test on a config where the index size is larger than memory (we couldn't this time due to limitations in ANN benchmarks [0], the tool we use).

To your question about RAM usage, we provide a graph of index size. When enabling PQ, our new index is 10x smaller than pgvector HNSW. We don't have numbers for HNSWPQ in FAISS yet.

[0]: https://github.com/erikbern/ann-benchmarks/

In terms of "All You Need" for Vector Search, ANN Benchmarks (https://ann-benchmarks.com/) is a good site to review when deciding what you need. As with anything complex, there often isn't a universal solution.

txtai (https://github.com/neuml/txtai) can build indexes with Faiss, Hnswlib and Annoy. All 3 libraries have been around at least 4 years and are mature. txtai also supports storing metadata in SQLite, DuckDB and the next release will support any JSON-capable database supported by SQLAlchemy (Postgres, MariaDB/MySQL, etc).

pg_vector doesn't perform well compared to other methods, at least according to ANN-Benchmarks (https://ann-benchmarks.com/).

txtai is more than just a vector database. It also has a built-in graph component for topic modeling that utilizes the vector index to autogenerate relationships. It can store metadata in SQLite/DuckDB with support for other databases coming. It has support for running LLM prompts right with the data, similar to a stored procedure, through workflows. And it has built-in support for vectorizing data into vectors.

For vector databases that simply store vectors, I agree that it's nothing more than just a different index type.

The reason https://ann-benchmarks.com is so good, is that we can see a plot of recall vs latency. I can see you have some latency numbers in the leaderboard at the bottom, but it's very difficult to make a decision.

As a practitioner that works with vector databases every day, just latency is meaningless to me, because I need to know if it's fast AND accurate, and what the tradeoff is! You can't have it both ways. So it would be helpful if you showed plots showing this tradeoff, similar to ann-benchmarks.