Show HN: BloomSearch – Keyword search with hierarchical Bloom filters | Hacker News Reader

Show HN: BloomSearch – Keyword search with hierarchical Bloom filters

github.com

66 pointsdangoodmanUT13 comments

Hey HN! I got nerd-sniped by Bloom Filters this weekend, specifically for searching datasets with high "cardinality" (number of unique items).

They're an _amazing_ data structure that, at a fixed size, tracks potential set membership. That means unlike normal b-tree indexes, they don't grow with the number of unique items in the dataset.

This makes them great for "needle in a haystack" search (logs, document) as implementations like VictoriaMetrics and Bing's BitFunnel show. I've used them in the past, but they've never been center-stage in my projects.

I wanted high cardinality keyword search for ANOTHER project... and, well, down the yak-shaving rabbit hole we go!

BloomSearch brings this into an extensible Go package:

- Very memory efficient via bloom filters and streaming row scans

- DataStore and MetaStore interfaces for any backend (can be same or separate)

- Hierarchical pruning via partitions, minmax indexes, and of course bloom filters

- Search by field, token, or field:token with complex combinators

- Disaggregated storage and compute for unbound ingest and query throughput

And of course, you know I had to make a custom file format ^-^ (FILE_FORMAT.MD)

BloomSearch is optimized for massive concurrency, arbitrary cardinality and dataset size, and super low memory usage. There's still a lot on the table too in terms of size and performance optimizations, but I'm already super pleased with it. With distributed query processing I'm targeting >100B rows/s over large datasets.

I'm also excited to replace our big logging bill ~$0.003/GB for log storage with infinite retention and guilt-free querying :P

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How do you use Bloom filters to replace your current logs? Bloom filters are very good at knowing for sure that something does not exist in a set. What exactly is your set in this case? In other words, how can you query a dataset that's behind a bloom filter?

There are three kinds of queries supported for keywords:

- field

- term

- term in field

Each file, and each row group within the file, has 3 bloom filters to handle these queries.

So something like:

{"user": {"name": "John", "tags": [{"type": "user"}, {"role": "admin"}]}}

Gets turned into queryable pairs of:

[{Path: "user.name", Values: ["John"]}, {Path: "user.tags.type", Values: ["user"]}, {Path: "user.tags.role", Values: ["admin"]}]

Then you can search for:

- any record that has "john" in it

- any record that has the "user.tags.type" key

- any record that has "user.tags.type"="user" and "user.tags.role"="admin"

Which bloom filters are used depends on how you build the query, but they test for whether a row matching the condition(s) is in the file/row group

Doesnt this mean you have to do a row scan though? With BTREE you have O(log N) index query and that’s it

To actually retrieve the row, yeah, but a btree index size scales ~linearly with the dataset size.

You can prune based on partitions, minmax indexes, then bloom filters first. By that point the row group scan, if all other cheks suggest that the row you are after is in the block, is a very small amount of data.

https://itnext.io/how-do-open-source-solutions-for-logs-work... covers this very well

Does that mean that you can't query substrings or do fuzzy searches?

The BloomSearchEngine takes a TokenizerFunc so you can determine how JSON values are tokenized (that's why each path always returns an array of strings).

The default tokenizer is a a whitespace one: https://github.com/danthegoodman1/bloomsearch/blob/148a79967...

So {"name": "John Smith"} is tokenized to [{Path: "name", Values: ["john", "smith"]}], and the bloom filters will store:

- field: "name"

- token: "john"

- token: "smith"

- fieldtoken: "name:john"

- fieldtoken: "name:smith"

The same tokenizer must be used at query time too.

Fuzzy searches and sub-word searches could be supported with custom tokenizers (eg trigrams, stemming), but it's more generally targeting the "I know some exact subset of the record, I need all that have this exactly" searches

Not OP, but to me, this reads fairly similar to how ClickHouse can be set up, with Bloom filters, MinMax indexes, etc.

A way to “handle” partial substrings is to break up your input data into tokens (like substrings split in spaces or dashes) and then you can break up your search string up in the same way.

If you want to adapt the technique to full-text search, you can index trigrams instead of full keywords.

haha you beat me to it! yes tokenize with trigrams is a very simple way to get this functionality. That's how systems like postgres has historically done it

Super ! Bloom filters are smart. Created a hierchial bloom filter for a revisit log for an indexer almost 20 years ago. Saved us $$$ and a still kind of proud of it

Library/package with AGPL license, not a great thing even for a lot of FOSS projects.

Not true, its very permissive as long as it's not a "feature" of a product you're building, or offering as a service.

Otherwise you can happily use it in indirect backend services (e.g. your own logging) without license concerns.

I have a question about using HBFs for logs - how do you determine the hierarchy ?

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