TileDB VS simdjson

- single cell genomics: in collaboration with the Chan-Zuckerberg Initiative, we recently released TileDB-SOMA for single cell data, with APIs for both Python and R built around a common storage specification: https://tiledb.com/blog/tiledb-101-single-cell

With TileDB, all data — tables, genomics, images, videos, location, time-series — across multiple domains is captured as multi-dimensional arrays. TileDB Cloud implements a totally serverless infrastructure and delivers access control, easier data and code sharing and distributed computing at global scale, eliminating cluster management, minimizing TCO and promoting scientific collaboration and reproducibility.

Website: https://tiledb.com

GitHub: https://github.com/TileDB-Inc/TileDB

Stavros from TileDB here (Founder and CEO). I thought of requesting some feedback from the community on this blog. It was only natural for a multi-dimensional array database like TileDB to offer vector (i.e., 1D array) search capabilities. But the team managed to do it very well and the results surprised us. We are just getting started in this domain and a lot of new algorithms and features are coming up, but the sooner we get feedback the better.

TileDB-Vector-Search Github repo: https://github.com/TileDB-Inc/TileDB-Vector-Search

TileDB-Embedded (core array engine) Github repo: https://github.com/TileDB-Inc/TileDB

TileDB 101: Vector Search (blog to get kickstarted): https://tiledb.com/blog/tiledb-101-vector-search/

TileDB, Inc. | Full-Time | REMOTE | USA | Greece | https://tiledb.com

TileDB is the database for complex data, allowing data scientists, researchers, and analysts to access, analyze, and share any data with any tool at global scale. We have just launched a vector search library leveraging TileDB and TileDB Cloud for powerful local search and seamless scaling to multi-modal organizational datasets and batched computation: https://tiledb.com/blog/why-tiledb-as-a-vector-database

With TileDB, all data — tables, genomics, images, videos, location, time-series — across multiple domains is captured as multi-dimensional arrays. Our vector search library and other offerings are designed to empower these datasets with extreme interoperability via numerous APIs and tool integrations across the data science ecosystem, eliminating the hassles and inefficiencies of data conversion. TileDB Cloud implements a totally serverless infrastructure and delivers access control, easier data and code sharing and distributed computing at global scale, eliminating cluster management, minimizing TCO and promoting scientific collaboration and reproducibility.

TileDB, Inc. | Full-Time | REMOTE | USA | Greece | https://tiledb.com

TileDB transforms the lives of analytics professionals and data scientists with a universal database, allowing them to access, analyze, and share any data with any tool at global scale. TileDB unifies the way we think about data, delivering superior performance and foundational data management capabilities. All data — tables, genomics, images, videos, location, time-series — across multiple domains is captured as multi-dimensional arrays. TileDB offers extreme interoperability via numerous APIs and tool integrations across the data science ecosystem, eliminating the hassles and inefficiencies of data conversion. TileDB Cloud implements a totally serverless infrastructure and delivers access control, easier data and code sharing and distributed computing at global scale, eliminating cluster management, minimizing TCO and promoting scientific collaboration and reproducibility.

TileDB, Inc. was spun out of MIT and Intel Labs in May 2017 and is backed by Two Bear Capital, Nexus Venture Partners, Uncorrelated Ventures, Intel Capital and Big Pi.

Recent HN article: https://news.ycombinator.com/item?id=23896131

Website: https://tiledb.com

GitHub: https://github.com/TileDB-Inc/TileDB

Docs: https://docs.tiledb.com

Blog: https://tiledb.com/blog

Our headquarters are located in Cambridge, MA and we have a subsidiary in Athens, Greece. We offer the ability to work remotely. If you are located outside of the USA and Greece we have options to accommodate this, don't hesitate to apply!

We have several open positions aimed at increasing TileDB’s feature set, growth and adoption. You will have the opportunity to work on innovative technology that creates impact on challenging and exciting problems in Genomics, Geospatial, Time Series, and more. Immediate features on the roadmap for TileDB Cloud include, advanced distributed computations, advanced computation pushdown, improved multi-cloud deployments and more.

We are actively seeking:

- Senior Golang Engineer

- Senior Python Engineer

- Site Reliability Engineer

- React Frontend Engineer

Apply today at https://tiledb.workable.com !

Interesting. Have you come across TileDB before?

https://tiledb.com/

Hi folks, Stavros from TileDB here. Here are my two cents on tabular data. TileDB (Embedded) is a very serious competitor to Parquet, the only other sane choice IMO when it comes to storing large volumes of tabular data (especially when combined with Arrow). Admittedly, we haven’t been advertising TileDB’s tabular capabilities, but that’s only because we were busy with much more challenging applications, such as genomics (population and single-cell), LiDAR, imaging and other very convoluted (from a data format perspective) domains.

Similar to Parquet:

* TileDB is columnar and comes with a lot of compressors, checksum and encryption filters.

* TileDB is built in C++ with multi-threading and vectorization in mind

* TileDB integrates with Arrow, using zero-copy techniques

* TileDB has numerous optimized APIs (C, C++, C#, Python, R, Java, Go)

* TileDB pushes compute down to storage, similar to what Arrow does

Better than Parquet:

* TileDB is multi-dimensional, allowing rapid multi-column conditions

* TileDB builds versioning and time-traveling into the format (no need for Delta Lake, Iceberg, etc)

* TileDB allows for lock-free parallel writes / parallel reads with ACID properties (no need for Delta Lake, Iceberg, etc)

* TileDB can handle more than tables, for example n-dimensional dense arrays (e.g., for imaging, video, etc)

Useful links:

* Github repo (https://github.com/TileDB-Inc/TileDB)

* TileDB Embedded overview (https://tiledb.com/products/tiledb-embedded/)

* Docs (https://docs.tiledb.com/)

* Webinar on why arrays as a universal data model (https://tiledb.com/blog/why-arrays-as-a-universal-data-model)

Happy to hear everyone’s thoughts.

It could be worth checking out TileDB: https://github.com/TileDB-Inc/TileDB The entire system, down to the data format itself, is optimized around storing multi-dimensional arrays. It also supports timestamps and real numbers as dimensions, which could be handy given your example data. [Full disclosure: I currently work for TileDB.]

TileDB, Inc. | Full-Time | REMOTE | USA | Greece | https://tiledb.com

TileDB, Inc. is the company behind TileDB, the first universal data engine. TileDB allows analytics professionals and data scientists to access, analyze, and share complex data sets with any tool at extreme scale. TileDB overcomes the constraints of columnar tables, flat files, and SQL-only tools, handling all data with a multi-dimensional array engine and extreme interoperability across the data science ecosystem. TileDB Cloud is a totally serverless offering of TileDB, which delivers access control and enables distributed computing at planet-scale, eliminating all cluster management and minimizing cost. TileDB, Inc. was spun out of MIT and Intel Labs in May 2017 and closed a $15M Series A in July 2020, following a previous $4M Seed Round.

Recent HN article: https://news.ycombinator.com/item?id=23896131

Website: https://tiledb.com

GitHub: https://github.com/TileDB-Inc/TileDB

Docs: https://docs.tiledb.com

Blog: https://tiledb.com/blog

Our headquarters are located in Cambridge, MA and we have a subsidiary in Athens, Greece. We offer the ability to work remotely, but the candidates must reside either in the US or in Greece. US candidates must be US citizens, whereas Greek candidates must be Greek or EU citizens.

We have several open positions aimed at increasing TileDB’s feature set, growth and adoption. You will have the opportunity to work on innovative technology that creates impact on challenging and exciting problems in Genomics, Geospatial, Time Series, and more. A few features on the roadmap include enhancing our TileDB Cloud offering, optimizing our serverless framework, improving integration with JupyterLab, and expanding our marketplace functionality.

We are primarily seeking:

- Senior Golang Engineer

Apply today at https://tiledb.workable.com !

It's also supported by simdjson [0] (which has a lot of language bindings [1]):

> Multithreaded processing of gigantic Newline-Delimited JSON (ndjson) and related formats at 3.5 GB/s

[0] https://simdjson.org/

[0] https://github.com/simdjson/simdjson?tab=readme-ov-file#bind...

String parsing is negligible compared to the speed of the DOM which is glacially slow: https://news.ycombinator.com/item?id=38835920

Come on, people, make an effort to learn how insanely fast computers are, and how insanely inefficient our software is.

String parsing can be done at gigabytes per second: https://github.com/simdjson/simdjson If you think that is the slowest operation in the browser, please find some resources that talk about what is actually happening in the browser?

Thanks for all the detailed information! That answers a bunch of my questions and the implementation of strlen is nice.

The instruction I was thinking of is pshufb. An example ‘weird’ use can be found for detecting white space in simdjson: https://github.com/simdjson/simdjson/blob/24b44309fb52c3e2c5...

This works as follows:

1. Observe that each ascii whitespace character ends with a different nibble.

2. Make some vector of 16 bytes which has the white space character whose final nibble is the index of the byte, or some other character with a different final nibble from the byte (eg first element is space =0x20, next could be eg 0xff but not 0xf1 as that ends in the same nibble as index)

3. For each block where you want to find white space, compute pcmpeqb(pshufb(whitespace, input), input). The rules of pshufb mean (a) non-ascii (ie bit 7 set) characters go to 0 so will compare false, (b) other characters are replaced with an element of whitespace according to their last nibble so will compare equal only if they are that whitespace character.

I’m not sure how easy it would be to do such tricks with vgather.vv. In particular, the length of the input doesn’t matter (could be longer) but the length of white space must be 16 bytes. I’m not sure how the whole vlen stuff interacts with tricks like this where you (a) require certain fixed lengths and (b) may have different lengths for tables and input vectors. (and indeed there might just be better ways, eg you could imagine an operation with a 256-bit register where you permute some vector of bytes by sign-extending the nth bit of the 256-bit register into the result where the input byte is n).

Additionally, if you like low level stuff, check out libfmt (https://github.com/fmtlib/fmt) - not a big project, not difficult to understand. Or something like simdjson (https://github.com/simdjson/simdjson).

Everything you said is totally reasonable. I'm a big fan of napkin math and theoretical upper bounds on performance.

simdjson (https://github.com/simdjson/simdjson) claims to fully parse JSON on the order of 3 GB/sec. Which is faster than OP's Go whitespace parsing! These tests are running on different hardware so it's not apples-to-apples.

The phrase "cannot go faster than this" is just begging for a "well ackshully". Which I hate to do. But the fact that there is an existence proof of Problem A running faster in C++ SIMD than OP's Probably B scalar Go is quite interesting and worth calling out imho. But I admit it doesn't change the rest of the post.

I have same question as /u/JDRiverRun : how do you deal with JSON, do you parse json on Rust side or on Emacs side. I see that you are requiring json.el in your lspce.el, but I haven't looked through entire file carefully. If you parse on Rust side, do you use simdjson (there are at least two Rust bindings to it)? If yes, what are your impressions, experiences compared to more "standard" json library?