jaychia
Joined 5 years ago
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25
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Oct 21
Recent submissions
Comment · 7 months ago
Comment · 10 months ago
It's fairly surprising to me how naive/early we are still in the techniques that we use here.
Anthropic's post on the Claude Agent SDK (formerly Claude Code SDK) talks about how the agent "gathers context", and is fairly accurate as to how people do it today.
1. Agentic Search (give the agent tools and let it run its own search trajectory): specifically, the industry seems to have made really strong advances towards giving the agents POSIX filesystems and UNIX utilities (grep/sed/awk/jq/head etc) for navigating data. MCP for data retrieval also falls into this category, since the agent can choose to invoke tools to hit MCP servers for required data. But because coding agents know filesystems really well, it seems like that is outperforming everything else today ("bash is all you need").
2. Semantic Search (essentially chunking + embedding, a la RAG in 2022/2023): I've definitely noticed a growing trend amongst leading AI companies to move away from this. Especially if your data is easily represented as a filesystem, (1) seems to be the winning approach.
Interestingly though this approach has a pretty glaring flaw: all the approaches today really only provide the agents with raw unprocessed data. There's a ton of recomputation on raw data! Agents that have sifted through the raw data once (maybe it reads v1, v2 and v_final of a design document or something) will have to do the same thing again in the next session.
I have a strong thesis that this will change in 2026 (Knowledge Curation, not search, is the next data problem for AI) https://www.daft.ai/blog/knowledge-curation-not-search-is-th... and we're building towards this future as well. Related ideas here that have anecdotal evidence of providing benefits, but haven't really stuck yet in practice include: agentic memory, processing agent trajectory logs, continuous learning, persistent note-taking etc.
Comment · 11 months ago
Great article and a timely reminder for many :)
Applicable not just for grad school applications, but also to job apps, startups, and relationships.
Hang in there y'all, all it takes is for one to work out. Keep working hard, kings & queens.
Comment · 1 year ago
Check out Daft (www.getdaft.io) - we've been working really hard on our Iceberg support. Supports full reads/writes (including partitioned writes) and our SQL support is also coming along quite well!
Also no cluster, no JVM. Just `pip install daft` and go. Runs locally (as fast as DuckDB for a lot of workloads; faster, if you have S3 cloud data access) and also runs distributed if you have a Ray cluster you can point it at
(Disclaimer: I work on it)
Comment · 1 year ago
Hey, I'm one of the developers of Daft :)
Thanks for the feedback on marketing! Daft is indeed distributed using Ray, but to do so involves Daft being architected very carefully for distributed computing (e.g. using map/reduce paradigms).
Ray fulfills almost a Kubernetes-like role for us in terms of orchestration/scheduling (admittedly it does quite a bit more as well especially in the area of data movement). But yes the technologies are very complementary!
Comment · 2 years ago
One of the maintainers of Daft here.
Just dug through the datachain codebase to understand a little more. I think while both projects have a Dataframe interface, they're very different projects!
Datachain seems to operate more on the orchestration layer, running Python libraries such as PIL and requests (for making API calls) and relying on an external database engine (SQLite or BigQuery/Clickhouse) for the actual compute.
Daft is an actual data engine. Essentially, it's "multimodal BigQuery/Clickhouse". We've built out a lot of our own data system functionality such as custom Rust-defined multimodal data structures, kernels to work on multimodal types, a query optimizer, distributed joins etc.
In non-technical terms, I think this means that Datachain really is more of a "DBT" which orchestrates compute over an existing engine, whereas Daft is the actual compute/data engine that runs the workload. A project such as Datachain could actually run on top of Daft, which can handle the compute and I/O operations necessary to execute the requested workload.
Comment · 2 years ago
I work on Daft and we’ve been collaborating with the team at Amazon to make this happen for about a year now!
We love Ray, and are excited about the awesome ecosystem of useful + scalable tools that run on it for model training and serving. We hope that Daft can complement the rest of the Ray ecosystem to enable large scale ETL/analytics to also run on your existing Ray clusters. If you have an existing Ray cluster setup, you absolutely should have access to best-in-class ETL/analytics without having to run a separate Spark cluster.
Also, on the nerdier side of things - the primitives that Ray provides gives us a real opportunity to build a solid non-JVM based, vectorized distributed query engine. We’re already seeing extremely good performance improvements here vs Spark, and are really excited about some of the upcoming work to get even better performance and memory stability.
This collaboration with Amazon really battle-tested our framework :) happy to answer any questions if folks have them.
Comment · 2 years ago
There’s a lot of interesting work happening in this area (see: XTable).
We are building a Python distributed query engine, and share a lot of the same frustrations… in fact until quite recently most of the table formats only had JVM client libraries and so integrating it purely natively with Daft was really difficult.
We finally managed to get read integrations across Iceberg/DeltaLake/Hudi recently as all 3 now have Python/Rust-facing APIs. Funny enough, the only non-JVM implementation of Hudi was contributed by the Hudi team and currently still lives in our repo :D (https://github.com/Eventual-Inc/Daft/tree/main/daft/hudi/pyh...)
It’s still the case that these libraries still lag behind their JVM counterparts though, so it’s going to be a while before we see full support across the full featureset of each table format. But we’re definitely seeing a large appetite for working with table formats outside of the JVM ecosystem (e.g. in Python and Rust)
Comment · 2 years ago
Interesting. Daft currently does validation on types/names only at runtime. The flow looks like:
1. Construct a dataframe (performs schema inference)
2. Access (now well-typed) columns and operations on those columns in the dataframe, with associated validations.
Unfortunately step (1) can only happen at runtime and not at type-checking-time since it requires running some schema inference logic, and step (2) relies on step (1) because the expressions of computation are "resolved" against those inferred types.
However, if we can fix (1) to happen at type-checking time using user-provided type-hints in place of the schema inference, we can maybe figure out a way to propagate this information through to mypy.
Would love to continue the discussion further as an Issue/Discussion on our Github!
Comment · 2 years ago
Daft developer here!
We actually already have read support. Check out the pyiceberg docs' Daft section: https://py.iceberg.apache.org/api/#daft
It's also very easy to use from Daft itself: `daft.read_iceberg(pyiceberg_table)`. Give it a shot and let us know how it works for you!
Comment · 2 years ago
Oh yes good point! We'll be sure to add more details about comparisons with local dataframe libraries such as Pandas/Polars/DuckDB.
Comment · 2 years ago
Hello, Daft developer here!
The network indeed becomes the bottleneck. In 2 main ways:
1. Reading data from cloud storage is very expensive. Here’s a blogpost where we talk about some of the optimizations we’ve done in that area: https://blog.getdaft.io/p/announcing-daft-02-10x-faster-io
2. During a global shuffle stage (e.g. sorts, joins, aggregations) network transfer of data between nodes becomes the bottleneck.
This is why the advice is often to stick with a local solution such as DuckDB, Polars or Pandas if you can keep vertically scaling!
However, horizontally scaling does have some advantages:
- Higher aggregate network bandwidth for performing I/O with storage
- Auto-scaling to your workload’s resource requirements
- Scaling to large workloads which may not fit on a single machine. This is more common in Daft usage because we also work with multimodal data such as images, tensors and more for ML data modalities.
Hope this helps!
Comment · 2 years ago
Hello! Daft developer here. We are most similar in API to Polars and PySpark.
And thanks for the feedback! We’ll add more capabilities for regex, as well as flesh out our documentation for partitioning.
Edit: added a new issue for regex support :) https://github.com/Eventual-Inc/Daft/issues/1962
Comment · 2 years ago
Hello! Daft developer here. The benchmarks we performed aren’t directly comparable to the benchmarks on TPC-H’s own page because of differences in hardware, storage etc.
For hardware, we were using AWS i3.2xlarge machines in a distributed cluster. And on the storage side we are reading Parquet files over the network from AWS S3. This is most representative of how users run query engines like Daft.
The TPC-H benchmarks are usually performed on databases which have pre-ingested the data into a single-node server-grade machine that’s running the database.
Note that Daft isn’t really a “database”, because we don’t have proprietary storage. Part of the appeal of using query engines like Daft and Spark is to able to read data “at rest” (as Parquet, CSV, JSON etc). However this will definitely be slower than a database which has pre-ingested the data into indexed storage and proprietary formats!
Hope that helps explain the discrepancies!
Excited for unstructured/multimodal data processing to become increasingly commoditized and abstracted away so that more such datasets can be built