Heneli @henelidotdev
Haskeller, Alum @recursecenter Sp2'17, alt: @anthrolawnmower heneli.dev SF Bay Area, CA Joined January 2014-
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Check out this deep dive into bit-packing integer arrays in #Rust without giving up mutability or O(1) random access. lukefleed.xyz/posts/compress… Cool to see the same tricks have already shown up in #Tantivy, the @rustlang Lucene alternative.
Most engineers overcomplicate locking. Here’s the truth: Optimistic Lock → Application-level control Pessimistic Lock → Database-level control Optimistic = let everyone edit, check for conflicts on save. (Think Google Docs.) Pessimistic = block everyone else until you’re…
B-tree skip scanning in Postgres 18: There’s a nice performance bump coming in Postgres 18 for some multi column B-tree indexes. Traditional B-tree index scans work by reading an index from a single starting point, determined by the query's conditions on the index’s columns in…
Every read from S3 takes 100ms-200ms on average. So reading a single byte takes roughly the same time as reading 10MB of data from S3 at 100MB/s speed. This complicates designing high-performance databases with S3 storage. If the query needs reading a million of 1KB data blocks…
I am once again begging you to put your database servers and application servers in the same region.
this is a great paper from snowflake, tl:dr; people (and tools) write very bad #SQL, my favorite which I did all the time is using joins on text field vldb.org/pvldb/vol18/p5…
this paper describes the hash join algorithm used in duckdb. typically a hash join works as follows: - imagine two tables: Orders (can be large), Customers (hopefully small). - imagine JOIN on O.cust_id = C.cust_id - the db engine builds a hash table on the join key cust_id and…
this paper describes the hash join algorithm used in duckdb. typically a hash join works as follows: - imagine two tables: Orders (can be large), Customers (hopefully small). - imagine JOIN on O.cust_id = C.cust_id - the db engine builds a hash table on the join key cust_id and…
Today is the new semester for @CMUDB's Intro to Database Systems! We're going harder into material than ever before. Projects are more challenging but you can use LLMs to help. We also have 10min talks each Wed from leading DB companies. Follow on YouTube: 15445.courses.cs.cmu.edu/fall2025
Finally shipped clickhouse-datafusion to crates.io/crates/clickho…! Bridge @ClickHouseDB and @ApacheDataFusio for high-performance analytics. Features: 🔗 Cross-database federation ⚡ Connection pooling 🎯 Arrow-native performance 🛠️ ClickHouse UDF support
DBMS Interview Challenge There has sometimes been confusion around io_uring in the past (e.g. misunderstanding of a Google post that in fact explicitly allowed for trusted environments). Nevertheless, (and performance aside) why is io_uring likely the future of I/O on Linux?
New to PlanetScale? We cover a lot of database topics on our blog. Here are some of our most popular posts over the last few years. First up, an interactive look at the history of IO devices and how IO device latency affects performance. planetscale.com/blog/io-device…
Folks: delivering @PostgreSQL CDC to @ApacheIceberg is, unfortuantely, not a "solved problem." My blog: risingwave.com/blog/postgres-…
Kudos to @cwi_da @peterabcz @afroozeh3 and team. Both FastLanes and ALP were mentioned on the @ApacheParquet sync call as candidates to add to the Parquet file format. Both papers are great examples of clear technical writing vldb.org/pvldb/vol13/p2… ir.cwi.nl/pub/33334/3333…
It is a common misconception that @ApacheParquet files are restricted to basic statistics. Footer metadata and offset-based addressing permit user-defined index structures today. Latest @ApacheDataFusio blog from Qi Zhi, Jigao Luo and myself explains how datafusion.apache.org/blog/2025/07/1…
.@abigale_kim's paper is unleashed! It's the most complete eval of DB extensions/plugins. We analyze @PostgreSQL, @MySQL, @mariadb , SQLite, @duckdb, @Redis. TLDR: Postgres ecosystem is fraught w/ footguns. Other DBMSs have fewer extns but less problems. DuckDB has cleanest API.
.@abigale_kim's paper is unleashed! It's the most complete eval of DB extensions/plugins. We analyze @PostgreSQL, @MySQL, @mariadb , SQLite, @duckdb, @Redis. TLDR: Postgres ecosystem is fraught w/ footguns. Other DBMSs have fewer extns but less problems. DuckDB has cleanest API.
Python is cursed
Python is cursed https://t.co/mFCHBcQXrN
Buckle up because we're crashing into the new year with my annual database retrospective: License change blowbacks! @databricks vs. @SnowflakeDB gangwar! @duckdb shotgun weddings! Buying a college quarterback with database money for your new lover! cs.cmu.edu/~pavlo/blog/20…
So, this is cool: treecalcul.us A very simple intensional calculus. A simple, privileged programming language where functions can be analyzed, serialized, etc.
How fast can servers talk to each other within a data center? Here I'm visualizing both the inter and intra availability zone latencies within Amazon's us-east-2 data center. Communicating between servers within an AZ is much faster! Check it out at benjdd.com/az
Which latency numbers you should know? Please note those are not precise numbers. They are based on some online benchmarks (Jeff Dean’s latency numbers + some other sources). 🔹L1 and L2 caches: 1 ns, 10 ns E.g.: They are usually built onto the microprocessor chip. Unless…
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