scsmithr commented on code in PR #67:
URL: https://github.com/apache/datafusion-site/pull/67#discussion_r2031364896
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content/blog/2025-04-10-fastest-tpch-generator.md:
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+---
+layout: post
+title: tpchgen-rs World’s fastest open source TPC-H data generator, written in
Rust
+date: 2025-04-10
+author: Andrew Lamb, Achraf B, and Sean Smith
+categories: [performance]
+---
+
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
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+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
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+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+<style>
+/* Table borders */
+table, th, td {
+ border: 1px solid black;
+ border-collapse: collapse;
+}
+th, td {
+ padding: 3px;
+}
+</style>
+
+3 members of the [Apache DataFusion] community used Rust and open source
+development to build [tpchgen-rs], a fully open TPC-H data generator over 20x
+faster than any other implementation we know of.
+
+It is now possible to create the TPC-H SF=100 dataset in 72.23 seconds (1.4
GB/s
+😎) on a Macbook Air M3 with 16GB of memory, compared to the classic `dbgen`
+which takes 30 minutes<sup>1</sup> (0.05GB/sec). On the same machine, it takes
less than
+2 minutes to create all 3.6 GB of SF=100 in [Apache Parquet] format, which
takes 44 minutes using [DuckDB].
+It is finally convenient and efficient to run TPC-H queries locally when
testing
+analytical engines such as DataFusion.
+
+<img src="/blog/images/fastest-tpch-generator/parquet-performance.png"
alt="Time to create TPC-H parquet dataset for Scale Factor 1, 10, 100 and
1000" width="80%" class="img-responsive">
+
+**Figure 1**: Time to create TPC-H dataset for Scale Factor (see below) 1, 10,
+100 and 1000 as 8 individual SNAPPY compressed parquet files using a 22 core
GCP
+VM with 88GB of memory. For Scale Factor(SF) 100 `tpchgen` takes 1 minute and
14 seconds and
+[DuckDB] takes 17 minutes and 48 seconds. For SF=1000, `tpchgen` takes 10
+minutes and 26 and uses about 5 GB of RAM at peak, and we could not measure
+DuckDB’s time as it [requires 647 GB of RAM], more than the 88 GB that was
+available on our test machine. The testing methodology is in the
+[documentation].
+
+[DuckDB]: https://duckdb.org
+[requires 647 GB of RAM]:
https://duckdb.org/docs/stable/extensions/tpch.html#resource-usage-of-the-data-generator
+[documentation]:
https://github.com/clflushopt/tpchgen-rs/blob/main/benchmarks/BENCHMARKS.md
+
+This blog explains what TPC-H is, how we ported the vintage C data generator to
+Rust (yes, [RWIR]) and optimized its performance over the course of a few weeks
+of part-time work. We began this project so we can easily generate TPC-H data
in
+[Apache DataFusion] and [GlareDB].
+
+[RWIR]: https://www.reddit.com/r/rust/comments/4ri2gn/riir_rewrite_it_in_rust/
+[Apache DataFusion]: https://datafusion.apache.org/
+[GlareDB]: https://glaredb.com/
+
+# Try it for yourself
+
+The tool is entirely open source under the [Apache 2.0
license](https://www.apache.org/licenses/LICENSE-2.0). Visit the [tpchgen-rs
repository](https://github.com/clflushopt/tpchgen-rs) or try it for yourself by
run the following commands after [installing
Rust](https://www.rust-lang.org/tools/install):
+
+```shell
+$ cargo install tpchgen-cli
+
+# create SF=1 in classic TBL format
+$ tpchgen-cli -s 1
+
+# create SF=10 in Parquet
+$ tpchgen-cli -s 10 --format=parquet
+```
+
+# What is TPC-H / dbgen?
+
+The popular [TPC-H] benchmark (often referred to as TPCH) helps evaluate the
+performance of database systems on [OLAP] queries*, *the kind used to build BI
+dashboards.
+
+TPC-H has become a de facto standard for analytic systems. While there are
[well
+known] limitations as the data and queries do not well represent many real
world
+use cases, the majority of analytic database papers and industrial systems
still
+use TPC-H query performance benchmarks as a baseline. You will inevitably find
+multiple results for “`TPCH Performance <your favorite database>`” in any
+search engine.
+
+The benchmark was created at a time when access to high performance analytical
+systems was not widespread, so the [Transaction Processing Performance Council]
+defined a process of formal result verification. More recently, given the broad
+availability of free and open source database systems, it is common for users
to
+run and verify TPC-H performance themselves.
+
+TPC-H simulates a business environment with eight tables: `REGION`, `NATION`,
+`SUPPLIER`, `CUSTOMER`, `PART`, `PARTSUPP`, `ORDERS`, and `LINEITEM`. These
+tables are linked by foreign keys in a normalized schema representing a supply
+chain with parts, suppliers, customers and orders. The benchmark itself is 22
+SQL queries containing joins, aggregations, and sorting operations.
+
+The queries run against data created with <code>[dbgen]</code>, a program
+written in a pre [C-99] dialect, which generates data in a format called *TBL*
+(example in Figure 2). `dbgen` creates data for each of the 8 tables for a
+certain *Scale Factor*, commonly abbreviated as SF. Example Scale Factors and
+corresponding dataset sizes are shown in Table 1. There is no theoretical upper
+bound on the Scale Factor.
+
+[TPC-H]: https://www.tpc.org/tpch/
+[OLAP]: https://en.wikipedia.org/wiki/Online_analytical_processing
+[well known]: https://www.vldb.org/pvldb/vol9/p204-leis.pdf
+[Transaction Processing Performance Council]: https://www.tpc.org/
+[dbgen]: https://github.com/electrum/tpch-dbgen
+[C-99]: https://en.wikipedia.org/wiki/C99
+
+```text
+103|2844|845|3|23|40177.32|0.01|0.04|N|O|1996-09-11|1996-09-18|1996-09-26|NONE|FOB|ironic
accou|
+229|10540|801|6|29|42065.66|0.04|0.00|R|F|1994-01-14|1994-02-16|1994-01-22|NONE|FOB|uriously
pending |
+263|2396|649|1|22|28564.58|0.06|0.08|R|F|1994-08-24|1994-06-20|1994-09-09|NONE|FOB|efully
express fo|
+327|4172|427|2|9|9685.53|0.09|0.05|A|F|1995-05-24|1995-07-11|1995-06-05|NONE|AIR|
asymptotes are fu|
+450|5627|393|4|40|61304.80|0.05|0.03|R|F|1995-03-20|1995-05-25|1995-04-14|NONE|RAIL|ve.
asymptote|
+```
+
+**Figure 2**: Example TBL formatted output of `dbgen` for the `LINEITEM` table
+
+<table>
+ <tr>
+ <td><strong>Scale Factor</strong>
+ </td>
+ <td><strong>Data Size (TBL)</strong>
+ </td>
+ <td><strong>Data Size (Parquet)</strong>
+ </td>
+ </tr>
+ <tr>
+ <td>0.1
+ </td>
+ <td>103 Mb
+ </td>
+ <td>31 Mb
+ </td>
+ </tr>
+ <tr>
+ <td>1
+ </td>
+ <td>1 Gb
+ </td>
+ <td>340 Mb
+ </td>
+ </tr>
+ <tr>
+ <td>10
+ </td>
+ <td>10 Gb
+ </td>
+ <td>3.6 Gb
+ </td>
+ </tr>
+ <tr>
+ <td>100
+ </td>
+ <td>107 Gb
+ </td>
+ <td>38 Gb
+ </td>
+ </tr>
+ <tr>
+ <td>1000
+ </td>
+ <td>1089 Gb
+ </td>
+ <td>379 Gb
+ </td>
+ </tr>
+</table>
+
+
+**Table 1**: TPC-H data set sizes at different scale factors for both TBL and
[Apache Parquet].
+
+[Apache Parquet]: https://parquet.apache.org/
+
+# Why do we need a new TPC-H Data generator?
+
+Despite the known limitations of the TPC-H benchmark, it is so well known that
it
+is used frequently in database performance analysis. To run TPC-H, you must
first
+load the data, using `dbgen`, which is not ideal for several reasons:
+
+1. You must find and compile a copy of the 15+ year old C program (for example
[electrum/tpch-dbgen])
+2. `dbgen` requires substantial time (Figure 3) and is not able to use more
than one core.
+3. It outputs TBL format, which typically requires loading into your database
(for example, [here is how to do so] in Apache DataFusion) prior to query.
+4. The implementation makes substantial assumptions about the operating
environment, making it difficult to extend or embed into other
systems.<sup>2</sup>
+
+[electrum/tpch-dbgen]: https://github.com/electrum/tpch-dbgen
+[here is how to do so]:
https://github.com/apache/datafusion/blob/507f6b6773deac69dd9d90dbe60831f5ea5abed1/datafusion/sqllogictest/test_files/tpch/create_tables.slt.part#L24-L124
+
+
+<img src="/blog/images/fastest-tpch-generator/tbl-performance.png" alt="Time
to generate TPC-H data in TBL format" width="80%" class="img-responsive">
+
+**Figure 3**: Time to generate TPC-H data in TBL format. `tpchgen` is
+shown in blue. `tpchgen` restricted to a single core is shown in red.
Unmodified
+`dbgen` is shown in green and `dbgen` modified to use `-O3` optimization level
+is shown in yellow.
+
+`dbgen` is so inconvenient and takes so long that vendors often provide
+preloaded TPC-H data, for example [Snowflake Sample Data], [Databricks Sample
+datasets] and [DuckDB Pre-Generated Data Sets].
+
+[Snowflake Sample Data]:
https://docs.snowflake.com/en/user-guide/sample-data-tpch
+[Databricks Sample datasets]:
https://docs.databricks.com/aws/en/discover/databricks-datasets
+[DuckDB Pre-Generated Data Sets]:
https://duckdb.org/docs/stable/extensions/tpch.html#pre-generated-data-sets
+
+
+In addition to pre-generated datasets, DuckDB also provides a [TPC-H
extension]
+for generating TPC-H datasets within DuckDB. This is so much easier to use than
+the current alternatives that it leads many researchers and other thought
+leaders to use DuckDB to evaluate new ideas. For example, [Wan Shen
+Lim] explicitly [mentioned the ease of creating the TPC-H dataset] as one
reason
+the first student project of [CMU-799 Spring 2025] used DuckDB.
+
+
+[TPC-H extension]: https://duckdb.org/docs/stable/extensions/tpch.html
+[Wan Shen Lim]: https://github.com/lmwnshn
+[mentioned the ease of creating the TPC-H dataset]:
https://github.com/apache/datafusion/issues/14373
+[CMU-799 Spring 2025]: https://15799.courses.cs.cmu.edu/spring2025/
+
+As beneficial as the DuckDB TPC-H extension is, it is non-ideal for several
reasons:
+
+1. Creates data in a proprietary format, which requires export to use in other
systems.
+2. Requires significant time (e.g. 17 minutes for Scale Factor 10).
+3. Requires unnecessarily large amounts of memory (e.g. 71 GB for Scale Factor
10)
+
+The above limitations makes it impractical to generate Scale Factor 100 and
+above on laptops or standard workstations, though DuckDB offers [pre-computed
+files] for larger factors<sup>3</sup>.
+
+[pre-computed files]:
https://duckdb.org/docs/stable/extensions/tpch.html#pre-generated-data-sets
+
+# Why Rust?
+
+Realistically we used Rust because we wanted to integrate the data generator
+into [Apache DataFusion] and [GlareDB]. However, we also believe Rust is
+superior to C/C++ due to its comparable performance, but much higher programmer
+productivity (Figure 4). Productivity in this case refers to the ease of
+optimizing and adding multithreading without introducing hard to debug memory
+safety or concurrency issues.
+
+While Rust does allow unsafe access to memory (eliding bounds checking, for
+example), when required for performance, our implementation is entirely memory
+safe. The only [unsafe] code is used to [skip] UTF8 validation on known ASCII
+strings.
+
+[Apache DataFusion]: https://datafusion.apache.org/
+[GlareDB]: https://glaredb.com/
+[unsafe]:
https://github.com/search?q=repo%3Aclflushopt%2Ftpchgen-rs%20unsafe&type=code
+[skip]:
https://github.com/clflushopt/tpchgen-rs/blob/c651da1fc309f9cb3872cbdf71e4796904dc62c6/tpchgen/src/text.rs#L72
+
+<img src="/blog/images/fastest-tpch-generator/lamb-theory.png" alt="Lamb
Theory on Evolution of Systems Languages" width="80%" class="img-responsive">
+
+**Figure 4**: Lamb Theory of System Language Evolution from [Boston University
+MiDAS Fall 2024 (Data Systems Seminar)] [slides(pdf)], [recording]. Special
+thanks to [@KurtFehlhauer]
+
+[Boston University MiDAS Fall 2024 (Data Systems Seminar)]:
https://midas.bu.edu/seminar.html
+[slides(pdf)]: https://midas.bu.edu/assets/slides/andrew_lamb_slides.pdf
+[recording]: https://www.youtube.com/watch?v=CpnxuBwHbUc
+[@KurtFehlhauer]: https://x.com/KurtFehlhauer
+
+# How: The Journey
+
+We did it together as a team in the open over the course of a few weeks.
+[Wan Shen Lim] inspired the project by pointing out the benefits of [easy TPC-H
+dataset creation] and [suggesting we check out a Java port on February 11,
+2025]. Achraf made [first commit a few days later] on February 16, and [Andrew
+and Sean started helping on March 8, 2025] and we [released version 0.1] on
+March 30, 2025.
+
+[Wan Shen Lim]: https://github.com/lmwnshn
+[easy TPC-H dataset creation]:
https://github.com/apache/datafusion/issues/14373
+[suggesting we check out a Java port on February 11, 2025]:
https://github.com/apache/datafusion/issues/14608#issuecomment-2651044600
+[first commit a few days later]:
https://github.com/clflushopt/tpchgen-rs/commit/53d3402680422a15349ece0a7ea3c3f001018ba0
+[Andrew and Sean started helping on March 8, 2025]:
https://github.com/clflushopt/tpchgen-rs/commit/9bb386a4c55b8cf93ffac1b98f29b5da990ee79e
+[released version 0.1]: https://crates.io/crates/tpchgen/0.1.0
+
+## Optimizing Single Threaded Performance
+
+Archaf [completed the end to end conformance tests], to ensure correctness, and
+an initial [cli check in] on March 15, 2025.
+
+[completed the end to end conformance tests]:
https://github.com/clflushopt/tpchgen-rs/pull/16
+[cli check in]: https://github.com/clflushopt/tpchgen-rs/pull/12
+
+On a Macbook Pro M3 (Nov 2023), the initial performance numbers were actually
+slower than the original Java implementation which was ported 😭. This wasn’t
+surprising since the focus of the first version was to get a byte of byte
+compatible port, and knew about the performance shortcomings and how to
approach
+them.
+
+
+<table>
+ <tr>
+ <td><strong>Scale Factor</strong>
+ </td>
+ <td><strong>Time</strong>
+ </td>
+ </tr>
+ <tr>
+ <td>1
+ </td>
+ <td>0m10.307s
+ </td>
+ </tr>
+ <tr>
+ <td>10
+ </td>
+ <td>1m26.530s
+ </td>
+ </tr>
+ <tr>
+ <td>100
+ </td>
+ <td>14m56.986s
+ </td>
+ </tr>
+</table>
Review Comment:
Just out of curiosity, why can't these be normal markdown tables?
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