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new e167e76 Commit build products
e167e76 is described below
commit e167e766666a58349569ecacc6423e7c88fd344c
Author: Build Pelican (action) <[email protected]>
AuthorDate: Fri Apr 11 17:37:28 2025 +0000
Commit build products
---
output/2025/04/10/fastest-tpch-generator/index.html | 2 +-
output/feeds/all-en.atom.xml | 2 +-
output/feeds/andrew-lamb-achraf-b-and-sean-smith.atom.xml | 2 +-
output/feeds/blog.atom.xml | 2 +-
4 files changed, 4 insertions(+), 4 deletions(-)
diff --git a/output/2025/04/10/fastest-tpch-generator/index.html
b/output/2025/04/10/fastest-tpch-generator/index.html
index e03c029..179e185 100644
--- a/output/2025/04/10/fastest-tpch-generator/index.html
+++ b/output/2025/04/10/fastest-tpch-generator/index.html
@@ -76,7 +76,7 @@ faster than any other implementation we know of.</p>
<p>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
<code>dbgen</code>
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 <a
href="https://parquet.apache.org/";>Apache Parquet</a> format, which takes 44
minutes using <a href="https://duckdb.org";>DuckDB</a>.
+2 minutes to create all 36 GB of SF=100 in <a
href="https://parquet.apache.org/";>Apache Parquet</a> format, which takes 44
minutes using <a href="https://duckdb.org";>DuckDB</a>.
It is finally convenient and efficient to run TPC-H queries locally when
testing
analytical engines such as DataFusion.</p>
<p><img alt="Time to create TPC-H parquet dataset for Scale Factor 1, 10, 100
and 1000" class="img-responsive"
src="/blog/images/fastest-tpch-generator/parquet-performance.png"
width="80%"/></p>
diff --git a/output/feeds/all-en.atom.xml b/output/feeds/all-en.atom.xml
index bde3316..07f0702 100644
--- a/output/feeds/all-en.atom.xml
+++ b/output/feeds/all-en.atom.xml
@@ -60,7 +60,7 @@ faster than any other implementation we know of.</p>
<p>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
<code>dbgen</code>
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 <a
href="https://parquet.apache.org/">Apache Parquet</a> format, which
takes 44 minutes using <a href="https://duckdb.org">DuckDB</a>.
+2 minutes to create all 36 GB of SF=100 in <a
href="https://parquet.apache.org/">Apache Parquet</a> format, which
takes 44 minutes using <a href="https://duckdb.org">DuckDB</a>.
It is finally convenient and efficient to run TPC-H queries locally when
testing
analytical engines such as DataFusion.</p>
<p><img alt="Time to create TPC-H parquet dataset for Scale Factor
1, 10, 100 and 1000" class="img-responsive"
src="/blog/images/fastest-tpch-generator/parquet-performance.png"
width="80%"/></p>
diff --git a/output/feeds/andrew-lamb-achraf-b-and-sean-smith.atom.xml
b/output/feeds/andrew-lamb-achraf-b-and-sean-smith.atom.xml
index 21b8660..0570f9a 100644
--- a/output/feeds/andrew-lamb-achraf-b-and-sean-smith.atom.xml
+++ b/output/feeds/andrew-lamb-achraf-b-and-sean-smith.atom.xml
@@ -60,7 +60,7 @@ faster than any other implementation we know of.</p>
<p>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
<code>dbgen</code>
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 <a
href="https://parquet.apache.org/">Apache Parquet</a> format, which
takes 44 minutes using <a href="https://duckdb.org">DuckDB</a>.
+2 minutes to create all 36 GB of SF=100 in <a
href="https://parquet.apache.org/">Apache Parquet</a> format, which
takes 44 minutes using <a href="https://duckdb.org">DuckDB</a>.
It is finally convenient and efficient to run TPC-H queries locally when
testing
analytical engines such as DataFusion.</p>
<p><img alt="Time to create TPC-H parquet dataset for Scale Factor
1, 10, 100 and 1000" class="img-responsive"
src="/blog/images/fastest-tpch-generator/parquet-performance.png"
width="80%"/></p>
diff --git a/output/feeds/blog.atom.xml b/output/feeds/blog.atom.xml
index c3fdf9f..bbacaca 100644
--- a/output/feeds/blog.atom.xml
+++ b/output/feeds/blog.atom.xml
@@ -60,7 +60,7 @@ faster than any other implementation we know of.</p>
<p>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
<code>dbgen</code>
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 <a
href="https://parquet.apache.org/">Apache Parquet</a> format, which
takes 44 minutes using <a href="https://duckdb.org">DuckDB</a>.
+2 minutes to create all 36 GB of SF=100 in <a
href="https://parquet.apache.org/">Apache Parquet</a> format, which
takes 44 minutes using <a href="https://duckdb.org">DuckDB</a>.
It is finally convenient and efficient to run TPC-H queries locally when
testing
analytical engines such as DataFusion.</p>
<p><img alt="Time to create TPC-H parquet dataset for Scale Factor
1, 10, 100 and 1000" class="img-responsive"
src="/blog/images/fastest-tpch-generator/parquet-performance.png"
width="80%"/></p>
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