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docs(blog-axis-labels): set correct y axis in plot in Ibis benchmark blog post #9445

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Jun 26, 2024
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docs(blog-axis-labels): set correct y axis in plot in Ibis benchmark blog post #9445

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28ea4ec
Benchmark post: The "total execution time by system" plot should show…
szarnyasg Jun 26, 2024
2885d03
minor edit + freeze
lostmygithubaccount Jun 26, 2024
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4 changes: 2 additions & 2 deletions docs/_freeze/posts/ibis-bench/index/execute-results/html.json
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11 changes: 5 additions & 6 deletions docs/posts/ibis-bench/index.qmd
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Original file line number Diff line number Diff line change
Expand Up @@ -507,10 +507,9 @@ c = px.bar(
t.group_by("system").agg(
total_seconds=t["execution_seconds"].sum(),
seconds_per_query=t["execution_seconds"].mean(),
num_records=t.count(),
),
x="system",
y="num_records",
y="total_seconds",
color="system",
category_orders={"system": systems},
title="total execution time by system",
Expand Down Expand Up @@ -938,10 +937,10 @@ engines. Polars is an OLAP query engine with a Python dataframe API that can be
used as a more performant alternative to pandas.

All three make great Ibis backends and you can switch between them in a single
line of code. This let's you write your code once and run it on the engine
that's best for your use case. If a better engine comes along you'll likely be
able to use that too. And you can scale up and out across the 20+ backends Ibis
supports as needed.
line of code. This lets you write your code once and run it on the engine that's
best for your use case. If a better engine comes along you'll likely be able to
use that too. And you can scale up and out across the 20+ backends Ibis supports
as needed.

TPC-H is a decent benchmark *for what it benchmarks, which is limited*. We're
not running window functions, doing timeseries analysis, or feature engineering
Expand Down