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commit f4a1df8231c786cb4801ffd20990e520783261ff
Author: Build Pelican (action) <priv...@infra.apache.org>
AuthorDate: Wed Jun 11 15:40:20 2025 +0000
Commit build products
---
blog/2025/06/15/optimizing-sql-dataframes-part-one/index.html | 7 ++++---
blog/feeds/alamb-akurmustafa.atom.xml | 7 ++++---
blog/feeds/all-en.atom.xml | 7 ++++---
blog/feeds/blog.atom.xml | 7 ++++---
4 files changed, 16 insertions(+), 12 deletions(-)
diff --git a/blog/2025/06/15/optimizing-sql-dataframes-part-one/index.html
b/blog/2025/06/15/optimizing-sql-dataframes-part-one/index.html
index 0621f36..0fd5637 100644
--- a/blog/2025/06/15/optimizing-sql-dataframes-part-one/index.html
+++ b/blog/2025/06/15/optimizing-sql-dataframes-part-one/index.html
@@ -112,9 +112,10 @@ language such as Python, where you describe how to do the
computation as shown
in Figure 1.</p>
<p><img alt="Fig 1: Query Execution." class="img-responsive"
src="/blog/images/optimizing-sql-dataframes/query-execution.png"
width="80%"/></p>
<p><strong>Figure 1</strong>: Query Execution: Users describe the answer they
want using either
-a DataFrame or SQL. The query planner or DataFrame API translates that
-description into an <em>Initial Plan</em>, which is correct but slow. The Query
-Optimizer then rewrites the initial plan to an <em>Optimized Plan</em>, which
computes
+SQL or a DataFrame. For SQL, a Query Planner translates the parsed query
+into an <em>initial plan</em>. The DataFrame API creates an initial plan
directly.
+The initial plan is correct, but slow. Then, the Query
+Optimizer rewrites the initial plan into an <em>optimized plan</em>, which
computes
the same results but faster and more efficiently. Finally, the Execution Engine
executes the optimized plan producing results.</p>
<h2>SQL, DataFrames, LogicalPlan Equivalence</h2>
diff --git a/blog/feeds/alamb-akurmustafa.atom.xml
b/blog/feeds/alamb-akurmustafa.atom.xml
index c19d77a..af2cdb7 100644
--- a/blog/feeds/alamb-akurmustafa.atom.xml
+++ b/blog/feeds/alamb-akurmustafa.atom.xml
@@ -94,9 +94,10 @@ language such as Python, where you describe how to do the
computation as shown
in Figure 1.</p>
<p><img alt="Fig 1: Query Execution." class="img-responsive"
src="/blog/images/optimizing-sql-dataframes/query-execution.png"
width="80%"/></p>
<p><strong>Figure 1</strong>: Query Execution: Users
describe the answer they want using either
-a DataFrame or SQL. The query planner or DataFrame API translates that
-description into an <em>Initial Plan</em>, which is correct but
slow. The Query
-Optimizer then rewrites the initial plan to an <em>Optimized
Plan</em>, which computes
+SQL or a DataFrame. For SQL, a Query Planner translates the parsed query
+into an <em>initial plan</em>. The DataFrame API creates an
initial plan directly.
+The initial plan is correct, but slow. Then, the Query
+Optimizer rewrites the initial plan into an <em>optimized
plan</em>, which computes
the same results but faster and more efficiently. Finally, the Execution Engine
executes the optimized plan producing results.</p>
<h2>SQL, DataFrames, LogicalPlan Equivalence</h2>
diff --git a/blog/feeds/all-en.atom.xml b/blog/feeds/all-en.atom.xml
index a44f973..8586a7b 100644
--- a/blog/feeds/all-en.atom.xml
+++ b/blog/feeds/all-en.atom.xml
@@ -94,9 +94,10 @@ language such as Python, where you describe how to do the
computation as shown
in Figure 1.</p>
<p><img alt="Fig 1: Query Execution." class="img-responsive"
src="/blog/images/optimizing-sql-dataframes/query-execution.png"
width="80%"/></p>
<p><strong>Figure 1</strong>: Query Execution: Users
describe the answer they want using either
-a DataFrame or SQL. The query planner or DataFrame API translates that
-description into an <em>Initial Plan</em>, which is correct but
slow. The Query
-Optimizer then rewrites the initial plan to an <em>Optimized
Plan</em>, which computes
+SQL or a DataFrame. For SQL, a Query Planner translates the parsed query
+into an <em>initial plan</em>. The DataFrame API creates an
initial plan directly.
+The initial plan is correct, but slow. Then, the Query
+Optimizer rewrites the initial plan into an <em>optimized
plan</em>, which computes
the same results but faster and more efficiently. Finally, the Execution Engine
executes the optimized plan producing results.</p>
<h2>SQL, DataFrames, LogicalPlan Equivalence</h2>
diff --git a/blog/feeds/blog.atom.xml b/blog/feeds/blog.atom.xml
index b43a27c..65a6a23 100644
--- a/blog/feeds/blog.atom.xml
+++ b/blog/feeds/blog.atom.xml
@@ -94,9 +94,10 @@ language such as Python, where you describe how to do the
computation as shown
in Figure 1.</p>
<p><img alt="Fig 1: Query Execution." class="img-responsive"
src="/blog/images/optimizing-sql-dataframes/query-execution.png"
width="80%"/></p>
<p><strong>Figure 1</strong>: Query Execution: Users
describe the answer they want using either
-a DataFrame or SQL. The query planner or DataFrame API translates that
-description into an <em>Initial Plan</em>, which is correct but
slow. The Query
-Optimizer then rewrites the initial plan to an <em>Optimized
Plan</em>, which computes
+SQL or a DataFrame. For SQL, a Query Planner translates the parsed query
+into an <em>initial plan</em>. The DataFrame API creates an
initial plan directly.
+The initial plan is correct, but slow. Then, the Query
+Optimizer rewrites the initial plan into an <em>optimized
plan</em>, which computes
the same results but faster and more efficiently. Finally, the Execution Engine
executes the optimized plan producing results.</p>
<h2>SQL, DataFrames, LogicalPlan Equivalence</h2>
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