Omega359 commented on code in PR #58:
URL: https://github.com/apache/datafusion-site/pull/58#discussion_r1989499607
##########
content/blog/2025-03-11-ordering-analysis.md:
##########
@@ -0,0 +1,381 @@
+---
+layout: post
+title: Using Ordering for Better Plans in Apache DataFusion
+date: 2025-03-11
+author: Mustafa Akur, Andrew Lamb
+categories: [tutorial]
+---
+
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+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
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+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 %}
+-->
+
+<!-- see https://github.com/apache/datafusion/issues/11631 for details -->
+
+## Introduction
+In this blog post, we explain when an ordering requirement of an operator is
satisfied by its input data. This analysis is essential for order-based
optimizations and is often more complex than one might initially think.
+<blockquote style="border-left: 4px solid #007bff; padding: 10px;
background-color: #f8f9fa;">
+ <strong>Ordering Requirement</strong> for an operator describes how the
input data to that operator must be sorted for the operator to compute the
correct result. It is the job of the planner to make sure that these
requirements are satisfied during execution (See DataFusion <a
href="https://docs.rs/datafusion/latest/datafusion/physical_optimizer/enforce_sorting/struct.EnforceSorting.html";
target="_blank">EnforceSorting</a> for an implementation of such rule).
+</blockquote>
+
+There are various use cases, where this type of analysis can be useful such as
the following examples.
+### Removing Unnecessary Sorts
+Imagine a user wants to execute the following query:
+```SQL
+SELECT hostname, log_line
+FROM telemetry ORDER BY time ASC limit 10
+```
+If we don't know anything about the `telemetry` table, we need to sort it by
`time ASC` and then retrieve the first 10 rows to get the correct result.
However, if the table is already ordered by `time ASC`, we can simply retrieve
the first 10 rows. This approach executes much faster and uses less memory
compared to resorting the entire table, even when the [TopK] operator is used.
+
+[TopK]:
https://docs.rs/datafusion/latest/datafusion/physical_plan/struct.TopK.html
+
+In order to avoid the sort, the query optimizer must determine the data is
already sorted. For simple queries the analysis is straightforward, but it gets
complicated fast. For example, what if your data is sorted by `[hostname, time
ASC]` and your query is
+```sql
+SELECT hostname, log_line
+FROM telemetry WHERE hostname = 'app.example.com' ORDER BY time ASC;
+```
+In this case, a sort still isn't needed, but the analysis must reason about
the sortedness of the stream when it knows `hostname` has a single value.
+
+### Optimized Operator Implementations
+As another use case, some operators can utilize the ordering information to
change its underlying algorithm to execute more efficiently. Consider the
following query:
+```SQL
+SELECT COUNT(log_line)
+FROM telemetry GROUP BY hostname;
+```
+Most analytic systems, including DataFusion, by default implement such a query
using a hash table keyed on values of `hostname` to store the counts. However,
if the `telemetry` table is sorted by `hostname`, there are much more
efficient algorithms for grouping on `hostname` values than hashing every value
and storing it in memory. However, the more efficient algorithm can only be
used when the input is sorted correctly. To see this in practice, check out the
[source](https://github.com/apache/datafusion/tree/main/datafusion/physical-plan/src/aggregates/order)
for ordered variant of the `Aggregation` in `DataFusion`.
+
+### Streaming-Friendly Execution
+
+Stream processing aims to produce results immediately as they become
available, ensuring minimal latency for real-time workloads. However, some
operators need to consume all input data before producing any output. Consider
the `Sort` operation: before it can start generating output, the algorithm must
first process all input data. As a result, data flow halts whenever such an
operator is encountered until all input is consumed. When a physical query plan
contains such an operator (`Sort`, `CrossJoin`, ..), we refer to this as
pipeline breaking, meaning the query cannot be executed in a streaming fashion.
+
+For a query to be executed in a streaming fashion, we need to satisfy 2
conditions:
+
+**Logically Streamable**
+It should be possible to generate what user wants in streaming fashion.
Consider following query:
+
+```SQL
+SELECT SUM(amount)
+FROM orders
+```
+Here, the user wants to compute the sum of all amounts in the orders table. By
nature, this query requires scanning the entire table to generate a result,
making it impossible to execute in a streaming fashion.
+
+**Streaming Aware Planner**
+Being logically streamable does not guarantee that a query will execute in a
streaming fashion. SQL is a declarative language, meaning it specifies 'WHAT'
user wants. It is up to the planner, 'HOW' to generate the result. In most
cases, there are many ways to compute the correct result for a given query. The
query planner is responsible for choosing "a way" (ideally the best<sup
id="optimal1">[*](#optimal)</sup> one) among the all alternatives to generate
what user asks for. If a plan contains a pipeline-breaking operator, the
execution will not be streaming—even if the query is logically streamable. To
generate truly streaming plans from logically streamable queries, the planner
must carefully analyze the existing orderings in the source tables to ensure
that the final plan does not contain any pipeline-breaking operators.
+
+
+## Analysis
+Let's start by creating an example table that we will refer throughout the
post. This table models the input data of an operator for the analysis:
+
+### Example Virtual Table
+
+<style>
+ table {
+ border-collapse: collapse;
+ width: 80%;
+ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto,
Helvetica, Arial, sans-serif;
+ }
+ th, td {
+ padding: 12px 16px;
+ text-align: left;
+ border-bottom: 1px solid #e0e0e0;
+ }
+ th {
+ background-color: #f9f9f9;
+ font-weight: 600;
+ }
+ tr:hover {
+ background-color: #f1f1f1;
+ }
+</style>
+
+<table>
+ <tr>
+ <th>amount</th> <th>price</th>
<th>hostname</th><th>currency</th><th>time_bin</th> <th>time</th>
<th>price_cloned</th> <th>time_cloned</th>
+ </tr>
+ <tr>
+ <td>12</td> <td>25</td> <td>app.example.com</td> <td>USD</td>
<td>08:00:00</td> <td>08:01:30</td> <td>25</td> <td>08:01:30</td>
+ </tr>
+ <tr>
+ <td>12</td> <td>26</td> <td>app.example.com</td> <td>USD</td>
<td>08:00:00</td> <td>08:11:30</td> <td>26</td> <td>08:11:30</td>
+ </tr>
+ <tr>
+ <td>15</td> <td>30</td> <td>app.example.com</td> <td>USD</td>
<td>08:00:00</td> <td>08:41:30</td> <td>30</td> <td>08:41:30</td>
+ </tr>
+ <tr>
+ <td>15</td> <td>32</td> <td>app.example.com</td> <td>USD</td>
<td>08:00:00</td> <td>08:55:15</td> <td>32</td> <td>08:55:15</td>
+ </tr>
+ <tr>
+ <td>15</td> <td>35</td> <td>app.example.com</td> <td>USD</td>
<td>09:00:00</td> <td>09:10:23</td> <td>35</td> <td>09:10:23</td>
+ </tr>
+ <tr>
+ <td>20</td> <td>18</td> <td>app.example.com</td> <td>USD</td>
<td>09:00:00</td> <td>09:20:33</td> <td>18</td> <td>09:20:33</td>
+ </tr>
+ <tr>
+ <td>20</td> <td>22</td> <td>app.example.com</td> <td>USD</td>
<td>09:00:00</td> <td>09:40:15</td> <td>22</td> <td>09:40:15</td>
+ </tr>
+</table>
+
+<br>
+
+<blockquote style="border-left: 4px solid #007bff; padding: 10px;
background-color: #f8f9fa;">
+<strong>How can a table have multiple orderings?:</strong> At first glance, it
may seem counterintuitive for a table to have more than one valid ordering.
However, during query execution, such scenarios can arise.
+
+For example, consider the following query:
+
+```sql
+SELECT time, date_bin('1 hour', time, '1970-01-01') as time_bin
+FROM table;
+```
+If we know that the table is ordered by <code>time ASC</code>, we can infer
that <code>time_bin ASC</code> is also a valid ordering. This is because the
<code>date_bin</code> function is monotonic, meaning it preserves the order of
its input.
+
+DataFusion leverages these functional dependencies to infer new orderings as
data flows through different query operators. For details on the
implementation, see the <a
href="https://github.com/apache/datafusion/blob/main/datafusion/common/src/functional_dependencies.rs";,
target="_blank">source</a> code.
+</blockquote>
+
+By inspection, you can see this table is sorted by the `amount` column, but It
is also sorted by `time` and `time_bin` as well as the compound `(time_bin,
amount)` and many other variations. While this example is an extreme case, many
real world data have multiple sort orders.
+
+A naive approach for analyzing whether the ordering requirement of an operator
is satisfied by its input would be:
+
+ - Store all the valid ordering expressions that the tables satisfies
+ - Check whether the ordering requirement by the operator is among valid
orderings.
+
+This naive algorithm works and correct. However, listing all valid orderings
can be quite lengthy and is of exponential complexity as the number of
orderings grows. For the example table, here is a (small) subset of the valid
orderings:
+
+`[amount ASC]`
+`[amount ASC, price ASC]`
+`[amount ASC, price_cloned ASC]`
+`[hostname ASC, amount ASC, price_cloned ASC]`
+`[amount ASC, hostname ASC, price_cloned ASC]`
+`[amount ASC, price_cloned ASC, hostname ASC]`
+.
+.
+.
+
+As can be seen from the listing above. Storing all valid orderings is
wasteful, and contains significant redundancy. Here are some observations,
suggesting we can do much better:
+
+
+- Storing a prefix of another valid ordering is redundant. If the table
satisfies the lexicographic ordering<sup id="fn1">[1](#footnote1)</sup>:
`[amount ASC, price ASC]`, it already satisfies ordering `[amount ASC]`
trivially. Hence, once we store `[amount ASC, price ASC]` storing `[amount
ASC]` is rdundant.
+
+- Using all columns that are equal to each other in the listings is redundant.
If we know the table is ordered by `[amount ASC, price ASC]` , it is also
ordered by `[amount ASC, price_cloned ASC]` since `price` and `price_cloned`
are copy of each other. It is enough to use just one expression among the
expressions that exact copy of each other.
+
+- Constant expressions can be inserted anywhere in a valid ordering with an
arbitrary direction (e.g. `ASC`, `DESC`). Hence, if the table is ordered by
`[amount ASC, price ASC]`, it is also ordered by: <br>
+ `[hostname ASC, amount ASC, price ASC]`,
+ `[hostname DESC, amount ASC, price ASC]`,
+ `[amount ASC, hostname ASC, price ASC]`,
+ .
+ .
+
+This is clearly redundant. For this reason, it is better to avoid explicitly
encoding constant expressions in valid sort orders.
+
+In summary,
+
+- We should store only the longest lexicographic ordering (shouldn't use any
prefix of it)
+- Using expressions that are exact copies of each other is redundant.
+- Ordering expressions shouldn't contain any constant expression.
+
+
+## Key Concepts for Analyzing Orderings
+To solve the shortcomings above, DataFusion needs to track of following
properties for the table:
+
+- Constant Expressions
+- Equivalent Expression Groups (will be explained shortly)
+- Succinct Valid Orderings (will be explained shortly)
+
+<blockquote style="border-left: 4px solid #007bff; padding: 10px;
background-color: #f8f9fa;">
+ <strong>Note:</strong> These properties are implemented in the
<code>EquivalenceProperties</code> structure in <code>DataFusion</code>, please
see the <a
href="https://github.com/apache/datafusion/blob/f47ea73b87eec4af044f9b9923baf042682615b2/datafusion/physical-expr/src/equivalence/properties/mod.rs#L134";
target="_blank">source</a> for more details<br>
+</blockquote>
+
+These properties allow us to analyze whether the ordering requirement is
satisfied by the data already.
+
+### 1. Constant Expressions
+Constant expressions are those where each row in the expression has the same
value across all rows. Although constant expressions may seem odd in a table,
they arise after operations like `Filter` or `Join`.
+
+For instance in the example table:
+
+- Columns `hostname` and `currency` are constant because every row in the
table has the same value (`'app.example.com'` for `hostname`, and `'USD'` for
`currency`) for these columns.
+
+<blockquote style="border-left: 4px solid #007bff; padding: 10px;
background-color: #f8f9fa;">
+ <strong>Note:</strong> Constant expressions can arise during query
execution. For example, in following query:<br>
+ <code>SELECT hostname FROM logs</code><br><code>WHERE
hostname='app.example.com'</code> <br>
+ after filtering is done, for subsequent operators the
<code>hostname</code> column will be constant.
+</blockquote>
+
+### 2. Equivalent Expression Groups
+Equivalent expression groups are expressions that always hold the same value
across rows. These expressions can be thought of as clones of each other and
may arise from operations like `Filter`, `Join`, or `Projection`.
+
+In the example table, the expressions `price` and `price_cloned` form one
equivalence group, and `time` and `time_cloned` form another equivalence group.
+
+<blockquote style="border-left: 4px solid #007bff; padding: 10px;
background-color: #f8f9fa;">
+ <strong>Note:</strong> Equivalent expression groups can arise during the
query execution. For example, in the following query:<br>
+ <code>SELECT time, time as time_cloned FROM logs</code> <br>
+ after the projection is done, for subsequent operators <code>time</code>
and <code>time_cloned</code> will form an equivalence group. As another
example, in the following query:<br>
+ <code>SELECT employees.id, employees.name,
departments.department_name</code>
+<code>FROM employees</code>
+<code>JOIN departments ON employees.department_id = departments.id;</code> <br>
+after joining, <code>employees.department_id</code> and
<code>departments.id</code> will form an equivalence group.
+</blockquote>
+
+### 3. Succinct Encoding of Valid Orderings
+Valid orderings are the orderings that the table already satisfies. However,
naively listing them requires exponential space as the number of columns grows
as discussed before. Instead, we list all valid orderings after following
constraints are applied:
+
+- Do not use any constant expressions in the valid ordering construction
+- Use only one entry (by convention the first entry) in the equivalent
expression group.
+- Lexicographic ordering shouldn't contain any leading ordering<sup
id="fn2">[2](#footnote2)</sup>except the first position <sup
id="fn3">[3](#footnote3)</sup>.
+- Do not use any prefix of a valid lexicographic ordering<sup
id="fn4">[4](#footnote4)</sup>.
+
+After applying the first and second constraint, example table simplifies to
+
+<style>
+ table {
+ border-collapse: collapse;
+ width: 80%;
+ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto,
Helvetica, Arial, sans-serif;
+ }
+ th, td {
+ padding: 12px 16px;
+ text-align: left;
+ border-bottom: 1px solid #e0e0e0;
+ }
+ th {
+ background-color: #f9f9f9;
+ font-weight: 600;
+ }
+ tr:hover {
+ background-color: #f1f1f1;
+ }
+</style>
+
+<table>
+ <tr>
+ <th>amount</th> <th>price</th><th>time_bin</th> <th>time</th>
+ </tr>
+ <tr>
+ <td>12</td> <td>25</td><td>08:00:00</td> <td>08:01:30</td>
+ </tr>
+ <tr>
+ <td>12</td> <td>26</td><td>08:00:00</td> <td>08:11:30</td>
+ </tr>
+ <tr>
+ <td>15</td> <td>30</td><td>08:00:00</td> <td>08:41:30</td>
+ </tr>
+ <tr>
+ <td>15</td> <td>32</td><td>08:00:00</td> <td>08:55:15</td>
+ </tr>
+ <tr>
+ <td>15</td> <td>35</td><td>09:00:00</td> <td>09:10:23</td>
+ </tr>
+ <tr>
+ <td>20</td> <td>18</td><td>09:00:00</td> <td>09:20:33</td>
+ </tr>
+ <tr>
+ <td>20</td> <td>22</td><td>09:00:00</td> <td>09:40:15</td>
+ </tr>
+</table>
+<br>
+Following third and fourth constraints for the simplified table, the succinct
valid orderings are:<br>
+`[amount ASC, price ASC]`,
+`[time_bin ASC]`,
+`[time ASC]`
+
+### Table Properties
+
+In summary, for the example table, the following properties correctly describe
the sort properties:
+
+- **Constant Expressions** = `hostname, currency`
+- **Equivalent Expression Groups** = `[price, price_cloned], [time,
time_cloned]`
+- **Valid Orderings** = `[amount ASC, price ASC], [time_bin ASC], [time ASC]`
+
+### Algorithm for Analyzing Ordering Requirements
+
+After deriving these properties for the data, following algorithm can be used
to check whether an ordering requirement is satisfied by the table:
+
+1. **Prune constant expressions**: Remove any constant expressions from the
ordering requirement.
+2. **Normalize the requirement**: Replace each expression in the ordering
requirement with the first entry from its equivalence group.
+3. **De-duplicate expressions**: If an expression appears more than once,
remove duplicates, keeping only the first occurrence.
+4. **Match leading orderings**: Check whether the leading ordering
requirement<sup id="fn5">[5](#footnote5)</sup> matches the leading valid
orderings<sup id="fn6">[6](#footnote6)</sup> of table. If so:
+ - Remove the leading ordering requirement from the ordering requirement
+ - Remove the matching leading valid ordering from the valid orderings of
table.
+5. **Iterate through the remaining expressions**: Go back to step 4 until
ordering requirement is empty or leading ordering requirement is not found
among the leading valid orderings of table.
+
+If, at the end of the procedure above, the ordering requirement is an empty
list, we can conclude that the requirement is satisfied by the table.
+
+### Example Walkthrough
+
+Let's say the user provided a query such as the following
+
+```sql
+SELECT * FROM table
+ORDER BY hostname DESC, amount ASC, time_bin ASC, price_cloned ASC, time ASC,
currency ASC, price DESC;
+```
+And the input has the same properties explained above
+
+- **Constant Expressions** = `hostname, currency`
+- **Equivalent Expressions Groups** = `[price, price_cloned], [time,
time_cloned]`
+- **Succinct Valid Orderings** = `[amount ASC, price ASC], [time_bin ASC],
[time ASC]`
+
+In order to remove a sort, the optimizer must check if the ordering
requirement `[hostname DESC, amount ASC, time_bin ASC, price_cloned ASC, time
ASC, currency ASC, price DESC]` is satisfied by the properties.
+
+### Algorithm Steps
+
+1. **Prune constant expressions**:
+ Remove `hostname` and `currency` from the requirement. The requirement
becomes:
+ `[amount ASC, time_bin ASC, price_cloned ASC, time ASC, price DESC]`.
+
+2. **Normalize using equivalent groups**:
+ Replace `price_cloned` with `price` and `time_cloned` with `time`. The
requirement becomes:
+ `[amount ASC, time_bin ASC, price ASC, time ASC, price DESC]`.
+
+3. **De-duplicate expressions**:
+ Since `price` appears twice, we simplify the requirement to:
+ `[amount ASC, time_bin ASC, price ASC, time ASC]` (keeping the first
occurrence from the left side).
+
+4. **Match leading orderings**:
+ Check if leading ordering requirement `amount ASC` is among the leading
valid orderings: `amount ASC, time_bin ASC, time ASC`. Since this is the case,
we remove `amount ASC` from both the ordering requirement and the valid
orderings of the table.
+5. **Iterate through the remaining expressions**:
+Now, the problem is converted from<br>
+*"whether the requirement: `[amount ASC, time_bin ASC, price ASC, time ASC]`
is satisfied by valid orderings: `[amount ASC, price ASC], [time_bin ASC],
[time ASC]`"*<br>
+into<br>
+*"whether the requirement: `[time_bin ASC, price ASC, time ASC]` is satisfied
by valid orderings: `[price ASC], [time_bin ASC], [time ASC]`"*<br>
+We go back to step 4 until the ordering requirement list is exhausted or its
length no longer decreases.
+
+At the end of stages above, we end up with an empty ordering requirement list.
Given this, we can conclude that the table satisfies the ordering requirement
and thus no sort is required.
Review Comment:
```suggestion
At the end of stages above we end up with an empty ordering requirement
list. Given this, we can conclude that the table satisfies the ordering
requirement and thus no sort is required.
```
##########
content/blog/2025-03-11-ordering-analysis.md:
##########
@@ -0,0 +1,381 @@
+---
+layout: post
+title: Using Ordering for Better Plans in Apache DataFusion
+date: 2025-03-11
+author: Mustafa Akur, Andrew Lamb
+categories: [tutorial]
+---
+
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+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
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+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 %}
+-->
+
+<!-- see https://github.com/apache/datafusion/issues/11631 for details -->
+
+## Introduction
+In this blog post, we explain when an ordering requirement of an operator is
satisfied by its input data. This analysis is essential for order-based
optimizations and is often more complex than one might initially think.
+<blockquote style="border-left: 4px solid #007bff; padding: 10px;
background-color: #f8f9fa;">
+ <strong>Ordering Requirement</strong> for an operator describes how the
input data to that operator must be sorted for the operator to compute the
correct result. It is the job of the planner to make sure that these
requirements are satisfied during execution (See DataFusion <a
href="https://docs.rs/datafusion/latest/datafusion/physical_optimizer/enforce_sorting/struct.EnforceSorting.html";
target="_blank">EnforceSorting</a> for an implementation of such rule).
+</blockquote>
+
+There are various use cases, where this type of analysis can be useful such as
the following examples.
+### Removing Unnecessary Sorts
+Imagine a user wants to execute the following query:
+```SQL
+SELECT hostname, log_line
+FROM telemetry ORDER BY time ASC limit 10
+```
+If we don't know anything about the `telemetry` table, we need to sort it by
`time ASC` and then retrieve the first 10 rows to get the correct result.
However, if the table is already ordered by `time ASC`, we can simply retrieve
the first 10 rows. This approach executes much faster and uses less memory
compared to resorting the entire table, even when the [TopK] operator is used.
+
+[TopK]:
https://docs.rs/datafusion/latest/datafusion/physical_plan/struct.TopK.html
+
+In order to avoid the sort, the query optimizer must determine the data is
already sorted. For simple queries the analysis is straightforward, but it gets
complicated fast. For example, what if your data is sorted by `[hostname, time
ASC]` and your query is
+```sql
+SELECT hostname, log_line
+FROM telemetry WHERE hostname = 'app.example.com' ORDER BY time ASC;
+```
+In this case, a sort still isn't needed, but the analysis must reason about
the sortedness of the stream when it knows `hostname` has a single value.
+
+### Optimized Operator Implementations
+As another use case, some operators can utilize the ordering information to
change its underlying algorithm to execute more efficiently. Consider the
following query:
+```SQL
+SELECT COUNT(log_line)
+FROM telemetry GROUP BY hostname;
+```
+Most analytic systems, including DataFusion, by default implement such a query
using a hash table keyed on values of `hostname` to store the counts. However,
if the `telemetry` table is sorted by `hostname`, there are much more
efficient algorithms for grouping on `hostname` values than hashing every value
and storing it in memory. However, the more efficient algorithm can only be
used when the input is sorted correctly. To see this in practice, check out the
[source](https://github.com/apache/datafusion/tree/main/datafusion/physical-plan/src/aggregates/order)
for ordered variant of the `Aggregation` in `DataFusion`.
+
+### Streaming-Friendly Execution
+
+Stream processing aims to produce results immediately as they become
available, ensuring minimal latency for real-time workloads. However, some
operators need to consume all input data before producing any output. Consider
the `Sort` operation: before it can start generating output, the algorithm must
first process all input data. As a result, data flow halts whenever such an
operator is encountered until all input is consumed. When a physical query plan
contains such an operator (`Sort`, `CrossJoin`, ..), we refer to this as
pipeline breaking, meaning the query cannot be executed in a streaming fashion.
+
+For a query to be executed in a streaming fashion, we need to satisfy 2
conditions:
+
+**Logically Streamable**
+It should be possible to generate what user wants in streaming fashion.
Consider following query:
+
+```SQL
+SELECT SUM(amount)
+FROM orders
+```
+Here, the user wants to compute the sum of all amounts in the orders table. By
nature, this query requires scanning the entire table to generate a result,
making it impossible to execute in a streaming fashion.
+
+**Streaming Aware Planner**
+Being logically streamable does not guarantee that a query will execute in a
streaming fashion. SQL is a declarative language, meaning it specifies 'WHAT'
user wants. It is up to the planner, 'HOW' to generate the result. In most
cases, there are many ways to compute the correct result for a given query. The
query planner is responsible for choosing "a way" (ideally the best<sup
id="optimal1">[*](#optimal)</sup> one) among the all alternatives to generate
what user asks for. If a plan contains a pipeline-breaking operator, the
execution will not be streaming—even if the query is logically streamable. To
generate truly streaming plans from logically streamable queries, the planner
must carefully analyze the existing orderings in the source tables to ensure
that the final plan does not contain any pipeline-breaking operators.
+
+
+## Analysis
+Let's start by creating an example table that we will refer throughout the
post. This table models the input data of an operator for the analysis:
+
+### Example Virtual Table
+
+<style>
+ table {
+ border-collapse: collapse;
+ width: 80%;
+ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto,
Helvetica, Arial, sans-serif;
+ }
+ th, td {
+ padding: 12px 16px;
+ text-align: left;
+ border-bottom: 1px solid #e0e0e0;
+ }
+ th {
+ background-color: #f9f9f9;
+ font-weight: 600;
+ }
+ tr:hover {
+ background-color: #f1f1f1;
+ }
+</style>
+
+<table>
+ <tr>
+ <th>amount</th> <th>price</th>
<th>hostname</th><th>currency</th><th>time_bin</th> <th>time</th>
<th>price_cloned</th> <th>time_cloned</th>
+ </tr>
+ <tr>
+ <td>12</td> <td>25</td> <td>app.example.com</td> <td>USD</td>
<td>08:00:00</td> <td>08:01:30</td> <td>25</td> <td>08:01:30</td>
+ </tr>
+ <tr>
+ <td>12</td> <td>26</td> <td>app.example.com</td> <td>USD</td>
<td>08:00:00</td> <td>08:11:30</td> <td>26</td> <td>08:11:30</td>
+ </tr>
+ <tr>
+ <td>15</td> <td>30</td> <td>app.example.com</td> <td>USD</td>
<td>08:00:00</td> <td>08:41:30</td> <td>30</td> <td>08:41:30</td>
+ </tr>
+ <tr>
+ <td>15</td> <td>32</td> <td>app.example.com</td> <td>USD</td>
<td>08:00:00</td> <td>08:55:15</td> <td>32</td> <td>08:55:15</td>
+ </tr>
+ <tr>
+ <td>15</td> <td>35</td> <td>app.example.com</td> <td>USD</td>
<td>09:00:00</td> <td>09:10:23</td> <td>35</td> <td>09:10:23</td>
+ </tr>
+ <tr>
+ <td>20</td> <td>18</td> <td>app.example.com</td> <td>USD</td>
<td>09:00:00</td> <td>09:20:33</td> <td>18</td> <td>09:20:33</td>
+ </tr>
+ <tr>
+ <td>20</td> <td>22</td> <td>app.example.com</td> <td>USD</td>
<td>09:00:00</td> <td>09:40:15</td> <td>22</td> <td>09:40:15</td>
+ </tr>
+</table>
+
+<br>
+
+<blockquote style="border-left: 4px solid #007bff; padding: 10px;
background-color: #f8f9fa;">
+<strong>How can a table have multiple orderings?:</strong> At first glance, it
may seem counterintuitive for a table to have more than one valid ordering.
However, during query execution, such scenarios can arise.
+
+For example, consider the following query:
+
+```sql
+SELECT time, date_bin('1 hour', time, '1970-01-01') as time_bin
+FROM table;
+```
+If we know that the table is ordered by <code>time ASC</code>, we can infer
that <code>time_bin ASC</code> is also a valid ordering. This is because the
<code>date_bin</code> function is monotonic, meaning it preserves the order of
its input.
+
+DataFusion leverages these functional dependencies to infer new orderings as
data flows through different query operators. For details on the
implementation, see the <a
href="https://github.com/apache/datafusion/blob/main/datafusion/common/src/functional_dependencies.rs";,
target="_blank">source</a> code.
+</blockquote>
+
+By inspection, you can see this table is sorted by the `amount` column, but It
is also sorted by `time` and `time_bin` as well as the compound `(time_bin,
amount)` and many other variations. While this example is an extreme case, many
real world data have multiple sort orders.
+
+A naive approach for analyzing whether the ordering requirement of an operator
is satisfied by its input would be:
+
+ - Store all the valid ordering expressions that the tables satisfies
+ - Check whether the ordering requirement by the operator is among valid
orderings.
+
+This naive algorithm works and correct. However, listing all valid orderings
can be quite lengthy and is of exponential complexity as the number of
orderings grows. For the example table, here is a (small) subset of the valid
orderings:
+
+`[amount ASC]`
+`[amount ASC, price ASC]`
+`[amount ASC, price_cloned ASC]`
+`[hostname ASC, amount ASC, price_cloned ASC]`
+`[amount ASC, hostname ASC, price_cloned ASC]`
+`[amount ASC, price_cloned ASC, hostname ASC]`
+.
+.
+.
+
+As can be seen from the listing above. Storing all valid orderings is
wasteful, and contains significant redundancy. Here are some observations,
suggesting we can do much better:
+
+
+- Storing a prefix of another valid ordering is redundant. If the table
satisfies the lexicographic ordering<sup id="fn1">[1](#footnote1)</sup>:
`[amount ASC, price ASC]`, it already satisfies ordering `[amount ASC]`
trivially. Hence, once we store `[amount ASC, price ASC]` storing `[amount
ASC]` is rdundant.
+
+- Using all columns that are equal to each other in the listings is redundant.
If we know the table is ordered by `[amount ASC, price ASC]` , it is also
ordered by `[amount ASC, price_cloned ASC]` since `price` and `price_cloned`
are copy of each other. It is enough to use just one expression among the
expressions that exact copy of each other.
+
+- Constant expressions can be inserted anywhere in a valid ordering with an
arbitrary direction (e.g. `ASC`, `DESC`). Hence, if the table is ordered by
`[amount ASC, price ASC]`, it is also ordered by: <br>
+ `[hostname ASC, amount ASC, price ASC]`,
+ `[hostname DESC, amount ASC, price ASC]`,
+ `[amount ASC, hostname ASC, price ASC]`,
+ .
+ .
+
+This is clearly redundant. For this reason, it is better to avoid explicitly
encoding constant expressions in valid sort orders.
+
+In summary,
+
+- We should store only the longest lexicographic ordering (shouldn't use any
prefix of it)
+- Using expressions that are exact copies of each other is redundant.
+- Ordering expressions shouldn't contain any constant expression.
+
+
+## Key Concepts for Analyzing Orderings
+To solve the shortcomings above, DataFusion needs to track of following
properties for the table:
+
+- Constant Expressions
+- Equivalent Expression Groups (will be explained shortly)
+- Succinct Valid Orderings (will be explained shortly)
+
+<blockquote style="border-left: 4px solid #007bff; padding: 10px;
background-color: #f8f9fa;">
+ <strong>Note:</strong> These properties are implemented in the
<code>EquivalenceProperties</code> structure in <code>DataFusion</code>, please
see the <a
href="https://github.com/apache/datafusion/blob/f47ea73b87eec4af044f9b9923baf042682615b2/datafusion/physical-expr/src/equivalence/properties/mod.rs#L134";
target="_blank">source</a> for more details<br>
+</blockquote>
+
+These properties allow us to analyze whether the ordering requirement is
satisfied by the data already.
+
+### 1. Constant Expressions
+Constant expressions are those where each row in the expression has the same
value across all rows. Although constant expressions may seem odd in a table,
they arise after operations like `Filter` or `Join`.
+
+For instance in the example table:
+
+- Columns `hostname` and `currency` are constant because every row in the
table has the same value (`'app.example.com'` for `hostname`, and `'USD'` for
`currency`) for these columns.
+
+<blockquote style="border-left: 4px solid #007bff; padding: 10px;
background-color: #f8f9fa;">
+ <strong>Note:</strong> Constant expressions can arise during query
execution. For example, in following query:<br>
+ <code>SELECT hostname FROM logs</code><br><code>WHERE
hostname='app.example.com'</code> <br>
+ after filtering is done, for subsequent operators the
<code>hostname</code> column will be constant.
+</blockquote>
+
+### 2. Equivalent Expression Groups
+Equivalent expression groups are expressions that always hold the same value
across rows. These expressions can be thought of as clones of each other and
may arise from operations like `Filter`, `Join`, or `Projection`.
+
+In the example table, the expressions `price` and `price_cloned` form one
equivalence group, and `time` and `time_cloned` form another equivalence group.
+
+<blockquote style="border-left: 4px solid #007bff; padding: 10px;
background-color: #f8f9fa;">
+ <strong>Note:</strong> Equivalent expression groups can arise during the
query execution. For example, in the following query:<br>
+ <code>SELECT time, time as time_cloned FROM logs</code> <br>
+ after the projection is done, for subsequent operators <code>time</code>
and <code>time_cloned</code> will form an equivalence group. As another
example, in the following query:<br>
+ <code>SELECT employees.id, employees.name,
departments.department_name</code>
+<code>FROM employees</code>
+<code>JOIN departments ON employees.department_id = departments.id;</code> <br>
+after joining, <code>employees.department_id</code> and
<code>departments.id</code> will form an equivalence group.
+</blockquote>
+
+### 3. Succinct Encoding of Valid Orderings
+Valid orderings are the orderings that the table already satisfies. However,
naively listing them requires exponential space as the number of columns grows
as discussed before. Instead, we list all valid orderings after following
constraints are applied:
+
+- Do not use any constant expressions in the valid ordering construction
+- Use only one entry (by convention the first entry) in the equivalent
expression group.
+- Lexicographic ordering shouldn't contain any leading ordering<sup
id="fn2">[2](#footnote2)</sup>except the first position <sup
id="fn3">[3](#footnote3)</sup>.
+- Do not use any prefix of a valid lexicographic ordering<sup
id="fn4">[4](#footnote4)</sup>.
+
+After applying the first and second constraint, example table simplifies to
+
+<style>
+ table {
+ border-collapse: collapse;
+ width: 80%;
+ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto,
Helvetica, Arial, sans-serif;
+ }
+ th, td {
+ padding: 12px 16px;
+ text-align: left;
+ border-bottom: 1px solid #e0e0e0;
+ }
+ th {
+ background-color: #f9f9f9;
+ font-weight: 600;
+ }
+ tr:hover {
+ background-color: #f1f1f1;
+ }
+</style>
+
+<table>
+ <tr>
+ <th>amount</th> <th>price</th><th>time_bin</th> <th>time</th>
+ </tr>
+ <tr>
+ <td>12</td> <td>25</td><td>08:00:00</td> <td>08:01:30</td>
+ </tr>
+ <tr>
+ <td>12</td> <td>26</td><td>08:00:00</td> <td>08:11:30</td>
+ </tr>
+ <tr>
+ <td>15</td> <td>30</td><td>08:00:00</td> <td>08:41:30</td>
+ </tr>
+ <tr>
+ <td>15</td> <td>32</td><td>08:00:00</td> <td>08:55:15</td>
+ </tr>
+ <tr>
+ <td>15</td> <td>35</td><td>09:00:00</td> <td>09:10:23</td>
+ </tr>
+ <tr>
+ <td>20</td> <td>18</td><td>09:00:00</td> <td>09:20:33</td>
+ </tr>
+ <tr>
+ <td>20</td> <td>22</td><td>09:00:00</td> <td>09:40:15</td>
+ </tr>
+</table>
+<br>
+Following third and fourth constraints for the simplified table, the succinct
valid orderings are:<br>
+`[amount ASC, price ASC]`,
+`[time_bin ASC]`,
+`[time ASC]`
+
+### Table Properties
+
+In summary, for the example table, the following properties correctly describe
the sort properties:
+
+- **Constant Expressions** = `hostname, currency`
+- **Equivalent Expression Groups** = `[price, price_cloned], [time,
time_cloned]`
+- **Valid Orderings** = `[amount ASC, price ASC], [time_bin ASC], [time ASC]`
+
+### Algorithm for Analyzing Ordering Requirements
+
+After deriving these properties for the data, following algorithm can be used
to check whether an ordering requirement is satisfied by the table:
+
+1. **Prune constant expressions**: Remove any constant expressions from the
ordering requirement.
+2. **Normalize the requirement**: Replace each expression in the ordering
requirement with the first entry from its equivalence group.
+3. **De-duplicate expressions**: If an expression appears more than once,
remove duplicates, keeping only the first occurrence.
+4. **Match leading orderings**: Check whether the leading ordering
requirement<sup id="fn5">[5](#footnote5)</sup> matches the leading valid
orderings<sup id="fn6">[6](#footnote6)</sup> of table. If so:
+ - Remove the leading ordering requirement from the ordering requirement
+ - Remove the matching leading valid ordering from the valid orderings of
table.
+5. **Iterate through the remaining expressions**: Go back to step 4 until
ordering requirement is empty or leading ordering requirement is not found
among the leading valid orderings of table.
+
+If, at the end of the procedure above, the ordering requirement is an empty
list, we can conclude that the requirement is satisfied by the table.
+
+### Example Walkthrough
+
+Let's say the user provided a query such as the following
+
+```sql
+SELECT * FROM table
+ORDER BY hostname DESC, amount ASC, time_bin ASC, price_cloned ASC, time ASC,
currency ASC, price DESC;
+```
+And the input has the same properties explained above
+
+- **Constant Expressions** = `hostname, currency`
+- **Equivalent Expressions Groups** = `[price, price_cloned], [time,
time_cloned]`
+- **Succinct Valid Orderings** = `[amount ASC, price ASC], [time_bin ASC],
[time ASC]`
+
+In order to remove a sort, the optimizer must check if the ordering
requirement `[hostname DESC, amount ASC, time_bin ASC, price_cloned ASC, time
ASC, currency ASC, price DESC]` is satisfied by the properties.
Review Comment:
```suggestion
In order to remove a sort the optimizer must check if the ordering
requirement `[hostname DESC, amount ASC, time_bin ASC, price_cloned ASC, time
ASC, currency ASC, price DESC]` is satisfied by the properties.
```
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: github-unsubscr...@datafusion.apache.org
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: github-unsubscr...@datafusion.apache.org
For additional commands, e-mail: github-h...@datafusion.apache.org
