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new beebf63d25f [fix] Fix typo of Parallel Execution (#2314)
beebf63d25f is described below
commit beebf63d25f749d1d3c0da9e3dc0334ce435e4eb
Author: KassieZ <139741991+kass...@users.noreply.github.com>
AuthorDate: Wed Apr 23 11:01:18 2025 +0800
[fix] Fix typo of Parallel Execution (#2314)
## Versions
- [x] dev
- [x] 3.0
- [x] 2.1
- [ ] 2.0
## Languages
- [ ] Chinese
- [x] English
## Docs Checklist
- [ ] Checked by AI
- [ ] Test Cases Built
---
blog/why-apache-doris-is-best-alternatives-for-real-time-analytics.md | 2 +-
.../optimization-technology-principle/pipeline-execution-engine.md | 2 +-
.../optimization-technology-principle/pipeline-execution-engine.md | 2 +-
.../optimization-technology-principle/pipeline-execution-engine.md | 2 +-
4 files changed, 4 insertions(+), 4 deletions(-)
diff --git
a/blog/why-apache-doris-is-best-alternatives-for-real-time-analytics.md
b/blog/why-apache-doris-is-best-alternatives-for-real-time-analytics.md
index bf4be96caf9..86c4bc66007 100644
--- a/blog/why-apache-doris-is-best-alternatives-for-real-time-analytics.md
+++ b/blog/why-apache-doris-is-best-alternatives-for-real-time-analytics.md
@@ -5,7 +5,7 @@
'description': "Apache Doris is a real-time data warehouse commonly used
for observability, cyber security analysis, online reports, customer profiles,
data lakehouse and more. Elasticsearch is more like a search engine, but it is
also widely used for data analytics, so there's an overlap in their use cases.
The comparison in this post will focus on the real-time analytics capabilities
of Apache Doris and Elasticsearch from a user-oriented perspective",
'date': '2025-03-25',
'author': 'Kang, Apache Doris PMC Member',
- 'tags': ['Release Notes'],
+ 'tags': ['Tech Sharing'],
'picked': "true",
'order': "4",
"image": '/images/es-alternatives/Alternative-to-Elasticsearch.jpg'
diff --git
a/docs/query-acceleration/optimization-technology-principle/pipeline-execution-engine.md
b/docs/query-acceleration/optimization-technology-principle/pipeline-execution-engine.md
index 82f0c5988ff..80098cf179c 100644
---
a/docs/query-acceleration/optimization-technology-principle/pipeline-execution-engine.md
+++
b/docs/query-acceleration/optimization-technology-principle/pipeline-execution-engine.md
@@ -82,7 +82,7 @@ In most cases, each operator in a Pipeline corresponds to a
PlanNode, but there
* SortNode is split into SortSinkOperator and SortSourceOperator.
The basic principle is that for certain "breaking" operators (those that need
to collect all the data before performing computation), the data ingestion part
is split into a Sink, while the part that retrieves data from the operator is
referred to as the Source.
-## Scan 并行化
+## Parallel Scan
Scanning data is a very heavy I/O operation, as it requires reading large
amounts of data from local disks (or from HDFS or S3 in the case of data lake
scenarios, which introduces even longer latency), consuming a significant
amount of time. Therefore, we have introduced parallel scanning technology in
the ScanOperator. The ScanOperator dynamically generates multiple Scanners,
each of which scans around 1 to 2 million rows of data. While performing the
scan, each Scanner handles tasks su [...]
diff --git
a/versioned_docs/version-2.1/query-acceleration/optimization-technology-principle/pipeline-execution-engine.md
b/versioned_docs/version-2.1/query-acceleration/optimization-technology-principle/pipeline-execution-engine.md
index 82f0c5988ff..80098cf179c 100644
---
a/versioned_docs/version-2.1/query-acceleration/optimization-technology-principle/pipeline-execution-engine.md
+++
b/versioned_docs/version-2.1/query-acceleration/optimization-technology-principle/pipeline-execution-engine.md
@@ -82,7 +82,7 @@ In most cases, each operator in a Pipeline corresponds to a
PlanNode, but there
* SortNode is split into SortSinkOperator and SortSourceOperator.
The basic principle is that for certain "breaking" operators (those that need
to collect all the data before performing computation), the data ingestion part
is split into a Sink, while the part that retrieves data from the operator is
referred to as the Source.
-## Scan 并行化
+## Parallel Scan
Scanning data is a very heavy I/O operation, as it requires reading large
amounts of data from local disks (or from HDFS or S3 in the case of data lake
scenarios, which introduces even longer latency), consuming a significant
amount of time. Therefore, we have introduced parallel scanning technology in
the ScanOperator. The ScanOperator dynamically generates multiple Scanners,
each of which scans around 1 to 2 million rows of data. While performing the
scan, each Scanner handles tasks su [...]
diff --git
a/versioned_docs/version-3.0/query-acceleration/optimization-technology-principle/pipeline-execution-engine.md
b/versioned_docs/version-3.0/query-acceleration/optimization-technology-principle/pipeline-execution-engine.md
index 82f0c5988ff..80098cf179c 100644
---
a/versioned_docs/version-3.0/query-acceleration/optimization-technology-principle/pipeline-execution-engine.md
+++
b/versioned_docs/version-3.0/query-acceleration/optimization-technology-principle/pipeline-execution-engine.md
@@ -82,7 +82,7 @@ In most cases, each operator in a Pipeline corresponds to a
PlanNode, but there
* SortNode is split into SortSinkOperator and SortSourceOperator.
The basic principle is that for certain "breaking" operators (those that need
to collect all the data before performing computation), the data ingestion part
is split into a Sink, while the part that retrieves data from the operator is
referred to as the Source.
-## Scan 并行化
+## Parallel Scan
Scanning data is a very heavy I/O operation, as it requires reading large
amounts of data from local disks (or from HDFS or S3 in the case of data lake
scenarios, which introduces even longer latency), consuming a significant
amount of time. Therefore, we have introduced parallel scanning technology in
the ScanOperator. The ScanOperator dynamically generates multiple Scanners,
each of which scans around 1 to 2 million rows of data. While performing the
scan, each Scanner handles tasks su [...]
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