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The following commit(s) were added to refs/heads/master by this push:
new ca699ff7a05 coffeebench-1-blog-upload (#2931)
ca699ff7a05 is described below
commit ca699ff7a05ff70a6b1368d3ca4267e87f76fbc8
Author: Qin Chen <[email protected]>
AuthorDate: Mon Sep 29 17:15:26 2025 +0800
coffeebench-1-blog-upload (#2931)
## Versions
- [ ] dev
- [ ] 3.0
- [ ] 2.1
- [ ] 2.0
## Languages
- [ ] Chinese
- [ ] English
## Docs Checklist
- [ ] Checked by AI
- [ ] Test Cases Built
---
...d-iceberg-building-hyperscale-data-lakehouse.md | 2 --
blog/coffeebench-olap-showdown-part1-250829.md | 14 ++++++++
blog/coffeebench-part2-250917.md | 14 ++++++++
blog/data-pruning-250908.md | 12 +++++++
blog/data-trait-250905.md | 14 ++++++++
...-at-scale-integrating-apache-flink-and-doris.md | 2 --
...se-in-cainiao-large-scale-business-scenarios.md | 2 --
blog/rtabench-250902.md | 14 ++++++++
...iaomi-achieves-6\303\227-faster-performance.md" | 2 --
src/components/recent-blogs/recent-blogs.data.ts | 16 ++++-----
src/constant/newsletter.data.ts | 38 ++++++++++-----------
.../coffeebench-olap-showdown-part1-250829.PNG | Bin 0 -> 589798 bytes
static/images/blogs/coffeebench-part2-250917.jpeg | Bin 0 -> 303704 bytes
static/images/blogs/data-pruning-250905.PNG | Bin 0 -> 110427 bytes
static/images/blogs/data-trait-250908.PNG | Bin 0 -> 146128 bytes
static/images/blogs/rtabench-250902.JPEG | Bin 0 -> 44543 bytes
16 files changed, 95 insertions(+), 35 deletions(-)
diff --git
a/blog/apache-doris-and-iceberg-building-hyperscale-data-lakehouse.md
b/blog/apache-doris-and-iceberg-building-hyperscale-data-lakehouse.md
index a6dae21312c..e3911d31995 100644
--- a/blog/apache-doris-and-iceberg-building-hyperscale-data-lakehouse.md
+++ b/blog/apache-doris-and-iceberg-building-hyperscale-data-lakehouse.md
@@ -3,8 +3,6 @@
'title': 'Leading Cloud Computing Service Provider Chose Apache Doris +
Iceberg for Hyperscale Data Lakehouse',
'summary': "The world's cloud computing service giant chose Apache Doris +
Apache Iceberg to upgrade its data platform into a flexible, efficient data
lakehouse with low costs. This solution handles reporting and BI, federated
analysis, log storage and analysis, and high-concurrency analysis. With Apache
Doris, this company has successfully launched 20+ projects with 50+ clusters,
3000+ nodes, and over 15 petabytes of data.",
'description': "The world's cloud computing service giant chose Apache
Doris + Apache Iceberg to upgrade its data platform into a flexible, efficient
data lakehouse with low costs. This solution handles reporting and BI,
federated analysis, log storage and analysis, and high-concurrency analysis.
With Apache Doris, this company has successfully launched 20+ projects with 50+
clusters, 3000+ nodes, and over 15 petabytes of data.",
- 'picked': "true",
- 'order': "4",
'date': '2025-08-14',
'author': 'velodb.io · VeloDB Engineering Team',
'externalLink': 'https://www.velodb.io/blog/1450',
diff --git a/blog/coffeebench-olap-showdown-part1-250829.md
b/blog/coffeebench-olap-showdown-part1-250829.md
new file mode 100644
index 00000000000..a876311bb57
--- /dev/null
+++ b/blog/coffeebench-olap-showdown-part1-250829.md
@@ -0,0 +1,14 @@
+---
+{
+ 'title': 'The Ultimate OLAP Showdown: Apache Doris vs. ClickHouse vs.
Snowflake (Part 1)',
+ 'summary': "Apache Doris consistently delivers significantly faster
performance in large-scale benchmarks spanning both straightforward JOINs and
production-grade TPC-H/TPC-DS workloads. On top of that, Apache Doris requires
just 10%-20% of the cost of Snowflake or ClickHouse for OLAP workloads.",
+ 'description': "Apache Doris consistently delivers significantly faster
performance in large-scale benchmarks spanning both straightforward JOINs and
production-grade TPC-H/TPC-DS workloads. On top of that, Apache Doris requires
just 10%-20% of the cost of Snowflake or ClickHouse for OLAP workloads.",
+ 'picked': "true",
+ 'order': "2",
+ 'date': '2025-08-29',
+ 'author': 'velodb.io · VeloDB Engineering Team',
+ 'externalLink': 'https://www.velodb.io/blog/1463',
+ 'tags': ['Tech Sharing'],
+ "image": '/images/blogs/coffeebench-olap-showdown-part1-250829.PNG'
+}
+---
diff --git a/blog/coffeebench-part2-250917.md b/blog/coffeebench-part2-250917.md
new file mode 100644
index 00000000000..90e60de1150
--- /dev/null
+++ b/blog/coffeebench-part2-250917.md
@@ -0,0 +1,14 @@
+---
+{
+ 'title': 'Apache Doris Up To 40x Faster Than ClickHouse | OLAP Showdown
Part 2',
+ 'summary': "In every benchmark tested: CoffeeBench, TPC-H, and TPC-DS,
Apache Doris consistently pulled ahead, establishing clear dominance over both
ClickHouse v25.8 on-premises and ClickHouse Cloud.",
+ 'description': "In every benchmark tested: CoffeeBench, TPC-H, and TPC-DS,
Apache Doris consistently pulled ahead, establishing clear dominance over both
ClickHouse v25.8 on-premises and ClickHouse Cloud.",
+ 'picked': "true",
+ 'order': "1",
+ 'date': '2025-09-07',
+ 'author': 'velodb.io · VeloDB Engineering Team',
+ 'externalLink': 'https://www.velodb.io/blog/1504',
+ 'tags': ['Tech Sharing'],
+ "image": '/images/blogs/coffeebench-part2-250917.jpeg'
+}
+---
diff --git a/blog/data-pruning-250908.md b/blog/data-pruning-250908.md
new file mode 100644
index 00000000000..d0871fd7d44
--- /dev/null
+++ b/blog/data-pruning-250908.md
@@ -0,0 +1,12 @@
+---
+{
+ 'title': 'Deep Dive: Data Pruning in Apache Doris',
+ 'summary': "At Apache Doris, we have implemented multiple strategies to
make the system more intelligent, enabling it to skip unnecessary data
processing. In this article, we will discuss all the data pruning techniques
used in Apache Doris.",
+ 'description': "At Apache Doris, we have implemented multiple strategies
to make the system more intelligent, enabling it to skip unnecessary data
processing. In this article, we will discuss all the data pruning techniques
used in Apache Doris.",
+ 'date': '2025-09-08',
+ 'author': 'velodb.io · VeloDB Engineering Team',
+ 'externalLink': 'https://www.velodb.io/blog/1489',
+ 'tags': ['Tech Sharing'],
+ "image": '/images/blogs/data-pruning-250905.PNG'
+}
+---
diff --git a/blog/data-trait-250905.md b/blog/data-trait-250905.md
new file mode 100644
index 00000000000..528870eb854
--- /dev/null
+++ b/blog/data-trait-250905.md
@@ -0,0 +1,14 @@
+---
+{
+ 'title': 'Data Traits in Apache Doris: The Secret Weapon Behind 2x Faster
Performance',
+ 'summary': "At the core of database systems, the query optimizer acts as a
shrewd strategist, constantly analyzing data traits to devise the optimal
execution plans. Apache Doris, a high-performance MPP analytical database,
employs a built-in Data Trait analysis mechanism in its optimizer. By
uncovering statistical traits and semantic constraints within the data, Data
Trait provides fundamental support for query optimization. Let’s explore its
power!",
+ 'description': "At the core of database systems, the query optimizer acts
as a shrewd strategist, constantly analyzing data traits to devise the optimal
execution plans. Apache Doris, a high-performance MPP analytical database,
employs a built-in Data Trait analysis mechanism in its optimizer. By
uncovering statistical traits and semantic constraints within the data, Data
Trait provides fundamental support for query optimization. Let’s explore its
power!",
+ 'picked': "true",
+ 'order': "4",
+ 'date': '2025-09-05',
+ 'author': 'velodb.io · VeloDB Engineering Team',
+ 'externalLink': 'https://www.velodb.io/blog/1488',
+ 'tags': ['Tech Sharing'],
+ "image": '/images/blogs/data-trait-250908.PNG'
+}
+---
diff --git
a/blog/real-time-data-analytics-at-scale-integrating-apache-flink-and-doris.md
b/blog/real-time-data-analytics-at-scale-integrating-apache-flink-and-doris.md
index 3827b17d520..f0cac2ffe43 100644
---
a/blog/real-time-data-analytics-at-scale-integrating-apache-flink-and-doris.md
+++
b/blog/real-time-data-analytics-at-scale-integrating-apache-flink-and-doris.md
@@ -3,8 +3,6 @@
'title': 'Real-time Data Analytics at Scale: Integrating Apache Flink and
Doris',
'summary': "In this article, we'll discuss the main technical use cases of
Flink Doris Connector and Flink CDC, showing a complete playbook to combine
Flink's real-time processing with Doris's fast analytics.",
'description': "In this article, we'll discuss the main technical use
cases of Flink Doris Connector and Flink CDC, showing a complete playbook to
combine Flink's real-time processing with Doris's fast analytics.",
- 'picked': "true",
- 'order': "3",
'date': '2025-08-18',
'author': 'velodb.io · VeloDB Engineering Team',
'externalLink': 'https://www.velodb.io/blog/1453',
diff --git
a/blog/real-time-lakehouse-in-cainiao-large-scale-business-scenarios.md
b/blog/real-time-lakehouse-in-cainiao-large-scale-business-scenarios.md
index e51888e9a0c..8277a13b6b6 100644
--- a/blog/real-time-lakehouse-in-cainiao-large-scale-business-scenarios.md
+++ b/blog/real-time-lakehouse-in-cainiao-large-scale-business-scenarios.md
@@ -5,8 +5,6 @@
'description': "Cainiao, the world of e-commerce logistics giant, chose
Apache Doris to upgrade its data platform. This step-by-step migration started
in 2023, including validating Doris in a mission-critical scenario, expanding
Doris's application scenarios, and executing full-scale deployment. the cost
efficiency, stability, and operational efficiency of Doris have been powerfully
proven. Currently, Doris powers over 25 clusters (10,000+ CPUs) across 3
regions without any failure.",
'date': '2025-08-18',
'author': 'velodb.io · VeloDB Engineering Team',
- 'picked': "true",
- 'order': "2",
'externalLink': 'https://www.velodb.io/blog/1454',
'tags': ['Best Practice'],
"image":
'/images/blogs/real-time-lakehouse-in-cainiao-large-scale-business-scenarios.png'
diff --git a/blog/rtabench-250902.md b/blog/rtabench-250902.md
new file mode 100644
index 00000000000..5246665b775
--- /dev/null
+++ b/blog/rtabench-250902.md
@@ -0,0 +1,14 @@
+---
+{
+ 'title': 'Apache Doris Tops RTABench, 6x Faster Than ClickHouse, 30x
Faster Than PostgreSQL',
+ 'summary': "Apache Doris, a popular real-time data warehouse, ranked first
in the latest RTABench results, setting a new benchmark for real-time analytics
performance. In standardized tests, Doris delivered up to 6 times the
performance of ClickHouse, 30 times that of PostgreSQL, and 100 times that of
MongoDB.",
+ 'description': "Apache Doris, a popular real-time data warehouse, ranked
first in the latest RTABench results, setting a new benchmark for real-time
analytics performance. In standardized tests, Doris delivered up to 6 times the
performance of ClickHouse, 30 times that of PostgreSQL, and 100 times that of
MongoDB.",
+ 'picked': "true",
+ 'order': "3",
+ 'date': '2025-09-02',
+ 'author': 'velodb.io · VeloDB Engineering Team',
+ 'externalLink': 'https://www.velodb.io/blog/1465',
+ 'tags': ['Tech Sharing'],
+ "image": '/images/blogs/rtabench-250902.JPEG'
+}
+---
diff --git
"a/blog/unified-lakehouse-with-apache-doris-and-paimon-xiaomi-achieves-6\303\227-faster-performance.md"
"b/blog/unified-lakehouse-with-apache-doris-and-paimon-xiaomi-achieves-6\303\227-faster-performance.md"
index 011d6e9f060..a5cff74f940 100644
---
"a/blog/unified-lakehouse-with-apache-doris-and-paimon-xiaomi-achieves-6\303\227-faster-performance.md"
+++
"b/blog/unified-lakehouse-with-apache-doris-and-paimon-xiaomi-achieves-6\303\227-faster-performance.md"
@@ -5,8 +5,6 @@
'description': "Xiaomi is a leading global player in consumer electronics.
Best known for its smartphones and smart home devices, Xiaomi is among the top
global three smartphone makers and continues to expand into new offerings like
electric vehicles. With a global operation, Xiaomi requires an analytical data
architecture that can support its growth and increasing demand. Their solution:
Apache Doris and Apache Paimon.",
'date': '2025-08-28',
'author': 'velodb.io · VeloDB Engineering Team',
- 'picked': "true",
- 'order': "1",
'externalLink': 'https://www.velodb.io/blog/1461',
'tags': ['Best Practice'],
'image': '/images/blogs/lakehouse-performance.png'
diff --git a/src/components/recent-blogs/recent-blogs.data.ts
b/src/components/recent-blogs/recent-blogs.data.ts
index 3a40a2fdaed..edff4bb384d 100644
--- a/src/components/recent-blogs/recent-blogs.data.ts
+++ b/src/components/recent-blogs/recent-blogs.data.ts
@@ -1,19 +1,19 @@
export const RECENT_BLOGS_POSTS = [
{
- label: `Apache Doris Empowered 5G Fully-Connected Factory with A
Unified Real-time & Batch Data Platform`,
- link: 'https://www.velodb.io/blog/1444',
+ label: 'Apache Doris Up To 40x Faster Than ClickHouse | OLAP Showdown
Part 2',
+ link: 'https://www.velodb.io/blog/1504',
},
{
- label: 'Journey of telecom giant from ClickHouse to Apache Doris: 13PB
in one table',
- link: 'https://www.velodb.io/blog/1440',
+ label: 'The Ultimate OLAP Showdown: Apache Doris vs. ClickHouse vs.
Snowflake (Part 1)',
+ link: 'https://www.velodb.io/blog/1463',
},
{
- label: 'How Tencent Music saved 80% in costs by migrating from
Elasticsearch to Apache Doris',
- link: 'https://www.velodb.io/blog/1395',
+ label: 'Apache Doris Tops RTABench, 6x Faster Than ClickHouse, 30x
Faster Than PostgreSQL',
+ link: 'https://www.velodb.io/blog/1465',
},
{
- label: 'Why Apache Doris is a Better Alternative to Elasticsearch for
Real-Time Analytics',
- link: 'https://www.velodb.io/blog/1379',
+ label: 'At Apache Doris, we have implemented multiple strategies to
make the system more intelligent, enabling it to skip unnecessary data
processing. In this article, we will discuss all the data pruning techniques
used in Apache Doris.',
+ link: 'https://www.velodb.io/blog/1489',
},
diff --git a/src/constant/newsletter.data.ts b/src/constant/newsletter.data.ts
index c2c51625f36..3b78ef9b84a 100644
--- a/src/constant/newsletter.data.ts
+++ b/src/constant/newsletter.data.ts
@@ -1,31 +1,31 @@
export const NEWSLETTER_DATA = [
{
- tags: ['Best Practice'],
- title: "Unified Lakehouse with Apache Doris and Paimon: Xiaomi
Achieves 6× Faster Performance",
- content: `Xiaomi is a leading global player in consumer electronics.
Best known for its smartphones and smart home devices, Xiaomi is among the top
global three smartphone makers and continues to expand into new offerings like
electric vehicles. With a global operation, Xiaomi requires an analytical data
architecture that can support its growth and increasing demand. Their solution:
Apache Doris and Apache Paimon.`,
- to: 'https://www.velodb.io/blog/1461',
- image: 'blogs/lakehouse-performance.png',
+ tags: ['Tech Sharing'],
+ title: "Apache Doris Up To 40x Faster Than ClickHouse | OLAP Showdown
Part 2",
+ content: `In every benchmark tested: CoffeeBench, TPC-H, and TPC-DS,
Apache Doris consistently pulled ahead, establishing clear dominance over both
ClickHouse v25.8 on-premises and ClickHouse Cloud.`,
+ to: 'https://www.velodb.io/blog/1504',
+ image: 'blogs/coffeebench-part2-250917.jpeg',
},
{
- tags: ['Best Practice'],
- title: "Apache Doris Empowers Real-time Lakehouse in Large-Scale
Business Scenarios of Cainiao",
- content: `Cainiao, the world of e-commerce logistics giant, chose
Apache Doris to upgrade its data platform. This step-by-step migration started
in 2023, including validating Doris in a mission-critical scenario, expanding
Doris's application scenarios, and executing full-scale deployment. the cost
efficiency, stability, and operational efficiency of Doris have been powerfully
proven. Currently, Doris powers over 25 clusters (10,000+ CPUs) across 3
regions without any failure.`,
- to: 'https://www.velodb.io/blog/1454',
- image:
'blogs/real-time-lakehouse-in-cainiao-large-scale-business-scenarios.png',
+ tags: ['Tech Sharing'],
+ title: "The Ultimate OLAP Showdown: Apache Doris vs. ClickHouse vs.
Snowflake (Part 1)",
+ content: `Apache Doris consistently delivers significantly faster
performance in large-scale benchmarks spanning both straightforward JOINs and
production-grade TPC-H/TPC-DS workloads. On top of that, Apache Doris requires
just 10%-20% of the cost of Snowflake or ClickHouse for OLAP workloads.`,
+ to: 'https://www.velodb.io/blog/1463',
+ image: 'blogs/coffeebench-olap-showdown-part1-250829.PNG',
},
{
- tags: ['Best Practice'],
- title: "Leading Cloud Computing Service Provider Chose Apache Doris +
Iceberg for Hyperscale Data Lakehouse",
- content: `The world's cloud computing service giant chose Apache Doris
+ Apache Iceberg to upgrade its data platform into a flexible, efficient data
lakehouse with low costs. This solution handles reporting and BI, federated
analysis, log storage and analysis, and high-concurrency analysis. With Apache
Doris, this company has successfully launched 20+ projects with 50+ clusters,
3000+ nodes, and over 15 petabytes of data.`,
- to: 'https://www.velodb.io/blog/1450',
- image:
'blogs/apache-doris-and-iceberg-building-hyperscale-data-lakehouse.png',
+ tags: ['Tech Sharing'],
+ title: "Apache Doris Tops RTABench, 6x Faster Than ClickHouse, 30x
Faster Than PostgreSQL",
+ content: `Apache Doris, a popular real-time data warehouse, ranked
first in the latest RTABench results, setting a new benchmark for real-time
analytics performance. In standardized tests, Doris delivered up to 6 times the
performance of ClickHouse, 30 times that of PostgreSQL, and 100 times that of
MongoDB.`,
+ to: 'https://www.velodb.io/blog/1465',
+ image: 'blogs/rtabench-250902.JPEG',
},
{
tags: ['Tech Sharing'],
- title: "Real-time Data Analytics at Scale: Integrating Apache Flink
and Doris",
- content: `In this article, we'll discuss the main technical use cases
of Flink Doris Connector and Flink CDC, showing a complete playbook to combine
Flink's real-time processing with Doris's fast analytics.`,
- to: 'https://www.velodb.io/blog/1453',
- image:
'blogs/real-time-data-analytics-at-scale-integrating-apache-flink-and-doris.JPEG',
+ title: "Deep Dive: Data Pruning in Apache Doris",
+ content: `At Apache Doris, we have implemented multiple strategies to
make the system more intelligent, enabling it to skip unnecessary data
processing. In this article, we will discuss all the data pruning techniques
used in Apache Doris.`,
+ to: 'https://www.velodb.io/blog/1489',
+ image: 'blogs/data-pruning-250905.PNG',
},
diff --git a/static/images/blogs/coffeebench-olap-showdown-part1-250829.PNG
b/static/images/blogs/coffeebench-olap-showdown-part1-250829.PNG
new file mode 100644
index 00000000000..14a4d139682
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b/static/images/blogs/coffeebench-olap-showdown-part1-250829.PNG differ
diff --git a/static/images/blogs/coffeebench-part2-250917.jpeg
b/static/images/blogs/coffeebench-part2-250917.jpeg
new file mode 100644
index 00000000000..a7e21af13af
Binary files /dev/null and b/static/images/blogs/coffeebench-part2-250917.jpeg
differ
diff --git a/static/images/blogs/data-pruning-250905.PNG
b/static/images/blogs/data-pruning-250905.PNG
new file mode 100644
index 00000000000..622c927f273
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diff --git a/static/images/blogs/data-trait-250908.PNG
b/static/images/blogs/data-trait-250908.PNG
new file mode 100644
index 00000000000..cd8da7fdc73
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diff --git a/static/images/blogs/rtabench-250902.JPEG
b/static/images/blogs/rtabench-250902.JPEG
new file mode 100644
index 00000000000..8cdace8952f
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