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new 0734b8660e6 [blog] Update doris compute-storage-decoupled blog (#2221)
0734b8660e6 is described below
commit 0734b8660e697333917d211a5c989f907133cd93
Author: KassieZ <[email protected]>
AuthorDate: Sat Mar 22 11:48:19 2025 +0800
[blog] Update doris compute-storage-decoupled blog (#2221)
## Versions
- [ ] dev
- [ ] 3.0
- [ ] 2.1
- [ ] 2.0
## Languages
- [ ] Chinese
- [ ] English
## Docs Checklist
- [ ] Checked by AI
- [ ] Test Cases Built
---
blog/doris-compute-storage-decoupled.md | 195 +++++++++++++++++++++
...-scaling-bitcoin-data-to-billions-of-records.md | 2 -
blog/release-note-3.0.4.md | 2 +-
docusaurus.config.js | 2 +-
sidebars.json | 1 +
src/constant/newsletter.data.ts | 14 +-
src/pages/index.tsx | 2 +-
static/images/batch-data-ingestion.jpg | Bin 0 -> 356837 bytes
static/images/compute-storage-decoupled-banner.jpg | Bin 0 -> 105054 bytes
static/images/compute-storage-decoupled-mode.jpg | Bin 0 -> 348378 bytes
static/images/compute-storage-decoupled.jpg | Bin 0 -> 630044 bytes
static/images/events/event-2.jpeg | Bin 128508 -> 135764 bytes
static/images/high-concurrency-ingestion.jpg | Bin 0 -> 401560 bytes
static/images/historical-data-cost.jpg | Bin 0 -> 411160 bytes
.../operational-cost-for-online-workloads.jpg | Bin 0 -> 300392 bytes
static/images/query-performance.jpg | Bin 0 -> 406930 bytes
versioned_sidebars/version-2.1-sidebars.json | 1 +
versioned_sidebars/version-3.0-sidebars.json | 1 +
18 files changed, 208 insertions(+), 12 deletions(-)
diff --git a/blog/doris-compute-storage-decoupled.md
b/blog/doris-compute-storage-decoupled.md
new file mode 100644
index 00000000000..acdf2157d54
--- /dev/null
+++ b/blog/doris-compute-storage-decoupled.md
@@ -0,0 +1,195 @@
+---
+{
+ 'title': 'Slash your cost by 90% with Apache Doris Compute-Storage
Decoupled Mode',
+ 'summary': 'Apache Doris compute-storage decoupled mode achieves 90% cost
reduction and provides elasticity and workload isolation, while maintaining
high performance in data ingestion and queries.',
+ 'description': 'Apache Doris compute-storage decoupled mode achieves 90%
cost reduction and provides elasticity and workload isolation, while
maintaining high performance in data ingestion and queries.',
+ 'date': '2025-03-21',
+ 'author': 'Apache Doris',
+ 'tags': ['Tech Sharing'],
+ 'picked': "true",
+ 'order': "1",
+ "image": '/images/compute-storage-decoupled-banner.jpg'
+}
+---
+
+<!--
+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.
+-->
+
+In the age of data-driven decision-making, the exponential growth of data
volume and the ever-evolving demands for analytics pose great challenges. Data
streams in from diverse sources (such as application logs, network
interactions, and mobile devices), spanning structured, semi-structured, and
unstructured formats. This diversity places pressure on storage and analytical
systems. Meanwhile, the surge in demand for real-time analytics and exploratory
queries requires systems to deliver [...]
+
+Apache Doris emerged in the era of integrated storage and computation, built
on a classic **Shared Nothing** architecture. In this design, storage and
computation are co-located on **Backend (BE) nodes**, leveraging an **MPP
(Massively Parallel Processing)** distributed computing model. This
architecture delivers key advantages, including high availability, simplified
deployment, seamless horizontal scalability, and exceptional real-time
analytical performance.
+
+For real-time analytics and small-scale data processing, Apache Doris stands
out with **predictable, stable low-latency performance**, making it an
indispensable solution. However, when scaling to large-scale data processing,
it encounters certain challenges, primarily in:
+
+- **Relatively high costs & low elasticity:** Balancing storage and compute
resources remains a big challenge. Storage capacity must be sufficient to
accommodate all data, while compute resources need to handle query workloads
efficiently. However, dynamically scaling clusters is often time-consuming,
prompting enterprises to over-provision resources to ensure stability. This
approach simplifies operations but leads to resource waste and increased costs.
+- **Limited workload isolation:** Since Apache Doris 2.0, Workload Groups
provide soft isolation, while Resource Groups offer a degree of hard isolation.
However, neither mechanism ensures complete physical isolation, which can
impact performance in multi-tenant or resource-intensive environments.
+- **Operational complexity:** Managing an OLAP system with built-in
distributed storage requires not only overseeing compute nodes but also
ensuring efficient storage administration. Storage management is inherently
complex, and misconfigurations or improper operations can lead to data loss,
making maintenance highly demanding.
+
+> Even so, in the absence of stable and large-scale storage support, a
integrated storage-compute architecture remains the optimal choice.
+
+As cloud infrastructure matures, enterprises increasingly seek **deeper Apache
Doris integration with public clouds, private clouds, and Kubernetes (K8s)
container platforms** to unlock greater elasticity and flexibility. Public
clouds offer mature object storage with on-demand compute resources,
eliminating the need for pre-allocated space, while private clouds leverage
technologies like K8s and MinIO to build scalable resource platforms. **This
evolution in cloud infrastructure has als [...]
+
+## Apache Doris Compute-Storage Decoupled Mode
+
+
+
+Since [version 3.0](https://doris.apache.org/blog/release-note-3.0.0), Apache
Doris has supported both the compute-storage decoupled mode and the
compute-storage coupled mode.
+
+
+
+### 01 Compute-Storage Decoupled
+
+In the compute-storage decoupled mode, Apache Doris adopts a three-tier
architecture consisting of three layers: shared storage, compute groups, and
meta data service:
+
+
+
+**Shared Storage Layer**
+
+Data is persisted in the shared storage layer, allowing compute nodes to
access and share data seamlessly. This design enhances compute node flexibility
and reduces operational overhead. Leveraging mature and reliable shared storage
results in ultra-low storage costs and high data reliability. Whether using
public cloud object storage or enterprise-managed shared storage, this approach
greatly reduces the maintenance complexity of Apache Doris.
+
+**Compute Groups**
+
+The compute layer consists of multiple compute groups responsible for
executing query plans. Each query is executed within a single compute group,
ensuring isolation and scalability. Compute nodes are stateless, utilizing
local disks as high-speed caches to accelerate queries while sharing the same
data and metadata services. Each compute group operates independently,
supporting on-demand scaling, and local caches remain isolated to ensure
workload separation and performance consistency.
+
+**Meta Data Service**
+
+The meta data layer manages system meta data, including databases, tables,
schemas, rowset meta data, and transaction information, with support for
horizontal scaling. Future iterations of Apache Doris’ compute-storage
decoupled mode will introduce stateless Frontend (FE) nodes, where memory
consumption is decoupled from cluster size. This evolution will eliminate
memory bottlenecks, allowing FE nodes to operate with minimal memory
requirements.
+
+### 02 Architecture Design
+
+Traditional compute-storage decoupling approaches typically store both data
and meta data in shared storage while centralizing transaction management on a
single FE node. However, this design introduces several challenges:
+
+- **Write performance bottlenecks**: The two-phase commit protocol, driven by
FE Master, incurs high latency and low throughput.
+- **Small file proliferation**: Frequent meta data writes generate excessive
small files, leading to system instability and inflated storage costs.
+- **Scalability constraints**: Since FE nodes manage meta data in memory, an
increasing number of Tablets amplifies memory pressure, eventually causing
write bottlenecks.
+- **Data deletion risks**: Relying on delta computation with timeout-based
mechanisms for deletion introduces challenges in synchronizing writes and
deletions. As a result, there is a risk of unintended data loss due to
misalignment between ongoing writes and scheduled deletions.
+
+Compared to traditional approaches, **Apache Doris** effectively addresses
these challenges through **a shared meta data service**:
+
+- **Real-time ingestion**: The meta data service provides a globally
consistent view, enabling low-latency, high-throughput writes. Benchmarks show
that the Apache Doris compute-storage decoupled mode achieves **100X higher**
performance than other solutions at 50 concurrent writes and **11X higher**
performance at 500 concurrent writes.
+- **Optimized small file management**: Data is written to shared storage,
while meta data is handled by the meta data service. This effectively reduces
small file overheads. Tests indicate that the Apache Doris compute-storage
decoupled mode generates only **1/2 the number of write files** compared to
other industry solutions.
+- **Enhanced scalability**: In future versions of Apache Doris, FE metadata
will be moved to the meta data service to eliminate cluster scaling limitations
and ensure seamless expansion.
+- **Reliable data deletion**: Doris employs a forward deletion mechanism based
on a globally consistent view. This ensures mutual exclusion between writes and
deletions, thus eliminating the risk of accidental data loss.
+
+### 03 What makes it stand out
+
+The Apache Doris compute-storage decoupled architecture provide values for
users in three aspects: cost efficiency, elasticity, and workload isolation.
+
+**Firstly, it brings a 90% cost reduction compared to the compute-storage
coupled mode.**
+
+- **Pay-as-you-go**: Unlike traditional coupled architectures, there’s no need
to pre-provision compute and storage resources. Storage costs scale with actual
usage, while compute resources can be dynamically adjusted based on demand.
+- **Single-replica storage**: Instead of maintaining three replicas in costly
block storage, data is stored as a single replica in low-cost object storage,
with hot data cached in block storage for performance. This dramatically
reduces storage footprint and hardware costs. For example, S3 costs only 25% to
50% of AWS EBS.
+- **Lower resource consumption**: In compute-storage decoupled mode,
compaction operations only process a single data replica, thus largely reducing
resource usage compared to multi-replica environments.
+
+**Secondly, with a stateless compute node design, Doris enables on-demand
resource scaling to meet fluctuating workloads efficiently.**
+
+- **Elastic auto-scaling**: Compute resources can be dynamically scaled to
accommodate traffic spikes or workload variations. When demand increases, Doris
can rapidly scale out compute nodes; when demand drops, resources scale down
automatically, avoiding unnecessary costs.
+- **Fine-grained compute resource allocation**: Doris allows compute nodes to
be strategically assigned to specific compute groups based on workload
requirements. For example, high-performance nodes handle complex queries and
high-concurrency workloads, while standard nodes manage lightweight queries and
infrequent requests.
+
+**Thirdly, Doris provides efficient resource scheduling and workload isolation
mechanisms.**
+
+- **Cross-business isolation**: Different business units can be assigned
dedicated compute groups with physical isolation, so workloads operate on
dedicated resources without interference.
+- **Offline workload isolation**: Large-scale batch processing tasks can be
segregated into dedicated compute groups, so users can leverage low-cost
resources for offline data processing without impacting real-time business
performance.
+- **Read-write isolation**: Doris allows dedicated compute groups for read and
write operations to ensure consistent query response times even under high
write loads.
+
+## Benchmarking and comparison
+
+To provide a clear evaluation of the compute-storage decoupled architecture of
Apache Doris, we conducted a series of benchmark tests across multiple
dimensions, including data ingestion, query performance, and resource cost
efficiency.
+
+### 01 Ingestion performance
+
+**High-concurrency ingestion**
+
+We compared Apache Doris' coupled and decoupled modes with other mainstream
solutions under the same compute resources. The tests measured real-time
ingestion performance under two levels of concurrency:
+
+- **50 concurrent writes**: Ingesting **250 files**, each containing **20,000
rows**.
+- **500 concurrent writes**: Ingesting **10,000 files**, each containing **500
rows**.
+
+**Test results:**
+
+- At 50 concurrent writes, Doris' compute-storage decoupled mode performed on
par with the coupled mode while achieving **100X the write performance** of
other industry compute-storage decoupled solutions.
+- At 500 concurrent writes, Doris' decoupled mode experienced a slight
performance drop compared to the coupled mode, yet still maintained an **11X
advantage** over other compute-storage decoupled architectures.
+
+
+
+**Batch data ingestion**
+
+To evaluate batch data ingestion efficiency, we conducted tests using **TPC-H
1TB** and **TPC-DS 1TB** datasets, comparing the compute-storage coupled and
decoupled modes of Apache Doris. Data was loaded using S3 Load. Under default
configurations, multiple tables were ingested sequentially, and the total
ingestion time was measured for comparison.
+
+Hardware configuration:
+
+- Cluster size: 4 compute instances (1 FE, 3 BE)
+- CPU: 48 cores per instance
+- Memory: 192GB per instance
+- Network Bandwidth: 21 Gbps
+- Storage: Enhanced SSD
+
+
+
+As is shown, even when using a single replica in both architectures, the
compute-storage decoupled mode outperforms the coupled mode in batch data
ingestion by **20.05%** and **27.98%** in the two benchmarks, respectively.
*(In real-world deployments, the coupled mode typically adopts a three-replica
strategy. This further amplifies the write performance gains of the decoupled
mode.)*
+
+### 02 Query Performance
+
+In the compute-storage decoupled mode, Apache Doris leverages a multi-tier
caching mechanism to accelerate queries. It improves overall query efficiency
by speeding up data access and minimizing reliance on shared storage. The cache
hierarchy includes:
+
+- **Doris Page Cache**: In-memory caching of decompressed data.
+- **Linux Page Cache**: In-memory caching of compressed data.
+- **Local Disk Cache**: Persistent caching of compressed data.
+
+Hardware configuration:
+
+- Cluster size: 4 compute instances (1 FE, 3 BE)
+- CPU: 48 cores per instance
+- Memory: 192GB per instance
+- Network Bandwidth: 21 Gbps
+- Storage: Enhanced SSD
+
+We conducted performance benchmarking under different caching scenarios in
both compute-storage coupled and decoupled modes. Using the TPC-DS 1TB dataset,
the test results are as follows:
+
+
+
+- **Full cache hit**: We execute the query twice and measure the runtime of
the second execution, ensuring that all data is preloaded into the cache. Query
performance in compute-storage decoupled mode matches that of the coupled
architecture with no performance degradation.
+- **Partial cache hit** (This scenario best reflects real-world usage.):
Before the test begins, all caches are cleared, and we measure the runtime of
the first execution while data is gradually loaded into the cache. Compared to
the coupled architecture, query performance remains nearly identical, with an
overall performance overhead of about 10%.
+- **No cache hit**: All caches are cleared before each SQL execution, ensuring
that every query runs without cached data. Compared to the coupled
architecture, query performance sees an approximate 35% degradation.
+
+### 03 Resource Cost
+
+**Operational cost for online workloads**
+
+Taking a real-world enterprise workload as an example, we compare the cost
differences between compute-storage coupled and decoupled modes in Apache Doris.
+
+- **Compute-storage coupled mode**: The dataset in Doris has a size of 100TB
per replica, resulting in a total of 300TB with three replicas. To prevent
frequent scaling operations from impacting business stability, disk usage is
maintained at about 50%. Thus, **the monthly resource cost amounts to
$36,962.7** (as detailed below).
+
+
+
+- **Compute-storage decoupled mode**: With the same data scale, adopting the
compute-storage decoupled model only requires storing a single replica in
object storage, while hot data is cached on local disks. As shown below, the
monthly resource cost is reduced to **$22,212.65**, achieving a 40% cost
savings.
+
+
+
+**Historical data cost**
+
+For example, with 200TB of historical data, the resource utilization under
both the compute-storage coupled and decoupled modes is shown below. The
coupled model incurs a monthly cost of $48,851.10, whereas the decoupled model
reduces the cost to just $4,502.40—**cutting expenses by over 90%**.
+
+
+
+## What's next
+
+Powered by compute-storage decoupling, Apache Doris excels in real-time
analytics, lakehouse analytics, observability and log storage & analysis.
Looking ahead, Apache Doris will continue to enhance its capabilities in this
mode. We will introduce new features such as snapshots, time travel, and
Cross-Cluster Replication (CCR) support, and achieve stateless FE to further
improve system stability and usability.
+
+If you're interested in Apache Doris' compute-storage decoupled mode and its
future development, we invite you to join the
[#compute-storage-decoupled](https://apachedoriscommunity.slack.com/archives/C08HZUZ37KJ)
channel in the Apache Doris Slack community, where you can connect with core
developers and users. We look forward to your thoughts and contributions!
+
+[Join us live on March
27](https://www.linkedin.com/events/exploringapachedoriscompute-sto7308127084457902080/comments/)
for more insights into the Apache Doris compute-storage decoupled mode!
\ No newline at end of file
diff --git
a/blog/ortege-studio-1-scaling-bitcoin-data-to-billions-of-records.md
b/blog/ortege-studio-1-scaling-bitcoin-data-to-billions-of-records.md
index edca940379b..cdc88ac0882 100644
--- a/blog/ortege-studio-1-scaling-bitcoin-data-to-billions-of-records.md
+++ b/blog/ortege-studio-1-scaling-bitcoin-data-to-billions-of-records.md
@@ -6,8 +6,6 @@
'date': '2024-11-20',
'author': 'Justin Trollip',
'tags': ['Best Practice'],
- 'picked': "true",
- 'order': "2",
"image": '/images/ortege-1.jpg'
}
diff --git a/blog/release-note-3.0.4.md b/blog/release-note-3.0.4.md
index 2530790d0dd..89b9dc58954 100644
--- a/blog/release-note-3.0.4.md
+++ b/blog/release-note-3.0.4.md
@@ -7,7 +7,7 @@
'author': 'Apache Doris',
'tags': ['Release Notes'],
'picked': "true",
- 'order': "1",
+ 'order': "2",
"image": '/images/3.0.4.jpg'
}
---
diff --git a/docusaurus.config.js b/docusaurus.config.js
index 0a825609dff..01feb3a1b0e 100644
--- a/docusaurus.config.js
+++ b/docusaurus.config.js
@@ -207,7 +207,7 @@ const config = {
content: `<a
href="https://www.velodb.io/events/apache-doris-compute-storage-decoupled-mode-and-velo-db-cloud-demo";
target="_blank" style="display:flex; width: 100%; align-items: center;
justify-content: center; margin-left: 4px; text-decoration: none;">
<img style="width: 19px; height: 19px; margin-right: 3px;"
src="/images/nav-star.svg">
<span
style="color:#52CAA3;font-size:0.875rem;font-weight:700;line-height:1rem;
margin-right:0.675rem; text-decoration: none;">NEW</span>
- <span>Join us for a live webinar to explore Apache Doris'
compute-storage decoupled mode and get a hands-on demo</span>
+ <span>Join us for a live webinar to explore Apache Doris'
compute-storage decoupled mode</span>
<p
style="margin-left:0.675rem;color:#52CAA3;font-size:0.875rem;line-height:1rem;font-weight:700;letter-spacing:0.28px;">Register
Now -></p>
</a>`,
textColor: '#4C576C',
diff --git a/sidebars.json b/sidebars.json
index df21771868b..d51e3a8a06c 100644
--- a/sidebars.json
+++ b/sidebars.json
@@ -995,6 +995,7 @@
"sql-manual/sql-functions/scalar-functions/string-functions/split-part",
"sql-manual/sql-functions/scalar-functions/string-functions/split-by-string",
"sql-manual/sql-functions/scalar-functions/string-functions/split-by-regexp",
+
"sql-manual/sql-functions/scalar-functions/string-functions/strcmp",
"sql-manual/sql-functions/scalar-functions/string-functions/substring-index",
"sql-manual/sql-functions/scalar-functions/string-functions/ngram-search",
"sql-manual/sql-functions/scalar-functions/string-functions/parse-url",
diff --git a/src/constant/newsletter.data.ts b/src/constant/newsletter.data.ts
index 134e4278b71..5ad7e2dd7d4 100644
--- a/src/constant/newsletter.data.ts
+++ b/src/constant/newsletter.data.ts
@@ -1,4 +1,11 @@
export const NEWSLETTER_DATA = [
+ {
+ tags: ['Tech Sharing'],
+ title: "Slash your cost by 90% with Apache Doris Compute-Storage
Decoupled Mode",
+ content: `Apache Doris compute-storage decoupled mode achieves 90%
cost reduction and provides elasticity and workload isolation, while
maintaining high performance in data ingestion and queries.`,
+ to: '/blog/doris-compute-storage-decoupled',
+ image: 'compute-storage-decoupled-banner.jpg',
+ },
{
tags: ['Release Note'],
title: "Apache Doris 3.0.4 Released",
@@ -19,13 +26,6 @@ export const NEWSLETTER_DATA = [
to:
'/blog/ortege-studio-2-fine-tuning-apache-doris-for-maximum-performance-and-resilience',
image: 'ortege-2.jpg',
},
- {
- tags: ['Best Practice'],
- title: "Scaling Bitcoin data to billions of records with Apache Doris:
our journey to auto-partitioning",
- content: `To power lightning-fast queries and ensure the Deep Dive
dashboards deliver real-time insights, Ortege relies on Apache Doris. A crucial
feature they embrace is Auto Partition.`,
- to:
'/blog/ortege-studio-1-scaling-bitcoin-data-to-billions-of-records',
- image: 'ortege-1.jpg',
- },
];
diff --git a/src/pages/index.tsx b/src/pages/index.tsx
index 16ef0bd6656..6399c36ae0f 100644
--- a/src/pages/index.tsx
+++ b/src/pages/index.tsx
@@ -87,7 +87,7 @@ export default function Home(): JSX.Element {
</span>
</div>
<p className="lg:ml-[0.75rem] group-hover:text-[#444FD9]
text-[1rem]/[1rem] text-[#000]">
- Join our live webinar to discover Doris' compute-storage
decoupled mode with a demo !
+ Join our live webinar to discover Doris' compute-storage
decoupled mode !
</p>
</Link>
),
diff --git a/static/images/batch-data-ingestion.jpg
b/static/images/batch-data-ingestion.jpg
new file mode 100644
index 00000000000..b5aae6005b1
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diff --git a/static/images/compute-storage-decoupled-banner.jpg
b/static/images/compute-storage-decoupled-banner.jpg
new file mode 100644
index 00000000000..e5511f6dd04
Binary files /dev/null and b/static/images/compute-storage-decoupled-banner.jpg
differ
diff --git a/static/images/compute-storage-decoupled-mode.jpg
b/static/images/compute-storage-decoupled-mode.jpg
new file mode 100644
index 00000000000..889e013b567
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differ
diff --git a/static/images/compute-storage-decoupled.jpg
b/static/images/compute-storage-decoupled.jpg
new file mode 100644
index 00000000000..5faf6c42514
Binary files /dev/null and b/static/images/compute-storage-decoupled.jpg differ
diff --git a/static/images/events/event-2.jpeg
b/static/images/events/event-2.jpeg
index 14af6eabb38..61ac94d8edf 100644
Binary files a/static/images/events/event-2.jpeg and
b/static/images/events/event-2.jpeg differ
diff --git a/static/images/high-concurrency-ingestion.jpg
b/static/images/high-concurrency-ingestion.jpg
new file mode 100644
index 00000000000..2532b2f3d3b
Binary files /dev/null and b/static/images/high-concurrency-ingestion.jpg differ
diff --git a/static/images/historical-data-cost.jpg
b/static/images/historical-data-cost.jpg
new file mode 100644
index 00000000000..0ea241d2a3b
Binary files /dev/null and b/static/images/historical-data-cost.jpg differ
diff --git a/static/images/operational-cost-for-online-workloads.jpg
b/static/images/operational-cost-for-online-workloads.jpg
new file mode 100644
index 00000000000..723758daa04
Binary files /dev/null and
b/static/images/operational-cost-for-online-workloads.jpg differ
diff --git a/static/images/query-performance.jpg
b/static/images/query-performance.jpg
new file mode 100644
index 00000000000..be5a2b7d7c0
Binary files /dev/null and b/static/images/query-performance.jpg differ
diff --git a/versioned_sidebars/version-2.1-sidebars.json
b/versioned_sidebars/version-2.1-sidebars.json
index 37bba4d3e69..c105480dc65 100644
--- a/versioned_sidebars/version-2.1-sidebars.json
+++ b/versioned_sidebars/version-2.1-sidebars.json
@@ -1004,6 +1004,7 @@
"sql-manual/sql-functions/scalar-functions/string-functions/split-part",
"sql-manual/sql-functions/scalar-functions/string-functions/split-by-string",
"sql-manual/sql-functions/scalar-functions/string-functions/substring-index",
+
"sql-manual/sql-functions/scalar-functions/string-functions/strcmp",
"sql-manual/sql-functions/scalar-functions/string-functions/ngram-search",
"sql-manual/sql-functions/scalar-functions/string-functions/parse-url",
"sql-manual/sql-functions/scalar-functions/string-functions/protocol",
diff --git a/versioned_sidebars/version-3.0-sidebars.json
b/versioned_sidebars/version-3.0-sidebars.json
index f7df049d2d3..c60236a1c6b 100644
--- a/versioned_sidebars/version-3.0-sidebars.json
+++ b/versioned_sidebars/version-3.0-sidebars.json
@@ -1052,6 +1052,7 @@
"sql-manual/sql-functions/scalar-functions/string-functions/split-part",
"sql-manual/sql-functions/scalar-functions/string-functions/split-by-string",
"sql-manual/sql-functions/scalar-functions/string-functions/substring-index",
+
"sql-manual/sql-functions/scalar-functions/string-functions/strcmp",
"sql-manual/sql-functions/scalar-functions/string-functions/ngram-search",
"sql-manual/sql-functions/scalar-functions/string-functions/parse-url",
"sql-manual/sql-functions/scalar-functions/string-functions/protocol",
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