Update slack links

blog/Auto-Synchronization-of-an-Entire-MySQL-Database-for-Data-Analysis.md

@@ -191,5 +191,5 @@ CREATE TABLE doris_sink (
 );
 ```
 
-If you've got any questions, find Apache Doris developers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw).
+If you've got any questions, find Apache Doris developers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ).
 

blog/Building-A-Log-Analytics-Solution-10-Times-More-Cost-Effective-Than-Elasticsearch.md

@@ -247,4 +247,4 @@ For more feature introduction and usage guide, see documentation: [Inverted Inde
 
 In a word, what contributes to Apache Doris' 10-time higher cost-effectiveness than Elasticsearch is its OLAP-tailored optimizations for inverted indexing, supported by the columnar storage engine, massively parallel processing framework, vectorized query engine, and cost-based optimizer of Apache Doris. 
 
-As proud as we are about our own inverted indexing solution, we understand that self-published benchmarks can be controversial, so we are open to [feedback](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) from any third-party users and see how [Apache Doris](https://github.com/apache/doris) works in real-world cases.
+As proud as we are about our own inverted indexing solution, we understand that self-published benchmarks can be controversial, so we are open to [feedback](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) from any third-party users and see how [Apache Doris](https://github.com/apache/doris) works in real-world cases.

blog/Building-a-Data-Warehouse-for-Traditional-Industry.md

@@ -172,4 +172,4 @@ Actually, before we evolved into our current data architecture, we tried Hive, S
 
 On the other hand, to smoothen our  big data transition, we need to make our data platform as simple as possible in terms of usage and maintenance. That's why we landed on Apache Doris. It is compatible with MySQL protocol and provides a rich collection of functions so we don't have to develop our own UDFs. Also, it is composed of only two types of processes: frontends and backends, so it is easy to scale and track.
 
-Find Apache Doris developers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw).
+Find Apache Doris developers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ).

blog/Building-the-Next-Generation-Data-Lakehouse-10X-Performance.md

@@ -249,5 +249,5 @@ http://doris.apache.org
 
 https://github.com/apache/doris
 
-Find Apache Doris developers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw).
+Find Apache Doris developers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ).
 

blog/Database-Dissection-How-Fast-Data-Queries-Are-Implemented.md

@@ -134,7 +134,7 @@ The results are as below:
 In short, what contributed to the fast data loading and data queries in this case?
 
 - The Colocate mechanism that's designed for distributed computing
-- Collaboration between database users and [developers](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) that enables the operator merging
+- Collaboration between database users and [developers](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) that enables the operator merging
 - Support for a wide range of data loading methods to choose from
 - A vectorized engine that brings overall performance increase
 

blog/Database-in-Fintech-How-to-Support-ten-thousand-Dashboards-Without-Causing-a-Mess.md

@@ -73,7 +73,7 @@ Analysts often check data reports of the same metrics on a regular basis. These
 
 The complexity of data analysis in the financial industry lies in the data itself other than the engineering side. Thus, the underlying data architecture should focus on facilitating the unified and efficient management of data. Apache Doris provides the flexibility of simple metric registration and the ability of fast and resource-efficient metric computation. In this case, the user is able to handle 10,000 active financial metrics in 10,000 dashboards with 30% less ETL efforts.
 
-Find Apache Doris developers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw).
+Find Apache Doris developers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ).
 
 
 

blog/For-Entry-Level-Data-Engineers-How-to-Build-a-Simple-but-Solid-Data-Architecture.md

@@ -100,4 +100,4 @@ Firstly, Apache Doris is famously fast in Join queries. So if you need to extrac
 
 ## Conclusion
 
-This is the overview of a simple data architecture and how it can provide the data services you need. It ensures data ingestion stability and quality with Flink CDC, and quick data analysis with Apache Doris. The deployment of this architecture is simple, too. If you plan for a data analytic upgrade for your business, you might refer to this case. If you need advice and help, you may join our [community here](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw).
+This is the overview of a simple data architecture and how it can provide the data services you need. It ensures data ingestion stability and quality with Flink CDC, and quick data analysis with Apache Doris. The deployment of this architecture is simple, too. If you plan for a data analytic upgrade for your business, you might refer to this case. If you need advice and help, you may join our [community here](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ).

blog/Is-Your-Latest-Data-Really-the-Latest-Check-the-Data-Update-Mechanism-of-Your-Database.md

@@ -223,5 +223,5 @@ mysql> select * from test_table;
 
 ## Conclusion
 
-Congratulations. Now you've gained an overview of how data updates are implemented in Apache Doris. With this knowledge, you can basically guarantee efficiency and accuracy of data updating. But wait, there is so much more about that. As Apache Doris 2.0 is going to provide more powerful Partial Column Update capabilities, with improved execution of the Update statement and the support for more complicated multi-table Join queries, I will show you how to take advantage of them in details in my follow-up writings. [We](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) are constantly updating our data updates!
+Congratulations. Now you've gained an overview of how data updates are implemented in Apache Doris. With this knowledge, you can basically guarantee efficiency and accuracy of data updating. But wait, there is so much more about that. As Apache Doris 2.0 is going to provide more powerful Partial Column Update capabilities, with improved execution of the Update statement and the support for more complicated multi-table Join queries, I will show you how to take advantage of them in details in my follow-up writings. [We](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) are constantly updating our data updates!
 

blog/Listen-to-That-Poor-BI-Engineer-We-Need-Fast-Joins.md

@@ -102,4 +102,4 @@ We believe self-service BI is the future in the BI landscape, just like AGI is t
 
 
 
-Find the Apache Doris developers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw)
+Find the Apache Doris developers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ)

blog/Log-Analysis-How-to-Digest-15-Billion-Logs-Per-Day-and-Keep-Big-Queries-Within-1-Second.md

@@ -88,7 +88,7 @@ These strategies have shortened the response time of queries. For example, a que
 
 ## Ongoing Plans
 
-The user is now testing with the newly added [inverted index](https://doris.apache.org/docs/table-design/index/inverted-index) in Apache Doris. It is designed to speed up full-text search of strings as well as equivalence and range queries of numerics and datetime. They have also provided their valuable feedback about the auto-bucketing logic in Doris: Currently, Doris decides the number of buckets for a partition  based on the data size of the previous partition. The problem for the user is, most of their new data comes in during daytime, but little at nights. So in their case, Doris creates too many buckets for night data but too few in daylight, which is the opposite of what they need. They hope to add a new auto-bucketing logic, where the reference for Doris to decide the number of buckets is the data size and distribution of the previous day. They've come to the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) and we are now working on this optimization. 
+The user is now testing with the newly added [inverted index](https://doris.apache.org/docs/table-design/index/inverted-index) in Apache Doris. It is designed to speed up full-text search of strings as well as equivalence and range queries of numerics and datetime. They have also provided their valuable feedback about the auto-bucketing logic in Doris: Currently, Doris decides the number of buckets for a partition  based on the data size of the previous partition. The problem for the user is, most of their new data comes in during daytime, but little at nights. So in their case, Doris creates too many buckets for night data but too few in daylight, which is the opposite of what they need. They hope to add a new auto-bucketing logic, where the reference for Doris to decide the number of buckets is the data size and distribution of the previous day. They've come to the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) and we are now working on this optimization. 
 
 
 

blog/Replacing-Apache-Hive-Elasticsearch-and-PostgreSQL-with-Apache-Doris.md

@@ -114,11 +114,11 @@ We have 2 clusters in Apache Doris accommodating tens of TBs of data, with almos
 
 ![user-segmentation-latency-3](/images/Tianyancha_11.png)
 
-Lastly, I would like to share with you something that interested us most when we first talked to the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw):
+Lastly, I would like to share with you something that interested us most when we first talked to the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ):
 
 - Apache Doris supports data ingestion transactions so it can ensure data is written **exactly once**.
 - It is well-integrated with the data ecosystem and can smoothly interface with most data sources and data formats.
 - It allows us to implement elastic scaling of clusters using the command line interface.
 - It outperforms ClickHouse in **join queries**.
 
-Find Apache Doris developers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-1t3wfymur-0soNPATWQ~gbU8xutFOLog)
+Find Apache Doris developers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ)

blog/Say-Goodbye-to-OOM-Crashes.md

@@ -163,5 +163,5 @@ If the process memory consumed is beyond the MemLimit (90% of total system memor
 
 After optimizations in memory allocation, memory tracking, and memory limit, we have substantially increased the stability and high-concurrency performance of Apache Doris as a real-time analytic data warehouse platform. OOM crash in the backend is a rare scene now. Even if there is an OOM, users can locate the problem root based on the logs and then fix it. In addition, with more flexible memory limits on queries and data ingestion, users don't have to spend extra effort taking care of memory when memory space is adequate. 
 
-In the next phase, we plan to ensure completion of queries in memory overcommitment, which means less queries will have to be canceled due to memory shortage. We have broken this objective into specific directions of work: exception safety, memory isolation between resource groups, and the flushing mechanism of intermediate data. If you want to meet our developers, [this is where you find us](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw).
+In the next phase, we plan to ensure completion of queries in memory overcommitment, which means less queries will have to be canceled due to memory shortage. We have broken this objective into specific directions of work: exception safety, memory isolation between resource groups, and the flushing mechanism of intermediate data. If you want to meet our developers, [this is where you find us](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ).
 

blog/Tencent-Data-Engineers-Why-We-Went-from-ClickHouse-to-Apache-Doris.md

@@ -255,4 +255,4 @@ http://doris.apache.org
 
 https://github.com/apache/doris
 
-Find Apache Doris developers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw)
+Find Apache Doris developers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ)

blog/Tiered-Storage-for-Hot-and-Cold-Data-What-Why-and-How.md

@@ -353,7 +353,7 @@ Apache Doris 2.0 has been optimized for cold data queries. Only the first-time a
 
 In Apache Doris, each data ingestion leads to the generation of a new  Rowset, so the update of historical data will be put in a Rowset that is separated from those of newly loaded data. That’s how it makes sure the updating of cold data does not interfere with the ingestion of hot  data. Once the rowsets cool down, they will be moved to S3 and deleted  locally, and the updated historical data will go to the partition where  it belongs.
 
-If you any questions, come find Apache Doris developers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw). We will be happy to provide targeted support.
+If you any questions, come find Apache Doris developers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ). We will be happy to provide targeted support.
 
 
 

blog/Understanding-Data-Compaction-in-3-Minutes.md

@@ -128,4 +128,4 @@ Every data engineer has somehow been harassed by complicated parameters and conf
 
 ## Conclusion
 
-This is how we keep our "storekeepers" working efficiently and cost-effectively. If you wonder how these strategies and optimization work in real practice, we tested Apache Doris with ClickBench. It reaches a **compaction speed of 300,000 row/s**; in high-concurrency scenarios, it maintains **a stable compaction score of around 50**. Also, we are planning to implement auto-tuning and increase observability for the compaction mechanism. If you are interested in the [Apache Doris](https://github.com/apache/doris) project and what we do, this is a group of visionary and passionate [developers](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) that you can talk to.
+This is how we keep our "storekeepers" working efficiently and cost-effectively. If you wonder how these strategies and optimization work in real practice, we tested Apache Doris with ClickBench. It reaches a **compaction speed of 300,000 row/s**; in high-concurrency scenarios, it maintains **a stable compaction score of around 50**. Also, we are planning to implement auto-tuning and increase observability for the compaction mechanism. If you are interested in the [Apache Doris](https://github.com/apache/doris) project and what we do, this is a group of visionary and passionate [developers](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) that you can talk to.

blog/a-fast-secure-high-available-real-time-data-warehouse-based-on-apache-doris.md

@@ -147,4 +147,4 @@ Disaster recovery is crucial for the financial industry. The user leverages the
 
 ## Conclusion
 
-We appreciate the user for their active [communication](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) with us along the way and are glad to see so many Apache Doris features fit in their needs. They are also planning on exploring federated query, compute-storage separation, and auto maintenance with Apache Doris. We look forward to more best practice and feedback from them.
+We appreciate the user for their active [communication](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) with us along the way and are glad to see so many Apache Doris features fit in their needs. They are also planning on exploring federated query, compute-storage separation, and auto maintenance with Apache Doris. We look forward to more best practice and feedback from them.

blog/a-financial-anti-fraud-solution-based-on-the-apache-doris-data-warehouse.md

@@ -105,4 +105,4 @@ So far, the bank has added nearly 100 alerting rules for various risk types to t
 
 ## Conclusion
 
-For a comprehensive anti-fraud solution, the bank conducts full-scale real-time monitoring and reporting for all their data workflows. Then, for each transaction, they look into the multiple dimensions of it to identify risks. For the suspicious transactions reported by the bank customers, they perform federated queries to retrieve the full details of them. Also, an auto alerting mechanism is always on patrol to safeguard the whole system. These are the various types of analytic workloads in this solution. The implementation of them rely on the capabilities of Apache Doris, which is a data warehouse designed to be an all-in-one platform for various workloads. If you try to build your own anti-fraud solution, the [Apache Doris open source developers](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) are happy to exchange ideas with you.
+For a comprehensive anti-fraud solution, the bank conducts full-scale real-time monitoring and reporting for all their data workflows. Then, for each transaction, they look into the multiple dimensions of it to identify risks. For the suspicious transactions reported by the bank customers, they perform federated queries to retrieve the full details of them. Also, an auto alerting mechanism is always on patrol to safeguard the whole system. These are the various types of analytic workloads in this solution. The implementation of them rely on the capabilities of Apache Doris, which is a data warehouse designed to be an all-in-one platform for various workloads. If you try to build your own anti-fraud solution, the [Apache Doris open source developers](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) are happy to exchange ideas with you.

blog/ai-unicorn-minimax-from-loki-and-built-a-pb-scale-logging-system-with-doris.md

@@ -143,4 +143,4 @@ After a successful initial experience with Apache Doris, MiniMax proceeds with t
 
 - **Lakehousing**: expand the use of Apache Doris include big data processing and analysis within MiniMax, laying the foundation for a data lakehouse.
 
-If you have any questions or require assistance regarding Apache Doris, join the [community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2gmq5o30h-455W226d79zP3L96ZhXIoQ).
+If you have any questions or require assistance regarding Apache Doris, join the [community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ).