diff --git a/blog/Auto-Synchronization-of-an-Entire-MySQL-Database-for-Data-Analysis.md b/blog/Auto-Synchronization-of-an-Entire-MySQL-Database-for-Data-Analysis.md index 94e06e4d631dd..947fe709f0dad 100644 --- a/blog/Auto-Synchronization-of-an-Entire-MySQL-Database-for-Data-Analysis.md +++ b/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). diff --git a/blog/Building-A-Log-Analytics-Solution-10-Times-More-Cost-Effective-Than-Elasticsearch.md b/blog/Building-A-Log-Analytics-Solution-10-Times-More-Cost-Effective-Than-Elasticsearch.md index 256ac9bbf8d9b..6cae594a195bf 100644 --- a/blog/Building-A-Log-Analytics-Solution-10-Times-More-Cost-Effective-Than-Elasticsearch.md +++ b/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. diff --git a/blog/Building-a-Data-Warehouse-for-Traditional-Industry.md b/blog/Building-a-Data-Warehouse-for-Traditional-Industry.md index 7eff20ccb4f4e..f96cc24ac69a6 100644 --- a/blog/Building-a-Data-Warehouse-for-Traditional-Industry.md +++ b/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). diff --git a/blog/Building-the-Next-Generation-Data-Lakehouse-10X-Performance.md b/blog/Building-the-Next-Generation-Data-Lakehouse-10X-Performance.md index 5663da2f13e52..a7e9246e1bdda 100644 --- a/blog/Building-the-Next-Generation-Data-Lakehouse-10X-Performance.md +++ b/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). diff --git a/blog/Database-Dissection-How-Fast-Data-Queries-Are-Implemented.md b/blog/Database-Dissection-How-Fast-Data-Queries-Are-Implemented.md index 9d31f8037aa96..5445739927c75 100644 --- a/blog/Database-Dissection-How-Fast-Data-Queries-Are-Implemented.md +++ b/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 diff --git a/blog/Database-in-Fintech-How-to-Support-ten-thousand-Dashboards-Without-Causing-a-Mess.md b/blog/Database-in-Fintech-How-to-Support-ten-thousand-Dashboards-Without-Causing-a-Mess.md index d64048d44c39e..df3d331a41e9a 100644 --- a/blog/Database-in-Fintech-How-to-Support-ten-thousand-Dashboards-Without-Causing-a-Mess.md +++ b/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). diff --git a/blog/For-Entry-Level-Data-Engineers-How-to-Build-a-Simple-but-Solid-Data-Architecture.md b/blog/For-Entry-Level-Data-Engineers-How-to-Build-a-Simple-but-Solid-Data-Architecture.md index 0619a1f0a6cd7..66ad0bc4aabff 100644 --- a/blog/For-Entry-Level-Data-Engineers-How-to-Build-a-Simple-but-Solid-Data-Architecture.md +++ b/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). diff --git a/blog/Is-Your-Latest-Data-Really-the-Latest-Check-the-Data-Update-Mechanism-of-Your-Database.md b/blog/Is-Your-Latest-Data-Really-the-Latest-Check-the-Data-Update-Mechanism-of-Your-Database.md index 1897fef3b9700..8a1dc95164428 100644 --- a/blog/Is-Your-Latest-Data-Really-the-Latest-Check-the-Data-Update-Mechanism-of-Your-Database.md +++ b/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! diff --git a/blog/Listen-to-That-Poor-BI-Engineer-We-Need-Fast-Joins.md b/blog/Listen-to-That-Poor-BI-Engineer-We-Need-Fast-Joins.md index 994a2765f8d19..595471a2fc9b6 100644 --- a/blog/Listen-to-That-Poor-BI-Engineer-We-Need-Fast-Joins.md +++ b/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) diff --git a/blog/Log-Analysis-How-to-Digest-15-Billion-Logs-Per-Day-and-Keep-Big-Queries-Within-1-Second.md b/blog/Log-Analysis-How-to-Digest-15-Billion-Logs-Per-Day-and-Keep-Big-Queries-Within-1-Second.md index 5ad31859c1d9e..9b9c284f316ae 100644 --- a/blog/Log-Analysis-How-to-Digest-15-Billion-Logs-Per-Day-and-Keep-Big-Queries-Within-1-Second.md +++ b/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. diff --git a/blog/Replacing-Apache-Hive-Elasticsearch-and-PostgreSQL-with-Apache-Doris.md b/blog/Replacing-Apache-Hive-Elasticsearch-and-PostgreSQL-with-Apache-Doris.md index 437b4629e528c..61502e9328076 100644 --- a/blog/Replacing-Apache-Hive-Elasticsearch-and-PostgreSQL-with-Apache-Doris.md +++ b/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) diff --git a/blog/Say-Goodbye-to-OOM-Crashes.md b/blog/Say-Goodbye-to-OOM-Crashes.md index ee7fbc941916c..fc0e5843d8f18 100644 --- a/blog/Say-Goodbye-to-OOM-Crashes.md +++ b/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). diff --git a/blog/Tencent-Data-Engineers-Why-We-Went-from-ClickHouse-to-Apache-Doris.md b/blog/Tencent-Data-Engineers-Why-We-Went-from-ClickHouse-to-Apache-Doris.md index 645c62d26bd3c..4e136dedd4431 100644 --- a/blog/Tencent-Data-Engineers-Why-We-Went-from-ClickHouse-to-Apache-Doris.md +++ b/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) diff --git a/blog/Tiered-Storage-for-Hot-and-Cold-Data-What-Why-and-How.md b/blog/Tiered-Storage-for-Hot-and-Cold-Data-What-Why-and-How.md index 1a6c99f0aac79..2ea76336a1d45 100644 --- a/blog/Tiered-Storage-for-Hot-and-Cold-Data-What-Why-and-How.md +++ b/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. diff --git a/blog/Understanding-Data-Compaction-in-3-Minutes.md b/blog/Understanding-Data-Compaction-in-3-Minutes.md index 91b245559987a..1f4e2ebe4a818 100644 --- a/blog/Understanding-Data-Compaction-in-3-Minutes.md +++ b/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. diff --git a/blog/a-fast-secure-high-available-real-time-data-warehouse-based-on-apache-doris.md b/blog/a-fast-secure-high-available-real-time-data-warehouse-based-on-apache-doris.md index fb99a85e7062e..708d4b70eef9b 100644 --- a/blog/a-fast-secure-high-available-real-time-data-warehouse-based-on-apache-doris.md +++ b/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. \ No newline at end of file +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. \ No newline at end of file diff --git a/blog/a-financial-anti-fraud-solution-based-on-the-apache-doris-data-warehouse.md b/blog/a-financial-anti-fraud-solution-based-on-the-apache-doris-data-warehouse.md index 6d9eff38a6397..721e4c1812b71 100644 --- a/blog/a-financial-anti-fraud-solution-based-on-the-apache-doris-data-warehouse.md +++ b/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. \ No newline at end of file +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. \ No newline at end of file diff --git a/blog/ai-unicorn-minimax-from-loki-and-built-a-pb-scale-logging-system-with-doris.md b/blog/ai-unicorn-minimax-from-loki-and-built-a-pb-scale-logging-system-with-doris.md index 0768d97e9453d..2b23dd5656d9d 100644 --- a/blog/ai-unicorn-minimax-from-loki-and-built-a-pb-scale-logging-system-with-doris.md +++ b/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). \ No newline at end of file +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). \ No newline at end of file diff --git a/blog/apache-doris-for-log-and-time-series-data-analysis-in-netease.md b/blog/apache-doris-for-log-and-time-series-data-analysis-in-netease.md index a1577749fb859..45bf64ff5fd7b 100644 --- a/blog/apache-doris-for-log-and-time-series-data-analysis-in-netease.md +++ b/blog/apache-doris-for-log-and-time-series-data-analysis-in-netease.md @@ -161,7 +161,7 @@ streaming_load_json_max_mb=250 During peak times, the data platform is undertaking up to 1 million TPS and a writing throughput of 1GB/s. This is demanding for the system. Meanwhile, at peak time, a large number of concurrent write operations are loading data into lots of tables, but each individual write operation only involves a small amount of data. Thus, it takes a long time to accumulate a batch, which is contradictory to the data freshness requirement from the query side. -As a result, the data platform was bottlenecked by data backlogs in Apache Kafka. NetEase adopts the [Stream Load](https://doris.apache.org/docs/2.0/data-operate/import/stream-load-manual) method to ingest data from Kafka to Doris. So the key was to accelerate Stream Load. After talking to the [Apache Doris developers](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw), NetEase adopted two optimizations for their log and time series data analysis: +As a result, the data platform was bottlenecked by data backlogs in Apache Kafka. NetEase adopts the [Stream Load](https://doris.apache.org/docs/2.0/data-operate/import/stream-load-manual) method to ingest data from Kafka to Doris. So the key was to accelerate Stream Load. After talking to the [Apache Doris developers](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ), NetEase adopted two optimizations for their log and time series data analysis: - **Single replica data loading**: Load one data replica and pull data from it to generate more replicas. This avoids the overhead of ranking and creating indexes for multiple replicas. @@ -234,4 +234,4 @@ If you want to enable `support_phrase` for existing tables that have already bee ## Conclusion -Apache Doris supports the log and time series data analytic workloads of NetEase with higher query performance and less storage consumption. Beyond these, Apache Doris has other capabilities such as data lake analysis since it is designed as an all-in-one big data analytic platform. If you want a quick evaluation of whether Doris is right for your use case, come talk to the Doris makers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw). \ No newline at end of file +Apache Doris supports the log and time series data analytic workloads of NetEase with higher query performance and less storage consumption. Beyond these, Apache Doris has other capabilities such as data lake analysis since it is designed as an all-in-one big data analytic platform. If you want a quick evaluation of whether Doris is right for your use case, come talk to the Doris makers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ). \ No newline at end of file diff --git a/blog/apache-doris-speeds-up-data-reporting-tagging-and-data-lake-analytics.md b/blog/apache-doris-speeds-up-data-reporting-tagging-and-data-lake-analytics.md index f0dad7397071d..b48afc49f342a 100644 --- a/blog/apache-doris-speeds-up-data-reporting-tagging-and-data-lake-analytics.md +++ b/blog/apache-doris-speeds-up-data-reporting-tagging-and-data-lake-analytics.md @@ -90,6 +90,6 @@ For easier deployment, they have also optimized their Deploy on Yarn process via ## Conclusion -For data reporting and customer tagging, Apache Doris smoothens data ingestion and merging steps, and delivers high query performance based on its own design and functionality. For data lake analytics, the user improves resource efficiency by elastic scaling of clusters using the Compute Node. Along their journey with Apache Doris, they have also developed a data ingestion task prioritizing mechanism and contributed it to the Doris project. A gesture to facilitate their use case ends up benefiting the whole [open source community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw). This is a great example of open-source products thriving on user involvement. +For data reporting and customer tagging, Apache Doris smoothens data ingestion and merging steps, and delivers high query performance based on its own design and functionality. For data lake analytics, the user improves resource efficiency by elastic scaling of clusters using the Compute Node. Along their journey with Apache Doris, they have also developed a data ingestion task prioritizing mechanism and contributed it to the Doris project. A gesture to facilitate their use case ends up benefiting the whole [open source community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ). This is a great example of open-source products thriving on user involvement. Check Apache Doris [repo](https://github.com/apache/doris) on GitHub \ No newline at end of file diff --git a/blog/apache-doris-summit-asia-2023-what-can-you-expect-from-apache-doris-as-a-data-warehouse.md b/blog/apache-doris-summit-asia-2023-what-can-you-expect-from-apache-doris-as-a-data-warehouse.md index d5a0924b6f02d..42285c9e38ba7 100644 --- a/blog/apache-doris-summit-asia-2023-what-can-you-expect-from-apache-doris-as-a-data-warehouse.md +++ b/blog/apache-doris-summit-asia-2023-what-can-you-expect-from-apache-doris-as-a-data-warehouse.md @@ -174,4 +174,4 @@ From the self-developed pre-aggregation storage engine, materialized views, and - We want to keep inspiring the data world by presenting more use cases. - We want to provide more and better choices for users by collaborating with partners along the data pipeline and cloud service providers. -By choosing Apache Doris, you choose to stay in the heartbeat of innovation. The [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) awaits newcomers. +By choosing Apache Doris, you choose to stay in the heartbeat of innovation. The [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) awaits newcomers. diff --git a/blog/arrow-flight-sql-in-apache-doris-for-10x-faster-data-transfer.md b/blog/arrow-flight-sql-in-apache-doris-for-10x-faster-data-transfer.md index 3b15e9bdbcdff..51e3c1f4cf48f 100644 --- a/blog/arrow-flight-sql-in-apache-doris-for-10x-faster-data-transfer.md +++ b/blog/arrow-flight-sql-in-apache-doris-for-10x-faster-data-transfer.md @@ -461,4 +461,4 @@ For Spark users, apart from connecting to Flight SQL Server using JDBC and JAVA, A number of enterprise users of Doris has tried loading data from Doris to Python, Spark, and Flink using Arrow Flight SQL and enjoyed much faster data reading speed. In the future, we plan to include the support for Arrow Flight SQL in data writing, too. By then, most systems built with mainstream programming languages will be able to read and write data from/to Apache Doris by an ADBC client. That's high-speed data interaction which opens up numerous possibilities. On our to-do list, we also envision leveraging Arrow Flight to implement parallel data reading by multiple backends and facilitate federated queries across Doris and Spark. -Download [Apache Doris 2.1](https://doris.apache.org/download/) and get a taste of 100 times faster data transfer powered by Arrow Flight SQL. If you need assistance, come find us in the [Apache Doris developer and user community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw). +Download [Apache Doris 2.1](https://doris.apache.org/download/) and get a taste of 100 times faster data transfer powered by Arrow Flight SQL. If you need assistance, come find us in the [Apache Doris developer and user community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ). diff --git a/blog/auto-increment-columns-in-databases.md b/blog/auto-increment-columns-in-databases.md index 86ff2926afa85..6ae0e8ff347a5 100644 --- a/blog/auto-increment-columns-in-databases.md +++ b/blog/auto-increment-columns-in-databases.md @@ -408,5 +408,5 @@ Attention is required regarding: ## Conclusion -AUTO_INCREMENT brings higher stability and reliability for Doris in large-scale data processing. If it sounds like something you need, download [Apache Doris](https://doris.apache.org/download/) and try it out. For issues you come across along the way, join us in the [Apache Doris developer and user community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) and we are happy to help. +AUTO_INCREMENT brings higher stability and reliability for Doris in large-scale data processing. If it sounds like something you need, download [Apache Doris](https://doris.apache.org/download/) and try it out. For issues you come across along the way, join us in the [Apache Doris developer and user community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) and we are happy to help. diff --git a/blog/auto-partition-in-apache-doris.md b/blog/auto-partition-in-apache-doris.md index 49e327cb3d29d..c581c77c7f597 100644 --- a/blog/auto-partition-in-apache-doris.md +++ b/blog/auto-partition-in-apache-doris.md @@ -479,4 +479,4 @@ Plans for Auto Partition by LIST: - Allow merging multiple values into the same partition based on specific rules. -Join [Apache Doris open-source community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2gmq5o30h-455W226d79zP3L96ZhXIoQ) for more information and further guidance. \ No newline at end of file +Join [Apache Doris open-source community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) for more information and further guidance. \ No newline at end of file diff --git a/blog/breaking-down-data-silos-with-an-apache-doris-based-cdp.md b/blog/breaking-down-data-silos-with-an-apache-doris-based-cdp.md index 78ae49636ff1a..6882ef1bbe676 100644 --- a/blog/breaking-down-data-silos-with-an-apache-doris-based-cdp.md +++ b/blog/breaking-down-data-silos-with-an-apache-doris-based-cdp.md @@ -130,4 +130,4 @@ In Apache Doris, this is implemented by the BITMAP functions: `BITMAP_CONTAINS` ## Conclusion -From CDP 1.0 to CDP 2.0, the insurance company adopts Apache Doris, a unified data warehouse, to replace Spark+Impala+HBase+NebulaGraph. That increases their data processing efficiency by breaking down the data silos and streamlining data processing pipelines. In CDP 3.0 to come, they want to group their customer by combining real-time tags and offline tags for more diversified and flexible analysis. The [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) and the [VeloDB](https://www.velodb.io) team will continue to be a supporting partner during this upgrade. \ No newline at end of file +From CDP 1.0 to CDP 2.0, the insurance company adopts Apache Doris, a unified data warehouse, to replace Spark+Impala+HBase+NebulaGraph. That increases their data processing efficiency by breaking down the data silos and streamlining data processing pipelines. In CDP 3.0 to come, they want to group their customer by combining real-time tags and offline tags for more diversified and flexible analysis. The [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) and the [VeloDB](https://www.velodb.io) team will continue to be a supporting partner during this upgrade. \ No newline at end of file diff --git a/blog/cross-cluster-replication-for-read-write.md b/blog/cross-cluster-replication-for-read-write.md index d451dc7b2bdc4..abc2ffbb40782 100644 --- a/blog/cross-cluster-replication-for-read-write.md +++ b/blog/cross-cluster-replication-for-read-write.md @@ -112,6 +112,6 @@ In the grocery store brand's case, they need to synchronize a few tables from th Using CCR in Apache Doris, the grocery store brand separates reading and writing workloads into different clusters and thus improves overall system stability. This solution delivers a real-time data synchronization latency of about 1 second. To further ensure normal functioning, it has a real-time monitoring and alerting mechanism so any issue will be notified and attended to instantly, and a contingency plan to guarantee uninterrupted query services. It also supports partition-based data synchronization (e.g. `ALTER TABLE tbl1 REPLACE PARTITION`). With demonstrated effectiveness of CCR, they are planning to replicate more of their data assets for efficient and secure data usage. -CCR is also applicable when you need to build multiple data centers or derive a test dataset from your production environment. For further guidance on CCR, join the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw). +CCR is also applicable when you need to build multiple data centers or derive a test dataset from your production environment. For further guidance on CCR, join the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ). \ No newline at end of file diff --git a/blog/data-analysis-for-live-streaming-what-happens-in-real-time-is-analyzed-in-real-time.md b/blog/data-analysis-for-live-streaming-what-happens-in-real-time-is-analyzed-in-real-time.md index ad45220081f1a..a96f20035c0f3 100644 --- a/blog/data-analysis-for-live-streaming-what-happens-in-real-time-is-analyzed-in-real-time.md +++ b/blog/data-analysis-for-live-streaming-what-happens-in-real-time-is-analyzed-in-real-time.md @@ -214,7 +214,7 @@ limit 1,10; ## Conclusion -Data analysis in live streaming is challenging for the underlying database, but it is also where the key competitiveness of Apache Doris comes to play. First of all, Apache Doris can handle most data processing workloads, so platform builders don't have to worry about putting many components together and consequential maintenance issues. Secondly, it has a lot of query-accelerating features, including but not limited to indexes. After tackling the speed issues, the [Apache Doris developer community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) has been exploring its boundaries, such as introducing a more efficient cost-based query optimizer in version 2.0 and inverted index for text searches, fuzzy queries, and range queries. These features are embraced by the live streaming service provider as they are actively testing them and planning to transfer their log analytic workloads to Apache Doris, too. +Data analysis in live streaming is challenging for the underlying database, but it is also where the key competitiveness of Apache Doris comes to play. First of all, Apache Doris can handle most data processing workloads, so platform builders don't have to worry about putting many components together and consequential maintenance issues. Secondly, it has a lot of query-accelerating features, including but not limited to indexes. After tackling the speed issues, the [Apache Doris developer community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) has been exploring its boundaries, such as introducing a more efficient cost-based query optimizer in version 2.0 and inverted index for text searches, fuzzy queries, and range queries. These features are embraced by the live streaming service provider as they are actively testing them and planning to transfer their log analytic workloads to Apache Doris, too. diff --git a/blog/empowering-cyber-security-by-enabling-seven-times-faster-log-analysis.md b/blog/empowering-cyber-security-by-enabling-seven-times-faster-log-analysis.md index f4500f7dbb62d..c1aa2d471e62f 100644 --- a/blog/empowering-cyber-security-by-enabling-seven-times-faster-log-analysis.md +++ b/blog/empowering-cyber-security-by-enabling-seven-times-faster-log-analysis.md @@ -140,4 +140,4 @@ Apart from cluster management, Doris Manager provides a visualized WebUI for log ![doris-manager-webui-showcase](/images/doris-manager-webui-showcase.png) -After a month-long trial run, they officially replaced their old LSAS with the Apache Doris-based system for production, and achieved great results as they expected. Now, they ingest their 100s of billions of new logs every day via the [Routine Load](https://doris.apache.org/docs/dev/data-operate/import/import-way/routine-load-manual/) method at a speed 3 times as fast as before. Among the 7-time overall query performance increase, they benefit from a speedup of over 20 times in full-text searches. And they enjoy easier maintenance and interactive analysis. Their next step is to expand the coverage of JSON data type and delve into semi-structured data analysis. Luckily, the upcoming Apache Doris 2.1 will provide more schema-free support. It will have a new Variant data type, support JSON data of any structures, and allow for flexible changes in field numbers and field types. Relevant updates will be released on the [Apache Doris website](https://doris.apache.org/) and the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw). +After a month-long trial run, they officially replaced their old LSAS with the Apache Doris-based system for production, and achieved great results as they expected. Now, they ingest their 100s of billions of new logs every day via the [Routine Load](https://doris.apache.org/docs/dev/data-operate/import/import-way/routine-load-manual/) method at a speed 3 times as fast as before. Among the 7-time overall query performance increase, they benefit from a speedup of over 20 times in full-text searches. And they enjoy easier maintenance and interactive analysis. Their next step is to expand the coverage of JSON data type and delve into semi-structured data analysis. Luckily, the upcoming Apache Doris 2.1 will provide more schema-free support. It will have a new Variant data type, support JSON data of any structures, and allow for flexible changes in field numbers and field types. Relevant updates will be released on the [Apache Doris website](https://doris.apache.org/) and the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ). diff --git a/blog/evolution-of-the-apache-doris-execution-engine.md b/blog/evolution-of-the-apache-doris-execution-engine.md index 48ee6b571f41c..77827f6ab27b5 100644 --- a/blog/evolution-of-the-apache-doris-execution-engine.md +++ b/blog/evolution-of-the-apache-doris-execution-engine.md @@ -230,4 +230,4 @@ From the Volcano Model to the Pipeline Execution Engine, Apache Doris 2.0.0 has What's next in our roadmap is to support spilling data to disk in PipelineX to further improve query speed and system reliability. We also plan to advance further in terms of automation, such as self-adaptive concurrency and auto execution plan optimization, accompanied by NUMA technologies to harvest better performance from hardware resources. -If you want to talk to the amazing Doris developers who lead these changes, you are more than welcome to join the [Apache Doris](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2gmq5o30h-455W226d79zP3L96ZhXIoQ) community. \ No newline at end of file +If you want to talk to the amazing Doris developers who lead these changes, you are more than welcome to join the [Apache Doris](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) community. \ No newline at end of file diff --git a/blog/from-elasticsearch-to-apache-doris-upgrading-an-observability-platform.md b/blog/from-elasticsearch-to-apache-doris-upgrading-an-observability-platform.md index 81d9cd7f26952..d1568fc70372d 100644 --- a/blog/from-elasticsearch-to-apache-doris-upgrading-an-observability-platform.md +++ b/blog/from-elasticsearch-to-apache-doris-upgrading-an-observability-platform.md @@ -161,8 +161,8 @@ Currently, the Variant type requires extra type assertion, we plan to automate t ## Conclusion -GuanceDB's transition from Elasticsearch to Apache Doris showcases a big stride in improving data processing speed and reducing costs. For these purposes, Apache Doris has optimized itself in the two major aspects of data processing: data integration and data analysis. It has expanded its schemaless support to flexibly accommodate more data types, introduced features like inverted index and tiered storage to enable faster and more cost-effective queries. Evolution is an ongoing process. Apache Doris has never stopped improving itself. We have a lot of new features under development and the Doris [community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) embrace any input and feedback. +GuanceDB's transition from Elasticsearch to Apache Doris showcases a big stride in improving data processing speed and reducing costs. For these purposes, Apache Doris has optimized itself in the two major aspects of data processing: data integration and data analysis. It has expanded its schemaless support to flexibly accommodate more data types, introduced features like inverted index and tiered storage to enable faster and more cost-effective queries. Evolution is an ongoing process. Apache Doris has never stopped improving itself. We have a lot of new features under development and the Doris [community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) embrace any input and feedback. Check Apache Doris GitHub [repo](https://github.com/apache/doris) -Find Apache Doris makers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) +Find Apache Doris makers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) diff --git a/blog/from-presto-trino-clickhouse-and-hive-to-apache-doris-sql-convertor-for-easy-migration.md b/blog/from-presto-trino-clickhouse-and-hive-to-apache-doris-sql-convertor-for-easy-migration.md index c8adf0bb55b2f..4c69cbdab0ca2 100644 --- a/blog/from-presto-trino-clickhouse-and-hive-to-apache-doris-sql-convertor-for-easy-migration.md +++ b/blog/from-presto-trino-clickhouse-and-hive-to-apache-doris-sql-convertor-for-easy-migration.md @@ -174,7 +174,7 @@ After deployment, you can access the service by `ip:8080` via your local browser 2. The Doris SQL Convertor supports 239 UNION ALL conversions at most. ::: -Join the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) to seek guidance from the Doris makers or provide your feedback! +Join the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) to seek guidance from the Doris makers or provide your feedback! diff --git a/blog/how-big-data-is-saving-lives-in-real-time-iov-data-analytics-helps-prevent-accidents.md b/blog/how-big-data-is-saving-lives-in-real-time-iov-data-analytics-helps-prevent-accidents.md index ad6893e5da899..d2e478bd7c0d5 100644 --- a/blog/how-big-data-is-saving-lives-in-real-time-iov-data-analytics-helps-prevent-accidents.md +++ b/blog/how-big-data-is-saving-lives-in-real-time-iov-data-analytics-helps-prevent-accidents.md @@ -110,5 +110,5 @@ Building a data platform to suit your use case is not easy, I hope this post hel Apache Doris [GitHub repo](https://github.com/apache/doris) -Find Apache Doris makers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) +Find Apache Doris makers on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) diff --git a/blog/introduction-to-apache-doris-a-next-generation-real-time-data-warehouse.md b/blog/introduction-to-apache-doris-a-next-generation-real-time-data-warehouse.md index 6e7094e4c60bb..8e64bc02dafa8 100644 --- a/blog/introduction-to-apache-doris-a-next-generation-real-time-data-warehouse.md +++ b/blog/introduction-to-apache-doris-a-next-generation-real-time-data-warehouse.md @@ -171,4 +171,4 @@ Roughly speaking, for a data asset consisting of 80% cold data, tiered storage w ## The Apache Doris Community -This is an overview of Apache Doris, an open-source real-time data warehouse. It is actively evolving with an agile release schedule, and the [community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) embraces any questions, ideas, and feedback. +This is an overview of Apache Doris, an open-source real-time data warehouse. It is actively evolving with an agile release schedule, and the [community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) embraces any questions, ideas, and feedback. diff --git a/blog/inverted-index-accelerates-text-searches-by-40-time-apache-doris.md b/blog/inverted-index-accelerates-text-searches-by-40-time-apache-doris.md index 29031cc78da68..7a795c7976a36 100644 --- a/blog/inverted-index-accelerates-text-searches-by-40-time-apache-doris.md +++ b/blog/inverted-index-accelerates-text-searches-by-40-time-apache-doris.md @@ -508,4 +508,4 @@ Inverted index has been available in Apache Doris for almost a year and stood th - **Self-defined tokenization**: provides a user-defined tokenizer to fit in different use cases. - **More data types**: Users will be able to create inverted index for complex data types including Array and Map. -If you encounter any issues while trying it out in Apache Doris or would like to know more details, join our [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw) community and talk to us! +If you encounter any issues while trying it out in Apache Doris or would like to know more details, join our [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) community and talk to us! diff --git a/blog/job-scheduler-for-task-automation.md b/blog/job-scheduler-for-task-automation.md index b4575520ba9de..9384dee73f0cc 100644 --- a/blog/job-scheduler-for-task-automation.md +++ b/blog/job-scheduler-for-task-automation.md @@ -202,7 +202,7 @@ In addition, for transactional tasks, the Job Scheduler can ensure data consiste The Doris Job Scheduler is a Swiss Army Knife. It is not only useful in ETL and data lake analytics as we mentioned, but also critical for the implementation of [asynchronous materialized views](https://doris.apache.org/docs/query/view-materialized-view/async-materialized-view). An asynchronous materialized view is a pre-computed result set. Unlike normal materialized views, it can be built on multiple tables. Thus, as you can imagine, changes in any of the source tables will lead to the need for updates in the asynchronous materialized view. That's why we apply the job scheduling mechanism for periodic data refreshing in asynchronous materialized views, which is low-maintenance and also ensures data consistency. -Where are we going with the Doris Job Scheduler? The [Apache Doris developer community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2gmq5o30h-455W226d79zP3L96ZhXIoQ) is looking at: +Where are we going with the Doris Job Scheduler? The [Apache Doris developer community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) is looking at: - Displaying the distribution of tasks executed in different time slots on the WebUI. diff --git a/blog/log-analysis-elasticsearch-vs-apache-doris.md b/blog/log-analysis-elasticsearch-vs-apache-doris.md index 61b4df50de8a9..43362ef3f87ea 100644 --- a/blog/log-analysis-elasticsearch-vs-apache-doris.md +++ b/blog/log-analysis-elasticsearch-vs-apache-doris.md @@ -351,4 +351,4 @@ SELECT * FROM log_table WHERE request MATCH_ALL 'image faq' ORDER BY ts DESC LIM If you are looking for an efficient log analytic solution, Apache Doris is friendly to anyone equipped with SQL knowledge; if you find friction with the ELK stack, try Apache Doris provides better schema-free support, enables faster data writing and queries, and brings much less storage burden. -But we won't stop here. We are going to provide more features to facilitate log analysis. We plan to add more complicated data types to inverted index, and support BKD index to make Apache Doris a fit for geo data analysis. We also plan to expand capabilities in semi-structured data analysis, such as working on the complex data types (Array, Map, Struct, JSON) and high-performance string matching algorithm. And we welcome any [user feedback and development advice](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw). +But we won't stop here. We are going to provide more features to facilitate log analysis. We plan to add more complicated data types to inverted index, and support BKD index to make Apache Doris a fit for geo data analysis. We also plan to expand capabilities in semi-structured data analysis, such as working on the complex data types (Array, Map, Struct, JSON) and high-performance string matching algorithm. And we welcome any [user feedback and development advice](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ). diff --git a/blog/migrate-lakehouse-from-bigquery-to-doris.md b/blog/migrate-lakehouse-from-bigquery-to-doris.md index ce3207d72f967..82c201e613e8f 100644 --- a/blog/migrate-lakehouse-from-bigquery-to-doris.md +++ b/blog/migrate-lakehouse-from-bigquery-to-doris.md @@ -32,7 +32,7 @@ under the License. --> :::tip Special Thanks -The Apache Doris community would like to extend our gratitude to Dien for sharing his valuable experience and best practices in migrating from BigQuery to Apache Doris in this insightful and informative article. Dien is also an active member of the Apache Doris open-source [community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2gmq5o30h-455W226d79zP3L96ZhXIoQ), and we are truly appreciative of his ongoing support. +The Apache Doris community would like to extend our gratitude to Dien for sharing his valuable experience and best practices in migrating from BigQuery to Apache Doris in this insightful and informative article. Dien is also an active member of the Apache Doris open-source [community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ), and we are truly appreciative of his ongoing support. ::: This article is written by [Dien, Tran Thanh](https://www.linkedin.com/in/dien-tran-thanh-19275b14a/) and originally posted on [Medium](https://dientt.medium.com/migrate-data-platform-từ-bigquery-sang-apache-doris-giúp-giảm-chi-phí-từ-6-000-xuống-còn-1-500-40ba9b22967e). @@ -183,7 +183,7 @@ The implementation was carried out by 1 Data Engineer, 1 Software Engineer, and - Data replication between nodes can sometimes lose synchronization due to network issues or other reasons, and the automatic replication retry mechanism may not be successful. In such cases, it is necessary to set up an external worker mechanism for automatic handling (Doris manages a storage unit called Tablet. The metadata on each node records the ETL data into a specific table, and the metadata version gets updated. Doris provides a SQL-based method to handle desynchronization). -- New versions may occasionally have bugs, so it's recommended to check if the [community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2gmq5o30h-455W226d79zP3L96ZhXIoQ) has reported any issues before deciding to upgrade. +- New versions may occasionally have bugs, so it's recommended to check if the [community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) has reported any issues before deciding to upgrade. - Limited support for processing Vietnamese text, with the full-text search feature not performing well in Vietnamese. diff --git a/blog/migrating-from-clickhouse-to-apache-doris-what-happened.md b/blog/migrating-from-clickhouse-to-apache-doris-what-happened.md index ff234ecbcaa3e..d7361a8ce5532 100644 --- a/blog/migrating-from-clickhouse-to-apache-doris-what-happened.md +++ b/blog/migrating-from-clickhouse-to-apache-doris-what-happened.md @@ -160,4 +160,4 @@ In terms of CPU and memory consumption, Apache Doris maintained stable cluster l ## Future Directions -As the migration goes on, the user works closely with the [Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw), and their feedback has contributed to the making of [Apache Doris 2.0.0](https://doris.apache.org/docs/dev/releasenotes/release-2.0.0/). We will continue assisting them in their migration from Kylin and Druid to Doris, and we look forward to see their Doris-based unified data platform come into being. +As the migration goes on, the user works closely with the [Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ), and their feedback has contributed to the making of [Apache Doris 2.0.0](https://doris.apache.org/docs/dev/releasenotes/release-2.0.0/). We will continue assisting them in their migration from Kylin and Druid to Doris, and we look forward to see their Doris-based unified data platform come into being. diff --git a/blog/multi-tenant-workload-isolation-in-apache-doris.md b/blog/multi-tenant-workload-isolation-in-apache-doris.md index f65ed34c8e9cd..2721e0f2d46f4 100644 --- a/blog/multi-tenant-workload-isolation-in-apache-doris.md +++ b/blog/multi-tenant-workload-isolation-in-apache-doris.md @@ -222,4 +222,4 @@ In future releases, we will keep improving user experience of the Workload Group - The main idea of Resource Tag is to group the BE nodes, while that of Workload Group is to further divide the resources of a single BE node. For users to grasp these ideas, they need to learn about the concept of BE nodes in Doris first. However, from an operational perspective, users only need to understand the resource consumption percentage of each of their workloads and what priority they should have when cluster load is saturated. Thus, we will try and figure out a way to flatten the learning curve for users, such as keeping the concept of BE nodes in the black box. -For further assistance on workload isolation in Apache Doris, join the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw). \ No newline at end of file +For further assistance on workload isolation in Apache Doris, join the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ). \ No newline at end of file diff --git a/blog/release-note-2.0.0.md b/blog/release-note-2.0.0.md index a5380d4bf505a..00fd19a1eebf1 100644 --- a/blog/release-note-2.0.0.md +++ b/blog/release-note-2.0.0.md @@ -235,5 +235,5 @@ This feature allows for higher availability of data, read/write workload separat To make Apache Doris 2.0.0 production-ready, we invited hundreds of enterprise users to engage in the testing and optimized it for better performance, stability, and usability. In the next phase, we will continue responding to user needs with agile release planning. We plan to launch 2.0.1 in late August and 2.0.2 in September, as we keep fixing bugs and adding new features. We also plan to release an early version of 2.1 in September to bring a few long-requested capabilities to you. For example, in Doris 2.1, the Variant data type will better serve the schema-free analytic needs of semi-structured data; the multi-table materialized views will be able to simplify the data scheduling and processing link while speeding up queries; more and neater data ingestion methods will be added and nested composite data types will be realized. -If you have any questions or ideas when investigating, testing, and deploying Apache Doris, please find us on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw). Our developers will be happy to hear them and provide targeted support. +If you have any questions or ideas when investigating, testing, and deploying Apache Doris, please find us on [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ). Our developers will be happy to hear them and provide targeted support. diff --git a/blog/release-note-2.1.0.md b/blog/release-note-2.1.0.md index 684f3235538ef..0b41ffd847c8e 100644 --- a/blog/release-note-2.1.0.md +++ b/blog/release-note-2.1.0.md @@ -40,7 +40,7 @@ Dear Apache Doris community, we are thrilled to announce the advent of Apache Do - **Better workload management**: optimizations of the Workload Group mechanism for higher performance stability and the display of SQL resource consumption in the runtime. -We appreciate the 237 contributors who made nearly 6000 commits in total to the Apache Doris project, and the nearly 100 enterprise users who provided valuable feedback. We will keep aiming for the stars with our agile release planning, and we appreciate your feedback in the [Apache Doris developer and user community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw). +We appreciate the 237 contributors who made nearly 6000 commits in total to the Apache Doris project, and the nearly 100 enterprise users who provided valuable feedback. We will keep aiming for the stars with our agile release planning, and we appreciate your feedback in the [Apache Doris developer and user community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ). **Download from GitHub**: https://github.com/apache/doris/releases diff --git a/blog/release-note-3.0.0.md b/blog/release-note-3.0.0.md index 03de9a1565b87..130dbe0efe873 100644 --- a/blog/release-note-3.0.0.md +++ b/blog/release-note-3.0.0.md @@ -463,7 +463,7 @@ Before the official release of version 3.0, the compute-storage decoupled mode o We highly recommend users with compute-storage decoupling needs to download version 3.0 and experience it firsthand. -Going forward, we will accelerate our release iteration cycle to deliver a more stable version experience for all users. Feel free to join us in the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2gmq5o30h-455W226d79zP3L96ZhXIoQ) and engage directly with the core developers. +Going forward, we will accelerate our release iteration cycle to deliver a more stable version experience for all users. Feel free to join us in the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) and engage directly with the core developers. ## Credits diff --git a/blog/tencent-music-migrate-elasticsearch-to-doris.md b/blog/tencent-music-migrate-elasticsearch-to-doris.md index 4a09a72aa2e47..0a14dd9b5bb9c 100644 --- a/blog/tencent-music-migrate-elasticsearch-to-doris.md +++ b/blog/tencent-music-migrate-elasticsearch-to-doris.md @@ -186,4 +186,4 @@ The migration from Elasticsearch to Apache Doris has yielded impressive gains. W By replacing its Elasticsearch cluster with Doris, TME has unified its content library's search and analytics engines into a single, streamlined platform. The system now supports complex custom tag-based segmentation with sub-second response. The next-phase plan of TME is to explore broader use cases of Apache Doris and prepare to adopt the [compute-storage decoupled mode](https://doris.apache.org/docs/3.0/compute-storage-decoupled/overview) to drive even greater cost efficiency. -For direct communication, real-world insights, and best practices, join [#elasticsearch-to-doris](https://apachedoriscommunity.slack.com/archives/C08CQKX20R5) channel in the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2gmq5o30h-455W226d79zP3L96ZhXIoQ). \ No newline at end of file +For direct communication, real-world insights, and best practices, join [#elasticsearch-to-doris](https://apachedoriscommunity.slack.com/archives/C08CQKX20R5) channel in the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ). \ No newline at end of file diff --git a/blog/variant-in-apache-doris-2.1.md b/blog/variant-in-apache-doris-2.1.md index 55b1f5bd50130..6dc70f030b5b1 100644 --- a/blog/variant-in-apache-doris-2.1.md +++ b/blog/variant-in-apache-doris-2.1.md @@ -428,4 +428,4 @@ The Doris-based solution also delivers lower CPU usage in data writing and highe The Variant data type has stood the test of many users before the official release of Apache Doris 2.1.0. It is production-available now. In the future, we plan to realize more lightweight changes for Variant to facilitate data modeling. -For more information about Variant and guides on how to build a semi-structured data analytics solution for your case, come talk to the [Apache Doris developer team](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2unfw3a3q-MtjGX4pAd8bCGC1UV0sKcw). \ No newline at end of file +For more information about Variant and guides on how to build a semi-structured data analytics solution for your case, come talk to the [Apache Doris developer team](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ). \ No newline at end of file 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 8d414e4b727b3..53e3e9f2a37ea 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 @@ -397,7 +397,7 @@ By 2023, the value of Doris with inverted indexes became increasingly evident, l The growth momentum has continued, and as of 2024, we are experiencing rapid expansion, with over 100 companies now leveraging Doris to replace Elasticsearch. -Looking ahead, I am very much looking forward to what 2025 will bring. This progress, advancing from the ground up to such significant milestones, has been made possible by the incredible support from the Doris community users and developers. We encourage everyone to join the [Apache Doris Slack community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2zlwvmzon-NIb2rz50rIhcflGAFpYjDQ) and join the dedicated channel [#elasticsearch-to-doris](https://apachedoriscommunity.slack.com/archives/C08CQKX20R5), where you can receive technical assistance, stay updated with the latest news about Doris, and engage with more Doris developers and users. +Looking ahead, I am very much looking forward to what 2025 will bring. This progress, advancing from the ground up to such significant milestones, has been made possible by the incredible support from the Doris community users and developers. We encourage everyone to join the [Apache Doris Slack community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) and join the dedicated channel [#elasticsearch-to-doris](https://apachedoriscommunity.slack.com/archives/C08CQKX20R5), where you can receive technical assistance, stay updated with the latest news about Doris, and engage with more Doris developers and users. More on Apache Doris: diff --git a/community/how-to-contribute/contribute-to-doris.md b/community/how-to-contribute/contribute-to-doris.md index c41e799553ca0..9bf30713da0de 100644 --- a/community/how-to-contribute/contribute-to-doris.md +++ b/community/how-to-contribute/contribute-to-doris.md @@ -44,7 +44,7 @@ For the first time in Doris community, you can: * Join Doris Wechat Group (add WeChat-ID: morningman-cmy, note: join Doris Group) and ask questions at any time. -* Enter Doris's [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-35mzao67o-BrpU70FNKPyB6UlgpXf8_w) +* Enter Doris's [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) Learn the development trends of Doris project in time and give your opinions on the topics you are concerned about. diff --git a/community/join-community.md b/community/join-community.md index 30ec4925141dc..231bdcb1ee883 100644 --- a/community/join-community.md +++ b/community/join-community.md @@ -39,7 +39,7 @@ We have graduated from Apache incubator successfully and become an Top-Level Pro Learn our latest techniques, get inspirations from our rich use cases, and see what the community has been up to ! -- Join our heated discussions - 💬 [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-35mzao67o-BrpU70FNKPyB6UlgpXf8_w) 📇 [Github](https://github.com/apache/doris) +- Join our heated discussions - 💬 [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) 📇 [Github](https://github.com/apache/doris) - Use cases and tech insight - 📭 [Twitter](https://twitter.com/doris_apache) diff --git a/docs/releasenotes/v3.0/release-3.0.0.md b/docs/releasenotes/v3.0/release-3.0.0.md index e0694e7b55ed1..93562b051730c 100644 --- a/docs/releasenotes/v3.0/release-3.0.0.md +++ b/docs/releasenotes/v3.0/release-3.0.0.md @@ -441,7 +441,7 @@ Before the official release of version 3.0, the compute-storage decoupled mode o We highly recommend users with compute-storage decoupling needs to download version 3.0 and experience it firsthand. -Going forward, we will accelerate our release iteration cycle to deliver a more stable version experience for all users. Feel free to join us in the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2gmq5o30h-455W226d79zP3L96ZhXIoQ) and engage directly with the core developers. +Going forward, we will accelerate our release iteration cycle to deliver a more stable version experience for all users. Feel free to join us in the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) and engage directly with the core developers. ## Credits diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs-community/current/how-to-contribute/contribute-to-doris.md b/i18n/zh-CN/docusaurus-plugin-content-docs-community/current/how-to-contribute/contribute-to-doris.md index abd294b203bf0..8ac46a5c41d4b 100644 --- a/i18n/zh-CN/docusaurus-plugin-content-docs-community/current/how-to-contribute/contribute-to-doris.md +++ b/i18n/zh-CN/docusaurus-plugin-content-docs-community/current/how-to-contribute/contribute-to-doris.md @@ -43,7 +43,7 @@ under the License. * 加入 Doris 微信群 (加微信号：morningman-cmy, 备注：加入 Doris 群) 随时提问； -* 加入 [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-35mzao67o-BrpU70FNKPyB6UlgpXf8_w); +* 加入 [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ); 通过以上方式及时了解 Doris 项目的开发动态并为您关注的话题发表意见。 diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs-community/current/join-community.md b/i18n/zh-CN/docusaurus-plugin-content-docs-community/current/join-community.md index 04c9b7621f3c4..3aef55e05fbd9 100644 --- a/i18n/zh-CN/docusaurus-plugin-content-docs-community/current/join-community.md +++ b/i18n/zh-CN/docusaurus-plugin-content-docs-community/current/join-community.md @@ -38,7 +38,7 @@ under the License. 一起加入 Apache Doris 社区，了解头部企业如何基于 Apache Doris 构建统一实时数仓，从技术见解获得更多灵感！ -- 加入社群参与讨论 - 💬 [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-35mzao67o-BrpU70FNKPyB6UlgpXf8_w) 📇 [Github](https://github.com/apache/doris) +- 加入社群参与讨论 - 💬 [Slack](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) 📇 [Github](https://github.com/apache/doris) - 用户案例与技术见解 - 📭 [Twitter](https://twitter.com/doris_apache) diff --git a/versioned_docs/version-2.1/releasenotes/v3.0/release-3.0.0.md b/versioned_docs/version-2.1/releasenotes/v3.0/release-3.0.0.md index f938ada281733..c5cbb75c536e4 100644 --- a/versioned_docs/version-2.1/releasenotes/v3.0/release-3.0.0.md +++ b/versioned_docs/version-2.1/releasenotes/v3.0/release-3.0.0.md @@ -441,7 +441,7 @@ Before the official release of version 3.0, the compute-storage decoupled mode o We highly recommend users with compute-storage decoupling needs to download version 3.0 and experience it firsthand. -Going forward, we will accelerate our release iteration cycle to deliver a more stable version experience for all users. Feel free to join us in the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2gmq5o30h-455W226d79zP3L96ZhXIoQ) and engage directly with the core developers. +Going forward, we will accelerate our release iteration cycle to deliver a more stable version experience for all users. Feel free to join us in the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) and engage directly with the core developers. ## Credits diff --git a/versioned_docs/version-3.x/releasenotes/v3.0/release-3.0.0.md b/versioned_docs/version-3.x/releasenotes/v3.0/release-3.0.0.md index 19cb88b848b5a..e87e50a16951f 100644 --- a/versioned_docs/version-3.x/releasenotes/v3.0/release-3.0.0.md +++ b/versioned_docs/version-3.x/releasenotes/v3.0/release-3.0.0.md @@ -441,7 +441,7 @@ Before the official release of version 3.0, the compute-storage decoupled mode o We highly recommend users with compute-storage decoupling needs to download version 3.0 and experience it firsthand. -Going forward, we will accelerate our release iteration cycle to deliver a more stable version experience for all users. Feel free to join us in the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2gmq5o30h-455W226d79zP3L96ZhXIoQ) and engage directly with the core developers. +Going forward, we will accelerate our release iteration cycle to deliver a more stable version experience for all users. Feel free to join us in the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) and engage directly with the core developers. ## Credits diff --git a/versioned_docs/version-4.x/releasenotes/v3.0/release-3.0.0.md b/versioned_docs/version-4.x/releasenotes/v3.0/release-3.0.0.md index e0694e7b55ed1..93562b051730c 100644 --- a/versioned_docs/version-4.x/releasenotes/v3.0/release-3.0.0.md +++ b/versioned_docs/version-4.x/releasenotes/v3.0/release-3.0.0.md @@ -441,7 +441,7 @@ Before the official release of version 3.0, the compute-storage decoupled mode o We highly recommend users with compute-storage decoupling needs to download version 3.0 and experience it firsthand. -Going forward, we will accelerate our release iteration cycle to deliver a more stable version experience for all users. Feel free to join us in the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2gmq5o30h-455W226d79zP3L96ZhXIoQ) and engage directly with the core developers. +Going forward, we will accelerate our release iteration cycle to deliver a more stable version experience for all users. Feel free to join us in the [Apache Doris community](https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-3b8tlr3le-Z~IrrVxkzqniFjhL17d1oQ) and engage directly with the core developers. ## Credits