Merge branch '3.0' of https://github.com/taosdata/TDengine into feat/stream_compression

README.md

@@ -15,19 +15,19 @@
 [![Coverage Status](https://coveralls.io/repos/github/taosdata/TDengine/badge.svg?branch=develop)](https://coveralls.io/github/taosdata/TDengine?branch=develop)
 [![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/4201/badge)](https://bestpractices.coreinfrastructure.org/projects/4201)
 
-English | [简体中文](README-CN.md) | [Lean more about TSDB](https://tdengine.com/tsdb)
+English | [简体中文](README-CN.md) | [Learn more about TSDB](https://tdengine.com/tsdb/)
 
 # What is TDengine？
 
 TDengine is an open source, high-performance, cloud native [time-series database](https://tdengine.com/tsdb/) optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. It enables efficient, real-time data ingestion, processing, and monitoring of TB and even PB scale data per day, generated by billions of sensors and data collectors. TDengine differentiates itself from other time-seires databases with the following advantages:
 
-- **[High-Performance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: TDengine is the only time-series database to solve the high cardinality issue to support billions of data collection points while out performing other time-series databases for data ingestion, querying and data compression.
+- **[High Performance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: TDengine is the only time-series database to solve the high cardinality issue to support billions of data collection points while out performing other time-series databases for data ingestion, querying and data compression.
 
 - **[Simplified Solution](https://tdengine.com/tdengine/simplified-time-series-data-solution/)**: Through built-in caching, stream processing and data subscription features, TDengine provides a simplified solution for time-series data processing. It reduces system design complexity and operation costs significantly.
 
 - **[Cloud Native](https://tdengine.com/tdengine/cloud-native-time-series-database/)**: Through native distributed design, sharding and partitioning, separation of compute and storage, RAFT, support for kubernetes deployment and full observability, TDengine is a cloud native Time-Series Database and can be deployed on public, private or hybrid clouds.
 
-- **[Ease of Use](https://docs.tdengine.com/get-started/docker/)**: For administrators, TDengine significantly reduces the effort to deploy and maintain. For developers, it provides a simple interface, simplified solution and seamless integrations for third party tools. For data users, it gives easy data access. 
+- **[Ease of Use](https://tdengine.com/tdengine/easy-time-series-data-platform/)**: For administrators, TDengine significantly reduces the effort to deploy and maintain. For developers, it provides a simple interface, simplified solution and seamless integrations for third party tools. For data users, it gives easy data access. 
 
 - **[Easy Data Analytics](https://tdengine.com/tdengine/time-series-data-analytics-made-easy/)**: Through super tables, storage and compute separation, data partitioning by time interval, pre-computation and other means, TDengine makes it easy to explore, format, and get access to data in a highly efficient way. 
 
@@ -232,9 +232,9 @@ After building successfully, TDengine can be installed by
 sudo make install
 ```
 
-Users can find more information about directories installed on the system in the [directory and files](https://docs.taosdata.com/reference/directory/) section.
+Users can find more information about directories installed on the system in the [directory and files](https://docs.tdengine.com/reference/directory/) section.
 
-Installing from source code will also configure service management for TDengine.Users can also choose to [install from packages](https://docs.taosdata.com/get-started/package/) for it.
+Installing from source code will also configure service management for TDengine.Users can also choose to [install from packages](https://docs.tdengine.com/get-started/package/) for it.
 
 To start the service after installation, in a terminal, use:
 

cmake/cmake.version

@@ -2,7 +2,7 @@
 IF (DEFINED VERNUMBER)
   SET(TD_VER_NUMBER ${VERNUMBER})
 ELSE ()
-  SET(TD_VER_NUMBER "3.0.1.1")
+  SET(TD_VER_NUMBER "3.0.1.2")
 ENDIF ()
 
 IF (DEFINED VERCOMPATIBLE)

source/dnode/vnode/src/vnd/vnodeSync.c

@@ -676,6 +676,18 @@ static void vnodeLeaderTransfer(struct SSyncFSM *pFsm, const SRpcMsg *pMsg, SFsm
 
 static void vnodeRestoreFinish(struct SSyncFSM *pFsm) {
   SVnode *pVnode = pFsm->data;
+
+  do {
+    int32_t itemSize = tmsgGetQueueSize(&pVnode->msgCb, pVnode->config.vgId, APPLY_QUEUE);
+    if (itemSize == 0) {
+      vInfo("vgId:%d, apply queue is empty, restore finish", pVnode->config.vgId);
+      break;
+    } else {
+      vInfo("vgId:%d, restore not finish since %d items in apply queue", pVnode->config.vgId);
+      taosMsleep(10);
+    }
+  } while (true);
+
   pVnode->restored = true;
   vDebug("vgId:%d, sync restore finished", pVnode->config.vgId);
 }

tests/system-test/fulltest.sh

@@ -35,6 +35,7 @@ python3 ./test.py -f 1-insert/mutil_stage.py
 python3 ./test.py -f 1-insert/table_param_ttl.py -R
 python3 ./test.py -f 1-insert/update_data_muti_rows.py
 python3 ./test.py -f 1-insert/db_tb_name_check.py
+python3 ./test.py -f 1-insert/database_pre_suf.py
 
 python3 ./test.py -f 2-query/abs.py
 python3 ./test.py -f 2-query/abs.py -R