Feature Introduction

• Standard SQLs

  Supports SQL92, SQL99, SQL2003, and SQL2011 standards, GBK and UTF-8 character sets, SQL standard functions and analytic functions, and SQL Procedural Language.

• Database storage management

  Supports tablespaces where different tables can be stored in different locations.

• Primary/standby deployment

  Supports the ACID properties, single-node fault recoveries, primary/standby data synchronization, and primary/standby switchover.

• APIs

  Supports standard JDBC 4.0 and ODBC 3.5 features.

• Management tools

  Provides installation and deployment tools, instance start and stop tools, and backup and restoration tools.

• Security management

  Supports SSL network connections, user permission management, password management, security auditing, and other functions, to ensure data security at the management, application, system, and network layers.

• AI

  Supports parameter self-tuning, slow SQL discovery, single query index recommendation, virtual index, workload index recommendation, database metric collection, forecasting, and exception monitoring; compatible with the MADlib ecosystem and supports high-performance AI algorithms in the library.

New Features

openGauss 1.1.0 is an updated version of openGauss 1.0.0. Features and functions of this version are compatible with those of the previous version. The new features are as follows:

• Supports LIST partition and HASH partition.

  • The list partitioning function divides the key values in the records to be inserted into a table into multiple lists (the lists do not overlap in different partitions) based on a column of the table, and then creates a partition for each list to store the corresponding data.
  • The hash partitioning function uses the internal hash algorithm to divide records to be inserted into a table into partitions based on a column of the table. If you specify the PARTITION parameter when running the CREATE TABLE statement, data in the table will be partitioned.

• Enhances primary/standby HA.

  • Supports the cascaded standby node which replicates logs from the standby node to reduce the service processing pressure of the primary node.
  • Scales the number of standby nodes to 8 nodes.
  • Supports the **catchup2normal_wait_time **parameter configuration. After the standby node starts and establishes a connection with the primary node, it is in log catchup mode. If the difference between logs that are caught up is less than the value of catchup2normal_wait_time, the standby node is changed to synchronous mode.
  • Supports non-synchronization of configuration files. The configuration parameters of primary and standby nodes vary according to the specifications of hardware where nodes are deployed. Therefore, the original synchronization function is modified and you do not have to synchronize parameter configuration files between the primary and standby nodes.

• Expands the data type compatibility.

  Both char and varchar are compatible with PostgreSQL mode. When the length is calculated, the length of the character instead of the length of the byte is returned.

• Adds monitoring dimensions.

  Monitors the sort&hash information about **work_mem **in the view returned by get_instr_unique_sql().

  Adds the **WAIT_EVENT_WAL_BUFFER_ACCESS **and **WAIT_EVENT_WAL_BUFFER_FULL **wait events to the **get_instr_wait_event **view to monitor wal_buffer. **WAIT_EVENT_WAL_BUFFER_ACCESS **counts the number of times that the WAL buffer is accessed. (In consideration of performance, the access duration is not measured.) **WAIT_EVENT_WAL_BUFFER_FULL **collects statistics on the number of access times and access duration when the WAL buffer is full.

• Supports AI4DB: intelligent O&M.

  • Index recommendation: automatically recommends proper indexes for the SELECT query based on the access conditions of SQL statements; Provides virtual indexes for users to view the execution plan without really creating an index. Recommends workload-level indexes based on the workload information provided by users.
  • Users can deploy the database information collection platform in one-click mode, forecast the future trend based on the collected data, and detect exceptions in advance.

• Supports DB4AI: AI algorithm in the library.

  Compatible with the MADlib ecosystem, supports more than 70 algorithms, and provides better performance than the native PostgreSQL. Adds common algorithm suites XGBoost, prophet, GBDT, and hierarchical clustering to supplement the shortcomings of the MADlib ecosystem.

• Supports PL/Python.

  Supports the Python language as the SQL programming language.

• **gs_basebackup **supports standby node backup.

  **gs_basebackup **can back up data from the standby node to reduce the service processing pressure of the primary node.

• Refines permission management models.

  Supports DDL permission granting and revoking.

• Rebuilds autonomous transactions.

  Rebuilds the inter-process communication method used by the original autonomous transaction implementation to the inter-thread communication method, which is simpler.

• Rebuilds parallel queries.

  Rebuilds a unified parallel framework to replace the original parallel query framework and the distributed cross-node parallel query framework.

• Supports the sysdate data type.

  Sysdate returns the current date and time, which are the time zone and time of the Linux OS on a host machine where the database is located.

• Supports standby node adding or deleting.

  Provides the OM tool to support online scale-out and scale-in of standby nodes which can be dynamically added or deleted without affecting services.

• Supports multiple Python versions.

  On CentOS, the database installation depends on Python 3.6. The released 1.1.0 version can be installed in Python 3.7. You can also build third-party libraries based on the specified Python 3._* _version to adapt to more Python versions.

• Supports adding an index online.

  Uses the create index concurrently syntax to create indexes online without blocking DML.

• Provides the upgrade tool.

  Supports the upgrade from 1.0.1 to 1.1.0.

  Note:

  The following parameters in the 1.0.1 version are automatically deleted from the 1.1.0 version during the upgrade. Evaluate the impact before the upgrade.

  enable_beta_nestloop_fusion	enable_upsert_to_merge	force_parallel_mode	gs_clean_timeout	max_background_workers
  max_parallel_workers_per_gather	min_parallel_table_scan_size	pagewriter_threshold	parallel_leader_participation	parallel_setup_cost
  parallel_tuple_cost	parctl_min_cost	tcp_recv_timeout	transaction_sync_naptime	transaction_sync_timeout
  twophase_clean_workers	wal_compression	-	-	-

• Decouples installation from the OM tool.

  The OM tool is decoupled from the database kernel in the 1.1.0 version:

  1. The OM tool is stored in the openGauss-OM repository which will be used to manage the OM tool code.

  2. The OM tool and kernel are packaged separately. You can place the images of the OM tool and kernel in the same directory and use the OM tool to install them. The installation method remains unchanged. If only the kernel is concerned, you can decompress the kernel image and run it separately.