Oracle International Corporation

Mark Dilman

Wei-Ming Hu

Leonid Novak

Ilya Taranov

Jean De Lavarene

Lik Wong

Wei Ming Hu

Michael James Stewart

Douglas N Surber

Vidya Hegde

Ghazi Nourdine Benadjaoud

Sampanna Shahaji Salunke

Zheng Zeng

Hubert Ken Sun

Vinay H Srihari

Ashok Shivarudraiah

Tong Zhou

Stephen Ball

Juan R Loaiza

Balasubrahmanyam Kuchibhotla

J William Lee

Kevin S Neel

Kant C Patel

Feroz Alam Khan

Saurabh Kumar Verma

Bhaskar Mathur

Ilesh Garish

Lakshminaray Chidambaran

Saravanakumar Ramasubramanian

Tirthankar Lahiri

Yunrui Li

Kumar Rajamani

Niloy Mukherjee

Jia Shi

Jaebock Lee

Margaret Susairaj

Neil MacNaughton

Alok Pareek

Muthu Olagappan

Bharat Chandra Baddepudi

Kiran Badrinarain Goyal

Saurabh Verma

Vipin Gokhale

Hochak Hung

Lawrence HinLuen To, Jr

Darshan Maniyani

Sachin Rathod

Margaret Susairai

Wumengjian Zhu

Wei Hu

Hubert Sun

Feroz A Khan

Michael J Stewart

Replication is improved in a globally distributed database, such as a replicated sharded database, which uses raft-based asynchronous database replication. Improvements include Raft log persistence, coordination of followers' processing speed, transaction outcome determination, and column name compression, and improved failover time through heartbeat consolidation and keeping apply processes of followers running across failovers.

Performance optimization in raft-based asynchronous database transaction replication

A consensus protocol-based replication approach is provided. Chunks are grouped into replication units (RUs) to optimize replication efficiency. Chunks may be assigned to RUs based on load and replication throughput. Splitting and merging RUs do not interrupt concurrent user workload or require routing changes. Transactions spanning chunks within an RU do not require distributed transaction processing. Each replication unit has a replication factor (RF), which refers to the number of copies/replicas of the replication unit, and an associated distribution factor (DF), which refers to the number of servers taking over the workload from a failed leader server. RUs may be placed in rings of servers, where the number of servers in a ring is equal to the replication factor, and quiescing the workload can be restricted to a ring of servers instead of the entire database.

Configuration and management of replication units for asynchronous database transaction replication

A lead-sync log record is used to synchronize the replication logs of follower shards to the leader shard. In response to a failure to determine that there is a consensus for a database transaction commit operation after a shard server becomes a new leader, the new leader shard performs a sync operation using the lead-sync log record to synchronize replication logs of the follower shards to the replication log of the new leader. A shard server identifies a first transaction having a first log record but not a post-commit log record in the replication log, defines a recovery window in the replication log starting at the first log record of the identified first transaction and ending at the lead-sync log record, identifies a set of transactions to be recovered, and performs a recovery action on the set of transactions to be recovered.

Recovery from loss of leader during asynchronous database transaction replication

A consensus protocol-based replication approach is provided. For each change operation performed by a leader server on a copy of the database, the leader server creates a replication log record and returns a result to the client. The leader does not wait for consensus for the change operation from the followers. For a commit, the leader creates a commit log record and waits for consensus. Thus, the leader executes database transactions asynchronously, performs replication of change operations asynchronously, and performs replication of transaction commits synchronously.

Consensus protocol for asynchronous database transaction replication with fast, automatic failover, zero data loss, strong consistency, full SQL support and horizontal scalability

Techniques are described herein for routing queries to particular nodes of a multi-node database system based on the query. A database table is partitioned into a plurality of affinity groups. Each affinity group is assigned a particular node as the master node of the affinity group. A mapping is sent to a query router indicating the master node for each affinity group of the plurality of affinity groups. The query router determines, for a particular query, a target node to which to send the particular query based on the mapping and the particular query.

Routing requests in shared-storage database systems

In accordance with an embodiment, described herein are systems and methods for providing direct access to a sharded database. A shard director provides access by software client applications to database shards. A connection pool (e.g., a Universal Connection Pool, UCP) and database driver (e.g., a Java Database Connectivity, JDBC, component) can be configured to allow a client application to provide a shard key, either during connection checkout or at a later time; recognize shard keys specified by the client application; and enable connection by the client application to a particular shard or chunk. The approach enables efficient re-use of connection resources, and faster access to appropriate shards.

System and method for providing direct access to a sharded database

Techniques are provided for processing a database command in a sharded database. The processing of the database command may include generating or otherwise accessing a shard key expression, and evaluating the shard key expression to identify one or more target shards that contain data used to execute the database command.

Automatic derivation of shard key values and transparent multi-shard transaction and query support

In accordance with an embodiment, the system enables access to a sharded database using a cache and a shard topology. A shard-aware client application connecting to a sharded database can use a connection pool (e.g., a Universal Connection Pool, UCP), to store or access connections to different shards or chunks of the sharded database within a shared pool. As new connections are created, a shard topology layer can be built at the database driver layer, which learns and caches shard key ranges to locations of shards. The shard topology layer enables subsequent connection requests from a client application to use a fast key path access to the appropriate shard or chunk.

System and method for providing access to a sharded database using a cache and a shard technology

Techniques are provided for creating, organizing, and maintaining a sharded database. A sharded database can be created using user-defined sharding, system-managed sharding, or composite sharding. The sharded database is implemented with relational database techniques. The techniques described can be used to route database requests and process queries in a sharded database.

Request routing and query processing in a sharded database

Techniques described herein create a sharding environment from existing independent databases, use the sharding environment to issue cross shard queries, create 'on all shards' new objects, and create and 'on all shards' new users that will have permission to access the data across all database shards.

Integration of existing databases into a sharding environment

Techniques are provided for creating, organizing, and maintaining a sharded database. A sharded database can be created using user-defined sharding, system-managed sharding, or composite sharding. The sharded database is implemented with relational database techniques. The techniques described provide improvements to load distribution, organization, query processing, and schema propagation in a sharded database.

Relational database organization for sharding

Techniques are provided for creating, organizing, and maintaining a sharded database. A sharded database can be created using user-defined sharding, system-managed sharding, or composite sharding. The sharded database is implemented with relational database techniques. The techniques described can be used for load distribution, organization, query processing, and schema propagation in a sharded database.

DDL processing in shared databases

Techniques are provided for creating, organizing, and maintaining a sharded database. A sharded database can be created using user-defined sharding, system-managed sharding, or composite sharding. The sharded database is implemented with relational database techniques. The techniques described can be used for organizing a sharded database using composite sharding.

Composite sharding

Growing community of inventors

Sunnyvale, CA, United States of America

Mark Dilman