Optimistic concurrency control (OCC) protocols validate whether a transaction has conflicts with other concurrent transactions after this transaction completes its execution. In this work, we demonstrate that the validation phase has a great influence on the performance of modern in-memory database systems, especially under heterogeneous workloads. The cost of validating operations in a transaction is determined by two main factors. The first factor is the operation type. An OCC protocol would take much less cost on validating a single-record read operation than validating a key-range scan operation. The second factor is the workload type. Existing schemes in OCC variants for validating key-range scan perform differently under various workloads. Although various validation schemes share the same goal of guaranteeing a transaction schedule to be serializable, there are remarkable differences between the costs they introduced. These observations motivate us to design an optimistic concurrency control which can choose a low-cost validation scheme at runtime, referred to as adaptive optimistic concurrency control (AOCC). First, at transaction-level granularity, AOCC can assign a validation method to a transaction according to the features of its operations. Furthermore, for each operation in a transaction, the validation method is selected according to not only the number of accessed records but also the instant characteristics of workloads. Experimental results show that AOCC has good performance and scalability under heterogeneous workloads mixed with point accesses and predicate queries.

This work addresses the need for efficient key-range validation for a composite OLTP and bulk processing workload characterized by modern enterprise applications. In-memory database system (IMDB), mostly adopting the optimistic concurrency control (OCC) mechanism, performs well if the contention of conventional OLTP workloads is low and each transaction only contains point read/write query with primary key. In this work we present the performance problem of IMDBs under mixed OLTP and bulk processing workloads. The reason is that existing OCC protocols take expensive cost to generate a serializable schedule if the OLTP workload contains bulk processing operations with key-range scan. To this end, we develop an efficient and scalable range optimistic concurrency control (ROCC) which uses logical ranges to track the potential conflicting transactions and to reduce the number of transactions to be validated. At the read phase, a transaction keeps a set of predicates to remember the version and precise scope of scanned ranges, which eliminates the cost of maintaining scanned records. Before entering the validation phase, if the transaction intends to update records in the logical range, it needs to register to the corresponding lock-free list implemented by a circular array. Finally, ROCC filters out unrelated transactions and validates the bulk operation at range level. Experimental results show that ROCC has good performance and scalability under heterogeneous workloads mixed with point access and bulk processing.

Membership testing is the problem of testing whether an element is in a set of elements. Performing the test exactly is expensive space-wise, requiring the storage of all elements in a set. In many applications, an approximate testing that can be done quickly using small space is often desired. Bloom filter (BF) was designed and has witnessed great success across numerous application domains. But there is no compact structure that supports set membership testing for temporal queries, e.g., has person A visited a web server between 9:30am and 9:40am? And has the same person visited the web server again between 9:45am and 9:50am? It is possible to support such "temporal membership testing" using a BF, but we will show that this is fairly expensive. To that end, this paper designs persistent bloom filter (PBF), a novel data structure for temporal membership testing with compact space.

Log replication is a key component in highly available database systems. In order to guarantee data consistency and reliability, it is common for modern database systems to utilize Paxos protocol, which is responsible for replicating transactional logs from one primary node to multiple backups. However, the Paxos replication needs to store and synchronize some additional metadata, such as committed log sequence number (commit point), to guarantee the consistency of the database. This increases the overhead of storage and network, which would have a negative impact on the throughput in the update intensive work load. In this paper, we present an implementation of log replication and database recovery methods, which adopts the idea of piggybacking, i.e., commit point can be embedded in the commit logs. This practice not only retains virtues of Paxos replication, but also reduces disk and network IO effectively. We implemented and evaluated our approach in a main memory database system. Our experiments show that the piggybacking method can offer 1.3× higher throughput than typical log replication with synchronization mechanism.