MonkeyDB: effectively testing correctness under weak isolation levels

Abstract

Modern applications, such as social networking systems and e-commerce platforms are centered around using large-scale storage systems for storing and retrieving data. In the presence of concurrent accesses, these storage systems trade off isolation for performance. The weaker the isolation level, the more behaviors a storage system is allowed to exhibit and it is up to the developer to ensure that their application can tolerate those behaviors. However, these weak behaviors only occur rarely in practice and outside the control of the application, making it difficult for developers to test the robustness of their code against weak isolation levels. This paper presents MonkeyDB, a mock storage system for testing storage-backed applications. MonkeyDB supports a key-value interface as well as SQL queries under multiple isolation levels. It uses a logical specification of the isolation level to compute, on a read operation, the set of all possible return values. MonkeyDB then returns a value randomly from this set. We show that MonkeyDB provides good coverage of weak behaviors, which is complete in the limit. We test a variety of applications for assertions that fail only under weak isolation. MonkeyDB is able to break each of those assertions in a small number of attempts.

Efficient Black-Box Checking of Snapshot Isolation in Databases

“…In our testing of production databases, we used a simple, yet effective, database schema adopted by most of blackbox checkers [6,38,47,48]: a two-column table storing key-value pairs. Extending it to multi-columns or even the column-family data model could be done by: (i) representing each cell in a table as a compound key, i.e., "TableName:PrimaryKey:ColumnName", and a single value, i.e., the content of the cell [7,29]; and (ii) utilizing the compiler in [7] to rewrite (more complex) SQL queries to key-value read/write operations.…”

“…Elle [27] is a state-of-the-art checker for a variety of isolation levels, including strong session SI, 7 which is part of the Jepsen [24] testing framework. Elle requires specific data models like lists in workloads to infer the WW dependencies and specific APIs to perform list-specific operations such as "append".…”