Amazon Deequ vs SQLancer

Amazon Deequ is an Apache Spark library for writing data unit tests that measure quality in large datasets, including datasets with billions of rows. Its checks cover row counts, completeness, uniqueness, allowed values, nonnegative values, patterns, and approximate quantiles. Deequ also supports data profiling, stored and queryable metrics, anomaly detection over time, automatic constraint suggestions, and incremental metric computation. Its declarative Data Quality Definition Language includes rules for counts, completeness, uniqueness, statistics, schema matching, freshness, and custom SQL. Row-level evaluation can identify records that pass or fail supported rules, while dataset-level rules such as RowCount and Mean are skipped in that mode. Deequ targets specific Apache Spark versions, so the release must match the Spark version; versions 2.1.0 and later require Java 11. The project provides Maven and sbt dependency examples. It is licensed under Apache 2.0, and Python users can use the PyDeequ interface.

SQLancer automatically tests database management systems to find implementation bugs. It generates SQL statements and database states, then validates query behavior with test oracles including TLP, NoREC, PQS, DQP, CODDTest, and CERT. Targets include logic bugs, performance issues, unexpected internal errors, and crashes. The supported-databases list contains 21 implementations, including PostgreSQL, MySQL, SQLite, DuckDB, and ClickHouse, while the impact page reports evidence of use by 22 DBMS projects. SQLancer runs on Linux, macOS, and Windows and requires Java 11 or above plus Maven. Releases are available through GitHub, Maven Central, and DockerHub. DuckDB, H2, and SQLite can run as embedded systems with binaries included as JAR dependencies. Runs may continue indefinitely when no bugs appear, with options to stop after a selected bug count or timeout. It is free under the MIT License.