advanced (than conventional databases) reasoning while being amenable to optimization and alternative evaluation strategies needed for larger data sets. After the initial surge in enthusiasm, the field entered a period of reduced interest and activity for a number of reasons. Among the most important ones was the blow-back due to “over-promise.” In the 1980s and early 1990s, the technology was simply not ready for production deployment, and it is not coincidental that this period of decline had a big overlap with the so-called “AI Winter.” Back then, the necessary theory and technology was yet to be developed and the pace was not keeping up with the expectations. On top of it, computers were severely underpowered compared to today and simply could not support the computational demands of either AI or logic programming. Another important factor was that the chosen application-area target—databases—was not optimal: DBMSs are demanding to implement, the query language is just one component of a DBMS out of many, and special-purpose solutions—albeit clunky—were available for applications requiring recursion (such as a bill of materials).
By the late 1990s, however, the situation started to change: computers became hundreds of times more powerful and the technology finally started to catch up with the needs. Importantly, that technology came both from the “ivory tower”—academia—as well as from application-driven research and practitioners. Moreover, people discovered that the application base for Datalog is much broader than just data management. Datalog has shown advantages in areas that involve complex, mutually recursive reasoning (program analysis) and benefits for distributed, asynchronous evaluation (declarative networking). Datalog was likewise recognized as an important reasoning tool for the Semantic Web as well as a design and prototyping tool. It is often used as an intermediate form that is easy to map to because it supports analysis and optimization, and can be translated to a variety of representations and evaluation techniques. Finally, Datalog simply took its place in the diverse family of programming languages.
The original idea about Datalog as a query language for DBMS turned out to be not that outlandish either: companies such as LogicBlox and Datomic have built DBMSs based on Datalog, and they include schemas, concurrency control and most of the usual accouterments of database systems. It just took more time.
In summary, do not expect Datalog (or any other language) to replace other technologies going forward, but it is clear that it has established itself as a viable option in commercially relevant applications. One should expect that there will be additional areas where Datalog will show advantages, such as data analytics.
Acknowledgments
The authors wish to thank Jeff Ullman, Raghu Ramakrishnan, Molham Aref, Moshe Vardi, Joe Hellerstein, and Serge Abiteboul for their comments on the ups and downs of Datalog. TJ Green helped early on with the structure of this chapter. Georg Gottlob and Bob Kowalski read this material and shared with us invaluable comments and critique, as did several anonymous reviewers. Special thanks is due Annie Liu for, without her gentle but persistent push and thoughtful comments, this chapter would not have been completed.
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