Google services continuously generate vast amounts of application data. This data provides valuable insights to business users. We need to store and serve these planet-scale data sets under the extremely demanding requirements of scalability, sub-second query response times, availability, and strong consistency; all this while ingesting a massive stream of updates from applications used around the globe. We have developed and deployed in production an analytical data management system, Napa, to meet these requirements. Napa is the backend for numerous clients in Google. These clients have a strong expectation of variance-free, robust query performance. At its core, Napa's principal technologies for robust query performance include the aggressive use of materialized views, which are maintained consistently as new data is ingested across multiple data centers. Our clients also demand flexibility in being able to adjust their query performance, data freshness, and costs to suit their unique needs. Robust query processing and flexible configuration of client databases are the hallmark of Napa design. Most of the related work in this area takes advantage of full flexibility to design the whole system without the need to support a diverse set of preexisting use cases. In comparison, a particular challenge we faced is that Napa needs to deal with hard constraints from existing applications and infrastructure, so we could not do a "green field" system, but rather had to satisfy existing constraints. These constraints led us to make particular design decisions and also devise new techniques to meet the challenges. In this paper, we share our experiences in designing, implementing, deploying, and running Napa in production with some of Google's most demanding applications.

Mesa is a highly scalable analytic data warehousing system that stores critical measurement data related to Google's Internet advertising business. Mesa is designed to satisfy a complex and challenging set of user and systems requirements, including near real-time data ingestion and queryability, as well as high availability, reliability, fault tolerance, and scalability for large data and query volumes. Specifically, Mesa handles petabytes of data, processes millions of row updates per second, and serves billions of queries that fetch trillions of rows per day. Mesa is geo-replicated across multiple datacenters and provides consistent and repeatable query answers at low latency, even when an entire datacenter fails. This paper presents the Mesa system and reports the performance and scale that it achieves.

This paper presents an architecture and a wrapper synthesis approach for the design of multi-clock systems-on-chips. We build upon the initial work on multi-clock latency-insensitive systems by Singh and Theobald (2004), and provide a detailed system architecture with the following capabilities and benefits: (i) modules arc stalled only when needed, thereby avoiding unnecessary stalling, (ii) adequate metastability resolution is provided, (iii) handshake interfaces between modules are high-performance and low-latency, i.e., capable of transferring data packets on every clock cycle, (iv) IP cores with large clock distribution delays are correctly handled, and (v) an automated approach is provided for wrapper synthesis from formal specifications. For wrapper synthesis, we have developed an automated tool which accepts interface specifications in a high-level language (Component Wrapper Language, or CWL), and automatically produces gate-level implementations of wrapper circuitry that will correctly and efficiently stall the synchronous modules depending on the availability of I/O channels. An optimization is introduced to reduce the cost of the wrapper circuitry by eliminating "busy waiting." A small set of benchmark examples is also proposed, and synthesis results for the tool are promising.