11 lines
2.8 KiB
TeX
11 lines
2.8 KiB
TeX
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Our primary observation is that an embedded database workload in a modern mobile device includes a mix of both OLTP and OLAP characteristics. The majority of operations performed by SQLite are simple key-value manipulations and look-ups. However, a substantial fraction of the (comparatively read-heavy) workload consists of far more complex OLAP-style operations involving wide, multi-table joins, nested sub-queries, complex selection predicates, and aggregation.
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Many of these workload characteristics are motivated by factors unique to embedded databases. For example, SQLite uses single-file databases that have a standard, platform-independent format. As a consequence, it is common to see entire databases, indexes and all, transported in their entirety through web downloads or as attachments to other files~\cite{Dit2015CIDR}. A common pattern we observed was for a cloud service to package a fragment of its state into a SQLite database, which could then be cached locally on the device for lower-latency and offline access.
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Optimization targets also differ substantially. Latency is a primary concern, but at vastly different scales. Over our one-month trial, the average SQL statement took 2 ms to evaluate, and even complex \texttt{SELECT} queries with 4-level deep nesting only took an average of 120 ms. %Flash wear~\cite{SIGMOD2014?} is also a concern.
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Finally, unlike typical server-class benchmark workloads where throughput is a key factor, embedded databases have fixed, ``small data"~\cite{Dit2015CIDR} workloads and need to share computing resources fairly with other processes on the same device. This means that in stark contrast to server-class workloads, the database is idle more frequently. Periods of low-utilization are opportunities for background optimization, but must be managed against the needs of other applications running on the device, as well as the device's limited power budget. We use the term ``pocket data" to refer to data management settings that exhibit such characteristics.
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Pocket data workloads represent a growing, and extremely important class of database consumers. Unfortunately, research and development on embedded databases (\textit{e.g.},~\cite{jeong2013iostack,kang2013xftl}) is presently obligated to rely on micro-benchmarks or anecdotal observations about the needs and requirements of embedded database engines. In this paper, we lay out the characteristics of a one month trace of SQLite operations performed on eleven Android smartphones participating in the PhoneLab experimental platform~\cite{phonelab}. We believe that a new TPC-MOBILE benchmark that captures these characteristics can provide a principled, standardized way to evaluate advances in mobile database technology, which will in turn, help to drive the development of such advances.
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