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Lukasz Ziarek 2015-06-20 22:43:53 -04:00
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sections/4-queryc.tex
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@ -49,7 +49,7 @@ Figure~\ref{fig:breakdownByCatAndFeature} summarizes all 45 million statements e
\label{fig:topBottom10Apps}
\end{figure*}
Figure~\ref{fig:topBottom10Apps} shows the 10 most frequent and 10 least frequent clients of SQLite over the one month trace. The most active SQLite clients include internal android services that broker access to data shared between apps such as personal media, calendars and address books, as well as pre-installed and popular social media apps. There is less of a pattern at the low end, although several infrequent SQLite clients are themselves apps that may be used only infrequently, especially on a phone-sized device. We suspect that the distribution of apps would differ significantly for a tablet-sized device.
Figure~\ref{fig:topBottom10Apps} shows the 10 most frequent and 10 least frequent clients of SQLite over the one month trace. The most active SQLite clients include internal Android services that broker access to data shared between apps such as personal media, calendars and address books, as well as pre-installed and popular social media apps. There is less of a pattern at the low end, although several infrequent SQLite clients are themselves apps that may be used only infrequently, especially on a phone-sized device. We suspect that the distribution of apps would differ significantly for a tablet-sized device.
\subsection{Database Reads}
\begin{figure*}
@ -109,7 +109,7 @@ Not surprisingly, this suggests that the most common use-case for SQLite is as a
Figure~\ref{fig:allSelectConditionBreakdown} shows a similar breakdown for all 33.5 million \texttt{SELECT} queries seen. As before, the table shows the form of all expressions that appear as one of the conjunctive terms of a \texttt{WHERE} clause, alongside the number of queries where the expression appears. 31.0 million of these queries contain an exact lookup.
1.6 million queries contain at least one multi-attribute equality expression such as an equijoin constraint, lining up nicely with the 1.7 million queries that reference at least two tables.
1.6 million queries contain at least one multi-attribute equality expression such as an equi-join constraint, lining up nicely with the 1.7 million queries that reference at least two tables.
\begin{figure}
\centering
@ -162,7 +162,7 @@ We next break the \texttt{SELECT} workload down by the calling application (app)
Due to limitations of the logging infrastructure, 4.32 million queries (just over 12.9\% of the workload) could not be associated with a specific application, and our app-specific analysis excludes these queries.
Additionally, system services in Android are often implemented as independent apps and counted as such in the numbers presented.
Over the course of the one-month trace we observed 179 distinct apps,, varying from built-in android applications such as \textit{Gmail} or \textit{YouTube} to video players such as \textit{VLC} to games such as \textit{3 Kingdoms}. Figure~\ref{fig:selectByApp:all} shows the cumulative distribution of apps sorted by the number of queries that the app performs. The results are highly skewed, with the top 10\% of apps each posing more than 100 thousand queries over the one month trace, or an average of about 1 query every 4 minutes on any given phone. The most query-intensive system service, \textit{Media Storage} was responsible for 13.57 million queries or just shy of 40 queries per minute per phone. The most query-intensive user-facing app was \textit{Google+}, which performed 1.94 million queries over the course of the month or 5 queries per minute.
Over the course of the one-month trace we observed 179 distinct apps,, varying from built-in Android applications such as \textit{Gmail} or \textit{YouTube} to video players such as \textit{VLC} to games such as \textit{3 Kingdoms}. Figure~\ref{fig:selectByApp:all} shows the cumulative distribution of apps sorted by the number of queries that the app performs. The results are highly skewed, with the top 10\% of apps each posing more than 100 thousand queries over the one month trace, or an average of about 1 query every 4 minutes on any given phone. The most query-intensive system service, \textit{Media Storage} was responsible for 13.57 million queries or just shy of 40 queries per minute per phone. The most query-intensive user-facing app was \textit{Google+}, which performed 1.94 million queries over the course of the month or 5 queries per minute.
At the other end of the spectrum, the bottom 10\% of apps posed as few as 30 queries over the entire month.
@ -171,7 +171,7 @@ We noted above that a large proportion of \texttt{SELECT} queries were exact loo
INSERT OR REPLACE INTO properties(property_key,property_value) VALUES (?,?);
SELECT property_value FROM properties WHERE property_key=?;
\end{verbatim}
In this query, \texttt{?} is a prepared statement parameter that acts as a placeholder for values that are bound when the prepared statement is evaluated.
In this query, \texttt{?} is a prepared statement parameter that acts as a place holder for values that are bound when the prepared statement is evaluated.
To broaden the scope of our search for key/value queries, we define a key-value look-up query as a \texttt{SELECT} query over a single relation that either performs a full table scan, or performs an exact look-up on a single attribute.
Figure~\ref{fig:selectByApp:simple} shows the cumulative distribution of apps sorted by the percent of its queries that are key-value lookup queries. For 24 apps (13.4\%), we observed only key-value queries during the entire, month-long trace.
@ -260,4 +260,13 @@ Figure~\ref{fig:updateByApp:modOps} illustrates app-level write workloads, sorti
Figure~\ref{fig:updateByApp:writeRatio} breaks apps down by their read/write ratio. Surprisingly, 25 apps (14\% of the apps seen) did not perform a single write over the course of the entire trace. Manual examination of these apps suggested two possible explanations. Several apps have reason to store state that is updated only infrequently. For example, \textit{JuiceSSH} or \textit{Key Chain} appear to use SQLite as a credential store. A second, far more interesting class of apps includes apps like \textit{Google Play Newsstand}, \textit{Eventbrite}, \textit{Wifi Analyzer}, and \textit{TuneIn Radio Pro}, which all have components that query data stored in the cloud. We suspect that the cloud data is being encapsulated into a pre-constructed SQLite database and being pushed to, or downloaded by the client applications.
This type of behavior might be compared to a bulk ETL process or log shipment in a server-class database workload, except that here, the database has already been constructed. Pre-caching through database encapsulation is a unique feature of embedded databases, and one that is already being used in a substantial number of apps.
% Barcode Scanner, BharatMatrimony, CamCard, CityMaps2Go Pro, Discover, Download Manager, Eventbrite, GPS Status, Google Play Newsstand, JuiceSSH, KBS kong, Key Chain, LTE Discovery, MX Player Pro, My Tracks, PlayerPro, Pushbullet, Quickoffice, SignalCheck Lite, Sound Search for Google Play, Splitwise, TuneIn Radio Pro, VLC, WeChat, Wifi Analyzer
% Barcode Scanner, BharatMatrimony, CamCard, CityMaps2Go Pro, Discover, Download Manager, Eventbrite, GPS Status, Google Play Newsstand, JuiceSSH, KBS kong, Key Chain, LTE Discovery, MX Player Pro, My Tracks, PlayerPro, Pushbullet, Quickoffice, SignalCheck Lite, Sound Search for Google Play, Splitwise, TuneIn Radio Pro, VLC, WeChat, Wifi Analyzer
%% LocalWords: SQLite UPSERT App Gmail Facebook Android Speedtest
%% LocalWords: KakaoStory MX Quickoffice VLC Barcode PlayerPro KBS
%% LocalWords: kong Apps apps pre TPC SQLite's equi Const JDBC app
%% LocalWords: SUBSTR CONCAT cdf app's YouTube BuzzFeed ms
%% LocalWords: Evernote LTE Muzei PayPal SignalCheck Lite TuneIn
%% LocalWords: Wifi UPSERTS Mappers ORMs upserts subqueries LINQ
%% LocalWords: StatusQuo Truffle cols ops JuiceSSH Eventbrite ETL
%% LocalWords: BharatMatrimony CamCard CityMaps Pushbullet WeChat
%% LocalWords: Splitwise