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description=Optimize your Android app's performance by following best practices for SQLite databases, including configuration, schema design, query optimization, and troubleshooting.;
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lyzing with profile gpu rendering improve layout performance battery and power optimize for doze and app standby monitor the battery level and charging state monitor connectivity status and connection metering determining and monitor docking state and type profile battery usage with batterystats and battery historian analyze power use with battery historian understand power management resource limits test power related issues background optimizations reduce app size hardware acceleration best practices for sqlite performance performance best practices monitoring performance about monitoring performance address common performance issues overview anrs crashes slow rendering slow sessions low memory killers lmks memory usage anonymous rss swap bitmap memory usage excessive wake locks stuck partial wake locks excessive wakeups excessive background wi fi scans excessive background network usage excessive battery usage permission denials app startup time dex code optimization jankstats library on google play android vitals healthy releases build ai experiences get started get started hello world developer verification adaptive apps compose for ui ai powered ide training monetization with play ️ optimize by form factor phones tablets foldables android for cars android tv android xr googlebook chromeos wear os build by category games camera media social messaging health fitness productivity enterprise apps get the latest latest updates experimental updates android studio preview jetpack compose libraries wear os releases privacy sandbox ️ excellent experiences learn more ui design design for android mobile desktop experiences xr headsets xr glasses ai glasses widgets wear os android tv android for cars architecture introduction libraries navigation modularization testing kotlin multiplatform quality overview core value user experience accessibility technical quality excellent experiences security overview privacy permissions identity fraud prevention gemini in android studio learn more get android studio core areas samples multidevice support user interfaces background work data and files connectivity all core areas ️ tools and workflow write and debug code build projects test your app performance command line tools gradle plugin api android bench device tech phones tablets foldables googlebook chromeos android for cars android tv android xr wear os android health better together all devices ️ libraries android platform jetpack libraries compose libraries google play services ️ google play sdk index ️ play console go to play console learn more ️ fundamentals play monetization play integrity android vitals play policies play programs ️ games dev center overview play asset delivery play games services play games on pc level up guidelines all play guides ️ libraries play feature delivery play in app updates play in app reviews play install referrer google play services ️ google play sdk index ️ all play libraries ️ tools resources android app bundles brand marketing play console apis ️ the android developer s blog read the latest explore the authors explore categories product news community how tos case studies events programs documentation android developers design plan app quality technical quality best practices for sqlite performance stay organized with collections save and categorize content based on your preferences android offers built in support for sqlite an efficient sql database follow these best practices to optimize your app s performance ensuring it remains fast and predictably fast as your data grows by using these best practices you also reduce the possibility of encountering performance issues that are difficult to reproduce and troubleshoot to achieve faster performance follow these performance principles read fewer rows and columns optimize your queries to retrieve only the necessary data minimize the amount of data read from the database because excess data retrieval can impact performance push work to sqlite engine perform computations filtering and sorting operations within the sql queries using sqlite s query engine can significantly improve performance modify the database schema design your database schema to help sqlite construct efficient query plans and data representations properly index tables and optimize table structures to enhance performance additionally you can use the available troubleshooting tools to measure the performance of your sqlite database to help identify areas that require optimization we recommend using the jetpack room library configure the database for performance follow the steps in this section to configure your database for optimal performance in sqlite enable write ahead logging sqlite implements mutations by appending them to a log which it occasionally compacts into the database this is called write ahead logging wal enable wal unless you are using attach database relax the synchronization mode when using wal by default every commit issues an fsync to help ensure that the data reaches the disk this improves data durability but slows down your commits sqlite has an option to control synchronous mode if you enable wal set synchronous mode to normal when opening the database val paramsbuilder sqlitedatabase openparams builder sqlitedatabase openparams builder paramsbuilder journalmode sqlitedatabase sync_mode_normal or after having opened the database db execsql pragma synchronous normal in this setting a commit can return before the data is stored in a disk if a device shutdown occurs such as on loss of power or a kernel panic the committed data might be lost however because of logging your database isn t corrupted if only your app crashes your data still reaches the disk for most apps this setting yields performance improvements at no material cost note if your app has multiple databases use the same synchronous setting everywhere in case there are data dependencies between different databases define efficient table schemas to optimize performance and minimize data consumption define an efficient table schema sqlite constructs efficient query plans and data leading to faster data retrieval this section provides best practices for creating table schemas consider integer primary key for this example define and populate a table as follows create table customers id integer name text city text insert into customers values 456 john lennon liverpool england insert into customers values 123 michael jackson gary in insert into customers values 789 dolly parton sevier county tn the table output is as follows rowid id name city 1 456 john lennon liverpool england 2 123 michael jackson gary in 3 789 dolly parton sevier county tn the column rowid is an index that preserves insertion order queries that filter by rowid are implemented as a fast b tree search but queries that filter by id are a slow table scan if you plan on doing lookups by id you can avoid storing the rowid column for less data in storage and an overall faster database create table customers id integer primary key name text city text your table now looks as follows id name city 123 michael jackson gary in 456 john lennon liverpool england 789 dolly parton sevier county tn since you don t need to store the rowid column id queries are fast note that the table is now sorted based on id instead of insertion order accelerate queries with indexes sqlite uses indexes to accelerate queries when filtering where sorting order by or aggregating group by a column if the table has an index for the column the query is accelerated in the previous example filtering by city requires scanning the entire table select id name where city london england for an app with a lot of city queries you can accelerate those queries with an index create index city_index on customers city an index is implemented as an additional table sorted by the index column and mapped to rowid city rowid gary in 2 liverpool england 1 sevier county tn 3 note that the storage cost of the city column is now double because it s now present in both the original table and the index since you are using the index the cost of added storage is worth the benefit of faster queries however don t maintain an index that you re not using to avoid paying the storage cost for no query performance gain create multi column indexes if your queries combine multiple columns you can create multi column indexes to fully accelerate the query you can also use an index on an outside column and let the inside search be done as a linear scan for example given the following query select id name where city london england order by city name you can accelerate the query with a multi column index in the same order as specified in the query create index city_name_index on customers city name however if you only have an index on city the outside ordering is still accelerated while the inside ordering requires a linear scan this also works with prefix inquiries for example an index on customers city name also accelerates filtering ordering and grouping by city since the index table for a multi column index is ordered by the given indexes in the given order consider without rowid by default sqlite creates a rowid column for your table where rowid is an implicit integer primary key autoincrement if you already have a column that is integer primary key then this column becomes an alias of rowid for tables that have a primary key other than integer or a composite of columns consider without rowid warning tables using without rowid can incur performance penalties if their primary keys are large for more information see the use cases store small data as a blob and large data as a file if you want to associate large data with a row such as a thumbnail of an image or a photo for a contact you can store the data either in a blob column or in a file and then store the path in the column files are generally rounded up to 4 kb increments for very small files where the rounding error is significant it s more efficient to store them in the database as a blob sqlite minimizes file system calls and is faster than the underlying filesystem in some cases note on android consider using a file for any data that is several multiples of 4 kb improve query performance follow these best practices to improve query performance in sqlite by minimizing response times and maximizing processing efficiency note many of the examples on this page include a limit clause this is good practice because queries that could return many rows have performance implications when returning large data sets the exception to this is for queries that implicitly return a size constrained dataset for example searching on a field defined as unique can only return zero or one row read only the rows you need filters let you narrow down your results by specifying certain criteria such as date range location or name limits let you control the number of results you see db rawquery select name from customers limit 10 trimindent null use cursor while cursor movetonext process cursor data read only the columns you need avoid selecting unneeded columns which can slow down your queries and waste resources instead only select columns that are used in the following example you select id name and phone this is not the most efficient way of doing this see the following example for a better approach db rawquery select id name phone from customers trimindent null use cursor while cursor movetonext val name cursor getstring 1 further processing however you only need the name column db rawquery select name from customers trimindent null use cursor while cursor movetonext val name cursor getstring 0 further processing parameterize queries your query string might include a parameter that is only known at runtime such as the following fun getnamebyid id long string db rawquery select name from customers where id id null use cursor return if cursor movetofirst cursor getstring 0 else null in the preceding code every query constructs a different string and thus doesn t benefit from the statement cache each call requires sqlite to compile it before it can execute instead you can replace the id argument with a parameter and bind the value with selectionargs fun getnamebyid id long string db rawquery select name from customers where id trimindent arrayof id tostring use cursor return if cursor movetofirst cursor getstring 0 else null now the query can be compiled once and cached the compiled query is reused between different invocations of getnamebyid long caution if the input argument is some other object less constrained than just a number string concatenation might lead to a sql injection security vulnerability always use parameters for variables or untrusted data iterate in sql not in code use a single query that returns all targeted results instead of a programmatic loop iterating on sql queries to return individual results the programmatic loop is about 1000 times slower than a single sql query use distinct for unique values using the distinct keyword can improve the performance of your queries by reducing the amount of data that needs to be processed for example if you want to return only the unique values from a column use distinct db rawquery select distinct name from customers trimindent null use cursor while cursor movetonext only iterate over distinct names in kotlin process distinct name use aggregate functions whenever possible use aggregate functions for aggregate results without row data for example the following code checks whether there is at least one matching row this is not the most efficient way of doing this see the following example for a better approach db rawquery select id name from customers where city paris trimindent null use cursor if cursor movetofirst at least one customer from paris handle found else no customers from paris handle not found to only fetch the first row you can use exists to return 0 if a matching row does not exist and 1 if one or more rows match db rawquery select exists select null from customers where city paris trimindent null use cursor if cursor movetofirst cursor getint 0 1 at least one customer from paris handle found else no customers from paris handle not found use sqlite aggregate functions in your app code count counts how many rows are in a column sum adds all numerical values in a column min or max determines the lowest or highest value works for numeric columns date types and text types avg finds the average numerical value group_concat concatenates strings with an optional separator use count instead of cursor getcount in the following example the cursor getcount function reads all the rows from the database and returns all the row values this is not the most efficient way of doing this see the following example for a better approach db rawquery select id from customers trimindent null use cursor val count cursor getcount use count however by using count the database returns only the count db rawquery select ...