As applications increasingly rely on lightweight, embedded databases for local storage and fast access, recruiters must identify SQLite professionals who can design and manage efficient data storage solutions. SQLite is widely used in mobile apps, desktop applications, IoT systems, and embedded environments due to its simplicity, reliability, and zero-configuration setup.
This resource, "100+ SQLite Interview Questions and Answers," is tailored for recruiters to simplify the evaluation process. It covers a wide range of topics—from SQLite fundamentals to advanced querying and optimization, including transactions, indexing, and concurrency handling.
Whether you're hiring Mobile Developers, Embedded Systems Engineers, Backend Developers, or Application Developers, this guide enables you to assess a candidate’s:
For a streamlined assessment process, consider platforms like WeCP, which allow you to:
Save time, enhance your hiring process, and confidently hire SQLite professionals who can build reliable, efficient, and lightweight data storage solutions from day one.
SQLite is a lightweight, embedded, relational database management system (RDBMS) that stores an entire database in a single file on disk. Unlike traditional databases, SQLite does not require a separate server process. Instead, it is integrated directly into the application that uses it.
SQLite follows the SQL standard and supports most common SQL features such as tables, indexes, views, triggers, and transactions. It is written in C, highly portable, and designed to be self-contained, zero-configuration, and reliable.
Because of its simplicity and efficiency, SQLite is widely used in mobile applications, desktop software, embedded systems, browsers, and IoT devices. Despite being lightweight, SQLite is robust and can handle databases up to terabytes in size, making it suitable for many real-world use cases.
SQLite differs from traditional relational databases like MySQL, PostgreSQL, or Oracle in several key ways:
While traditional databases are ideal for large, multi-user systems with heavy concurrency, SQLite excels in local storage, embedded environments, and applications where simplicity and reliability are more important than scalability.
SQLite is a serverless database.
This means:
In contrast, server-based databases require a running database server that clients connect to over a network. SQLite’s serverless architecture makes it faster for local access, easier to deploy, and ideal for environments where running a server is impractical or unnecessary.
SQLite provides a rich set of features despite being lightweight:
These features make SQLite powerful enough for production use while remaining extremely simple to use.
SQLite is commonly used in scenarios where local data storage is required without the complexity of a database server. Typical use cases include:
Because SQLite is stable, fast, and easy to embed, it is often the default choice for local persistence.
The most commonly used file extension for SQLite databases is:
.db.sqlite.sqlite3However, SQLite does not enforce any specific file extension. Any file can be an SQLite database as long as it follows the SQLite file format. The extension is mainly for convenience and readability, not functionality.
In SQLite, a database file is a single disk file that contains:
This file represents the entire database. There are no separate data files, log files, or configuration files required. This design makes SQLite databases easy to copy, move, back up, and restore, often by simple file operations.
A new SQLite database can be created in several ways:
No explicit “CREATE DATABASE” command is required. Opening the file is enough.
SQLite uses a single built-in storage engine, based on a B-tree data structure.
Key points:
This unified storage engine simplifies SQLite’s architecture and ensures consistent behavior across all platforms.
SQLite uses a dynamic typing system, meaning values have types, not columns. The core storage classes supported by SQLite are:
Although you can declare columns with types like VARCHAR, DATE, or BOOLEAN, SQLite maps them internally to these five storage classes using type affinity rules.
SQLite uses a dynamic typing system, which means that data types are associated with values, not with columns. Unlike many traditional databases where a column strictly enforces a data type, SQLite allows you to store different types of values in the same column.
For example, a column declared as INTEGER can still store text or floating-point values if inserted. SQLite determines the type of each value at runtime and stores it using one of its five internal storage classes: NULL, INTEGER, REAL, TEXT, or BLOB.
SQLite uses a concept called type affinity, which guides how values are converted and stored. Affinity influences behavior but does not strictly restrict it. This flexibility simplifies development, improves portability, and reduces schema rigidity, but it also requires developers to apply validation at the application level to maintain data consistency.
A table in SQLite is a structured collection of data organized into rows and columns. Each table represents a specific entity or concept, such as users, products, or transactions.
Internally, SQLite stores table data using a B-tree structure, which enables efficient searching, insertion, and deletion. Tables form the foundation of relational data modeling in SQLite and are used together with indexes, constraints, and relationships to organize and manage data effectively.
A table in SQLite is created using the CREATE TABLE statement. This statement defines the table name, columns, data types, and optional constraints such as primary keys or unique values.
When the CREATE TABLE command is executed:
SQLite also supports conditional table creation using IF NOT EXISTS, which prevents errors if the table already exists. Table creation is a fundamental step in designing an SQLite database schema.
A primary key is a column or a set of columns that uniquely identifies each row in a table. In SQLite, a primary key ensures that:
When a column is defined as INTEGER PRIMARY KEY, SQLite treats it as an alias for the internal ROWID, providing fast access and automatic indexing. Primary keys are critical for establishing relationships between tables and for optimizing query performance.
The ROWID is a unique, automatically generated integer identifier assigned to each row in an SQLite table that does not define an explicit INTEGER PRIMARY KEY.
Key characteristics of ROWID:
ROWID allows SQLite to efficiently locate and manage rows internally. Developers can directly reference ROWID in queries unless the table is created as a WITHOUT ROWID table.
Data is inserted into an SQLite table using the INSERT INTO statement. This statement allows you to insert one or more rows by specifying column names and corresponding values.
SQLite automatically:
SQLite also supports inserting data from the results of another query, enabling efficient bulk data operations. Proper use of transactions when inserting large volumes of data significantly improves performance.
The SELECT statement is used to retrieve data from one or more SQLite tables. It allows you to:
When a SELECT query is executed, SQLite’s query planner determines the most efficient way to access the data, using indexes when available. SELECT is the most frequently used SQL command and forms the basis of data querying and reporting in SQLite.
The WHERE clause is used in SQL statements to filter rows based on specified conditions. It allows you to retrieve, update, or delete only the rows that meet certain criteria.
SQLite evaluates the WHERE clause for each row and returns only those rows where the condition evaluates to true. The WHERE clause can include:
Using WHERE effectively improves performance and ensures accurate data manipulation.
DELETE and DROP serve different purposes in SQLite:
DELETE is reversible within a transaction, whereas DROP permanently removes the table definition. Choosing between them depends on whether you want to preserve the table structure.
The UPDATE statement is used to modify existing records in a table. It allows you to change one or more column values for rows that match a specified condition.
UPDATE works row by row and respects all constraints, triggers, and indexes defined on the table. When used without a WHERE clause, it updates all rows in the table, which can be dangerous in production systems.
Proper use of transactions and WHERE clauses ensures safe and efficient updates.
In SQLite, NULL represents the absence of a value, not an empty string, zero, or false. A NULL value indicates that the data is unknown, missing, or not applicable.
Important characteristics of NULL in SQLite:
= do not work with NULL; IS NULL must be usedCOUNT(*))Understanding NULL is critical for writing correct queries, especially when filtering data and handling optional fields.
SQLite sorts query results using the ORDER BY clause. This clause allows you to arrange results based on one or more columns in either ascending or descending order.
Key points:
ASC sorts in ascending order (default)DESC sorts in descending orderSorting happens after filtering and can impact performance, especially on large datasets. Proper indexing helps optimize ORDER BY operations.
The LIMIT clause restricts the maximum number of rows returned by a query. It is commonly used for pagination, previews, and performance optimization.
Key characteristics:
LIMIT improves application performance by avoiding unnecessary data retrieval, especially in user-facing applications.
The OFFSET clause specifies how many rows to skip before returning results. It is typically used along with LIMIT for pagination.
Important points:
While OFFSET is useful, large OFFSET values can be inefficient because SQLite still scans skipped rows internally.
An index in SQLite is a data structure that improves the speed of data retrieval operations on a table. It works similarly to an index in a book, allowing SQLite to locate rows without scanning the entire table.
Internally:
Indexes significantly improve read performance at the cost of additional storage and slightly slower write operations.
Indexes are used in SQLite to improve query performance, especially for SELECT queries involving WHERE, ORDER BY, JOIN, and GROUP BY clauses.
Benefits include:
However, excessive indexing can negatively impact insert, update, and delete performance. Indexes should be created strategically based on query patterns.
A UNIQUE constraint ensures that all values in a column or group of columns are distinct. It prevents duplicate data and helps maintain data integrity.
Key points:
UNIQUE constraints are often used for fields like email addresses, usernames, or identifiers that must remain distinct.
A NOT NULL constraint ensures that a column cannot store NULL values. It enforces mandatory data entry and improves data reliability.
Characteristics:
NOT NULL constraints are essential for critical fields such as IDs, timestamps, or required attributes.
A DEFAULT constraint assigns a predefined value to a column when no explicit value is provided during insertion.
Key benefits:
DEFAULT values can be constants or expressions, helping enforce standard behavior across records.
A foreign key is a constraint that establishes a relationship between two tables. It ensures that values in one table correspond to valid values in another table.
In SQLite:
Foreign keys help maintain consistent relationships between tables and prevent orphaned records.
SQLite supports foreign keys, but they are disabled by default in many environments. This means that even if foreign key constraints are defined in the table schema, SQLite will not enforce referential integrity unless foreign key support is explicitly enabled.
This design choice was made for backward compatibility and performance reasons. As a result, developers must consciously enable foreign key enforcement to ensure relationships between tables are maintained correctly. Failing to enable it can lead to orphaned records and inconsistent data.
Foreign key constraints in SQLite are enabled using a PRAGMA command. Once enabled, SQLite enforces referential integrity rules defined in table schemas.
Important points:
Once enabled, SQLite will automatically enforce rules such as CASCADE, SET NULL, and RESTRICT during INSERT, UPDATE, and DELETE operations.
A view in SQLite is a virtual table that is defined by a stored SQL SELECT query. Unlike regular tables, views do not store data physically; instead, they dynamically generate results when queried.
Views are used to:
Views behave like tables when queried but always reflect the latest data from their base tables.
A view in SQLite is created using the CREATE VIEW statement. This statement defines the view name and the SELECT query that generates its result set.
When a view is created:
SQLite also supports replacing views conditionally using IF NOT EXISTS, helping avoid conflicts during schema migrations.
INTEGER and REAL represent different numeric storage classes in SQLite:
SQLite may convert between these types automatically due to its dynamic typing system. INTEGER is preferred for IDs, counters, and flags, while REAL is used for measurements, percentages, and scientific values that require decimal precision.
The TEXT datatype in SQLite is used to store string and character data, such as names, descriptions, JSON strings, or encoded text.
Key characteristics:
TEXT is the most commonly used datatype for user-readable and structured string data.
BLOB (Binary Large Object) is used to store binary data exactly as it is provided, without any encoding or interpretation.
Common uses include:
BLOBs allow SQLite to store non-textual data efficiently, but large BLOBs should be used carefully due to memory and performance considerations.
All records in an SQLite table can be deleted using the DELETE statement without a WHERE clause.
Important considerations:
For better performance on large tables, dropping and recreating the table may be faster than deleting rows one by one.
In the SQLite command-line interface, all tables in the current database can be listed using a dot command.
This command:
It is a CLI-specific command and not part of standard SQL.
SQLite command-line dot commands are special meta-commands used to interact with the SQLite environment rather than the database itself.
Key characteristics:
.)Common uses include:
Dot commands make the SQLite CLI a powerful tool for database inspection, debugging, and maintenance.
SQLite stores all database data in a single disk file using a well-defined internal structure based on fixed-size pages and B-tree data structures. Each database file is divided into pages, and each page serves a specific purpose such as storing table data, index data, or metadata.
Tables and indexes are implemented as B-trees, where:
SQLite uses a pager module to manage page caching, disk I/O, atomic commits, and crash recovery. This design allows SQLite to efficiently locate, insert, update, and delete records while ensuring data consistency and durability even in the event of system crashes.
The SQLite page size is the fixed size of database pages used to store data within the database file. Each page holds a portion of table or index data.
Key details:
Page size affects performance:
Choosing the right page size depends on workload characteristics and storage hardware.
The SQLite database file format is highly structured and portable across platforms. It consists of:
This file format allows SQLite databases to be copied between systems without conversion.
Write-Ahead Logging (WAL) is a journaling mode in SQLite that improves concurrency and performance by writing changes to a separate WAL file instead of directly modifying the main database file.
In WAL mode:
WAL provides better read concurrency and reduces write blocking, making it ideal for modern applications with frequent reads.
WAL mode improves performance in several ways:
Because of these advantages, WAL mode is commonly used in mobile apps, desktop applications, and embedded systems where concurrency and responsiveness are critical.
Rollback journal mode is the traditional journaling mechanism used by SQLite to ensure atomic transactions.
How it works:
Rollback journal mode guarantees data integrity but limits concurrency, as writes block reads and other writes.
Key differences between WAL and rollback journal modes include:
.wal file; rollback uses a temporary journalRollback journal mode is simpler, while WAL is preferred for high-performance applications.
SQLite supports fully ACID-compliant transactions. A transaction groups multiple operations into a single logical unit of work.
Transaction handling involves:
Transactions can be explicit or implicit, and SQLite ensures that either all changes are committed or none are applied.
SQLite strictly adheres to ACID principles:
SQLite achieves ACID compliance through journaling, locking, and careful disk synchronization.
An implicit transaction is a transaction that SQLite automatically creates when a data-modifying statement is executed outside an explicit transaction block.
Characteristics:
For performance-critical applications, explicit transactions are recommended to reduce commit overhead.
An explicit transaction is a transaction that is manually started and controlled by the developer using SQL commands such as BEGIN, COMMIT, and ROLLBACK. Unlike implicit transactions, explicit transactions allow multiple SQL statements to be grouped into a single atomic unit of work.
Key characteristics:
Explicit transactions are critical in production systems to ensure data consistency, performance optimization, and predictable behavior.
SQLite uses a file-based locking mechanism to manage concurrent access to the database file. Since SQLite is serverless, it relies on operating system file locks to coordinate multiple readers and writers.
Core concepts:
SQLite’s locking model prioritizes simplicity and data integrity over high write concurrency.
SQLite uses four main lock states to control database access:
These lock transitions ensure safe writes while minimizing disruption to readers.
SQLite handles concurrency using a multi-reader, single-writer model. This means:
This design works well for read-heavy workloads but is less suitable for write-intensive, multi-user systems.
Database-level locking means that write locks apply to the entire database file, not individual tables or rows. When a write operation occurs:
This simplifies SQLite’s architecture but limits scalability for high-concurrency write workloads.
A composite primary key is a primary key composed of two or more columns that together uniquely identify a row in a table.
Key characteristics:
Composite primary keys help maintain relational integrity without introducing surrogate keys.
A composite index is an index that covers multiple columns and is created using a single CREATE INDEX statement.
Important considerations:
Composite indexes reduce query execution time by minimizing full table scans.
An expression index is an index created on the result of an expression rather than a plain column.
Key benefits:
Expression indexes are especially useful when queries frequently apply transformations to column values.
A partial index is an index that includes only a subset of rows, defined by a WHERE clause.
Advantages:
Partial indexes are ideal when only certain rows are frequently queried, such as active or non-deleted records.
The SQLite query planner is the component responsible for deciding how a SQL query is executed. It analyzes the query and determines the most efficient strategy.
Planner responsibilities:
The planner relies on statistics generated by the ANALYZE command and continuously adapts to schema and data changes to optimize performance.
SQLite chooses indexes using its cost-based query optimizer. When a query is executed, the optimizer analyzes all available indexes and evaluates multiple execution plans.
Key factors considered include:
ANALYZESQLite selects the index that minimizes estimated I/O and CPU cost. If no suitable index is found, it falls back to a full table scan.
EXPLAIN QUERY PLAN is a diagnostic command used to inspect how SQLite executes a query. It shows the high-level execution strategy chosen by the query planner.
It reveals:
This command is essential for debugging performance issues and validating index effectiveness.
Optimizing SELECT queries in SQLite involves both schema design and query design.
Key techniques include:
Proper transaction usage and WAL mode further improve read performance in real-world applications.
Common SQLite performance bottlenecks include:
Identifying these bottlenecks early and using tools like EXPLAIN QUERY PLAN helps maintain optimal performance.
SQLite handles joins using nested loop join algorithms. For each row in the outer table, SQLite searches matching rows in the inner table.
Performance depends on:
SQLite reorders joins automatically to minimize execution cost and may create temporary indexes if beneficial.
SQLite supports the following join types:
SQLite does not support RIGHT JOIN or FULL OUTER JOIN directly, but these can be emulated using query rewrites or UNION operations.
A CROSS JOIN returns the Cartesian product of two tables. Every row from the first table is combined with every row from the second table.
Key characteristics:
CROSS JOINs should be used carefully due to their potential performance impact.
A correlated subquery is a subquery that depends on values from the outer query. It is evaluated once for each row processed by the outer query.
Characteristics:
SQLite may optimize correlated subqueries, but joins are often faster for large datasets.
The key differences between subqueries and joins include:
Choosing between them depends on data size, complexity, and performance requirements.
A trigger in SQLite is a database object that automatically executes SQL statements in response to specific table events.
Triggers can be defined to run:
Triggers are used for enforcing business rules, auditing changes, maintaining derived data, and validating inputs at the database level.
SQLite supports triggers that are classified based on when they execute and which operation they respond to.
Based on timing:
Based on operations:
Triggers in SQLite fire once per affected row, not per statement, allowing fine-grained control over data changes.
The key difference lies in execution timing and use cases:
BEFORE triggers are ideal for enforcing rules, while AFTER triggers are best for post-processing actions.
An INSTEAD OF trigger is used primarily with views. Since views do not store data directly, SQLite cannot perform INSERT, UPDATE, or DELETE operations on them without guidance.
INSTEAD OF triggers:
They are essential for creating logical abstractions and secure data access layers.
Data integrity in SQLite is enforced using a combination of constraints, transactions, triggers, and foreign keys.
Key mechanisms include:
Proper schema design combined with these features ensures consistent and reliable data storage.
PRAGMA is a special SQLite command used to query or modify database behavior and internal settings.
PRAGMA statements:
They are often used for configuration, diagnostics, and optimization.
Some of the most commonly used PRAGMA commands include:
foreign_keys – Enable or disable foreign key enforcementjournal_mode – Set journaling mode (WAL, DELETE, etc.)synchronous – Control durability vs performance trade-offcache_size – Configure page cache sizepage_size – Define database page sizeintegrity_check – Validate database consistencyThese commands give developers fine-grained control over SQLite behavior.
SQLite provides built-in integrity checking through a PRAGMA command that verifies the internal consistency of the database.
This process:
Integrity checks are essential after crashes, disk failures, or unexpected shutdowns to ensure database reliability.
VACUUM is a command that rebuilds the entire SQLite database file.
What it does:
VACUUM can significantly reduce database size and improve read performance, but it is a resource-intensive operation.
VACUUM should be avoided in situations where:
Running VACUUM locks the database for the duration of the operation, making it unsuitable for busy production systems.
ANALYZE helps SQLite optimize queries by collecting statistical information about table contents and indexes.
ANALYZE:
Regular use of ANALYZE ensures that the query planner makes informed, efficient execution choices, especially after significant data changes.
SQLite stores both table data and index data using B-tree structures, which are optimized for disk-based storage and fast lookup. Each table or index is represented internally as a separate B-tree, stored across one or more fixed-size pages in the database file.
Key characteristics of SQLite’s B-tree architecture:
This design enables efficient searching, insertion, deletion, and range scans while minimizing disk I/O. SQLite’s B-tree implementation is tightly integrated with the pager and cache layers for performance and reliability.
Indexes in SQLite are implemented as separate B-tree structures, independent of the table’s B-tree. Each index entry contains:
Internally:
SQLite may also create automatic or transient indexes at runtime when it determines they will improve query performance.
The pager module is one of SQLite’s core internal components. It is responsible for managing all interaction between memory and disk.
The pager handles:
By abstracting low-level file I/O, the pager ensures SQLite remains portable, reliable, and ACID-compliant, regardless of the underlying operating system or filesystem.
SQLite ensures atomic commits through journaling mechanisms managed by the pager module. Depending on the journal mode, this is done using either:
In both cases:
This guarantees the “all-or-nothing” property of transactions.
Crash recovery in SQLite depends on the active journaling mode:
Rollback Journal Mode
WAL Mode
In both cases, SQLite guarantees that the database remains consistent and corruption-free, even after power loss or process crashes.
SQLite supports multi-process access using operating system file locks to coordinate readers and writers.
Key aspects:
This approach avoids complex server coordination but limits write scalability in highly concurrent environments.
Database contention occurs when multiple operations compete for access to the database file, especially write operations.
Common causes:
Symptoms include:
Reducing contention requires careful transaction design, batching writes, and enabling WAL mode.
WAL checkpointing is the process of moving committed data from the WAL file back into the main database file.
Checkpoint process:
Checkpointing keeps the WAL file from growing indefinitely and ensures long-term database consistency.
Auto-checkpoint is an automatic background process that triggers WAL checkpointing when the WAL file reaches a configured size threshold.
Key points:
Auto-checkpointing ensures WAL mode remains efficient under continuous write workloads.
SQLite can handle very large databases, theoretically up to 140 terabytes, though practical limits depend on filesystem and hardware.
Key mechanisms:
For large datasets, SQLite performs best in read-heavy or moderate-write workloads, but it is not designed to replace distributed or high-concurrency server databases.
SQLite is powerful but intentionally designed with certain limitations:
SQLite excels in embedded, local, and read-heavy use cases, but it is not a replacement for enterprise RDBMS platforms.
SQLite uses a layered memory management system to balance performance, portability, and low footprint.
Memory is used for:
SQLite allows memory behavior to be tuned via PRAGMA settings and compile-time options. It also supports memory-mapped I/O (mmap) to reduce copying and system calls on supported platforms.
Lookaside memory is a small, fixed-size memory pool used by SQLite to speed up frequent small memory allocations.
Key characteristics:
Lookaside memory is especially beneficial in embedded systems and mobile applications where allocation overhead is expensive.
SQLite handles schema changes conservatively to ensure data integrity and backward compatibility.
Key points:
sqlite_masterSQLite favors correctness over flexibility, which is why complex schema migrations often require explicit data copying.
Internally, ALTER TABLE often involves rebuilding the entire table:
Process:
This operation:
Recent SQLite versions optimize certain ALTER operations, but many still require full rewrites.
SQLite supports two enforcement modes for foreign key constraints:
Deferred constraints provide flexibility while maintaining consistency at commit time.
SQLite enforces foreign keys using internal triggers generated by the engine.
Mechanism:
This design allows SQLite to support foreign keys without adding complexity to the core execution engine.
The cost-based query optimizer is the component responsible for selecting the most efficient execution plan for a SQL query.
It evaluates:
SQLite generates multiple possible plans and chooses the one with the lowest estimated cost, rather than following fixed rules.
SQLite estimates query cost using:
Statistics collected via ANALYZE are stored in internal tables and used to predict:
Accurate statistics are critical for optimal query planning.
These are internal statistics tables populated by the ANALYZE command:
STAT tables significantly enhance query planner accuracy, especially for complex queries and skewed data distributions.
When the ANALYZE command is executed, SQLite scans tables and indexes to collect statistical metadata that helps the query planner make accurate cost estimations.
Internally:
sqlite_stat1, sqlite_stat3, and sqlite_stat4ANALYZE does not modify user data, only metadata. Running ANALYZE after large data changes significantly improves query planning accuracy and execution performance.
A covering index is an index that contains all the columns required to satisfy a query, eliminating the need to access the table itself.
Key characteristics:
SQLite automatically detects covering indexes and prefers them when cost estimation shows performance benefits.
Automatic index creation is an optimization feature where SQLite dynamically creates temporary indexes during query execution when it determines that doing so will reduce overall cost.
Key points:
This allows SQLite to optimize queries even when developers forget to create indexes.
SQLite creates transient (temporary) indexes when:
Transient indexes are discarded after query execution and help SQLite adapt dynamically to complex query patterns without schema changes.
Diagnosing slow queries in SQLite involves a combination of query analysis, instrumentation, and runtime observation.
Common techniques:
EXPLAIN QUERY PLANIn production, slow queries are often caused by missing indexes, excessive locking, or outdated statistics.
SQLite implements full-text search using FTS virtual tables, which maintain inverted indexes for fast text lookup.
Key features:
FTS enables high-performance text search while remaining fully embedded and portable.
FTS4 and FTS5 are different generations of SQLite’s full-text search engine.
FTS4
FTS5
FTS5 is recommended for new applications unless backward compatibility is required.
SQLite provides JSON support via a JSON extension, not a native JSON datatype.
Key characteristics:
This design keeps SQLite lightweight while enabling modern semi-structured data use cases.
Virtual tables are custom table implementations that allow SQLite to query external data sources as if they were regular tables.
Characteristics:
Examples include FTS tables, JSON tables, and external file-backed tables.
Key differences between virtual tables and regular tables:
Virtual tables are powerful tools for extending SQLite’s capabilities without modifying its core engine.
SQLite is designed to be highly extensible without increasing core complexity. Extensibility is achieved through well-defined APIs that allow developers to add new capabilities at runtime or compile time.
Key extensibility mechanisms include:
This modular architecture allows SQLite to remain lightweight while supporting advanced features when needed.
Loadable extensions are dynamically loaded shared libraries that extend SQLite’s functionality without modifying the core engine.
They can provide:
Extensions are loaded at runtime and are connection-specific. For security reasons, extension loading is often disabled by default in production builds and must be explicitly enabled.
SQLite security largely depends on how and where it is deployed, since it lacks built-in user authentication.
Key considerations:
SQLite is secure when properly configured, but responsibility lies primarily with the application and operating environment.
SQLite does not provide built-in encryption. Encryption is typically achieved using external solutions:
Common approaches:
Encryption must be applied carefully to avoid performance degradation and to ensure key management and recovery strategies are in place.
SQLite does not have dedicated DATE or TIME data types. Instead, date and time values are stored as:
SQLite provides built-in date and time functions to manipulate and convert between these formats. This flexible approach ensures portability but requires consistent usage conventions at the application level.
Common SQLite anti-patterns include:
Avoiding these anti-patterns ensures better performance, reliability, and scalability.
SQLite is not suitable when:
In such cases, server-based databases like PostgreSQL or MySQL are better choices.
Concurrency comparison highlights architectural differences:
SQLite prioritizes simplicity and reliability, while server databases prioritize scalability and concurrency.
Migration typically involves:
Care must be taken with type affinity, constraints, and SQL syntax differences.
Best practices include:
When used correctly, SQLite is extremely reliable, fast, and production-proven, even at massive scale in embedded and local-storage scenarios.
