{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-mariadb--server","slug":"mariadb--server","name":"server","type":"repo","url":"https://mariadb.org","page_url":"https://unfragile.ai/mariadb--server","categories":["search"],"tags":["amazon-web-services","database","fulltext-search","galera","geographical-information-system","innodb","json","mariadb","mysql","nearest-neighbor-search","rdbms","relational-databases","sql","storage-engine","vector-database"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"github-mariadb--server__cap_0","uri":"capability://code.generation.editing.sql.query.parsing.and.lexical.analysis.with.dialect.compatibility","name":"sql query parsing and lexical analysis with dialect compatibility","description":"MariaDB implements a bison-based SQL parser (sql_yacc.yy) coupled with a hand-coded lexer (sql_lex.h) that tokenizes and parses SQL statements into an abstract syntax tree (AST). The parser supports MySQL compatibility mode alongside MariaDB-specific extensions (Oracle PL/SQL compatibility, JSON operators, window functions). The lexer maintains state across multi-byte character sequences and handles dialect-specific keywords dynamically via the lex_keywords registry, enabling runtime switching between strict MySQL and extended MariaDB syntax without recompilation.","intents":["Parse incoming SQL statements from clients into executable query trees","Support both MySQL-compatible and MariaDB-extended SQL syntax in the same server instance","Handle edge cases like multi-byte UTF-8 identifiers and dialect-specific operators","Enable custom syntax extensions via the plugin system without modifying core parser"],"best_for":["Database administrators managing mixed MySQL/MariaDB workloads","Developers building MariaDB plugins that extend SQL syntax","Teams migrating from MySQL requiring backward compatibility"],"limitations":["Parser is monolithic bison grammar (~3000+ rules) making incremental syntax changes complex","No streaming parse mode — entire statement must be buffered before parsing begins","Dialect switching is per-connection, not per-statement, requiring reconnection for mode changes","Custom syntax extensions via plugins require careful keyword registration to avoid conflicts"],"requires":["C++ compiler with C++11 support","Bison 2.4+ for grammar compilation","MariaDB server initialization with sql_mode system variable set appropriately"],"input_types":["SQL text (UTF-8 encoded)","Binary protocol packets from clients"],"output_types":["Abstract syntax tree (SELECT_LEX, Item expression nodes)","Prepared statement metadata"],"categories":["code-generation-editing","parser-compiler"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mariadb--server__cap_1","uri":"capability://automation.workflow.multi.threaded.query.execution.with.thd.context.isolation","name":"multi-threaded query execution with thd context isolation","description":"MariaDB allocates a dedicated thread (THD — Thread Handler Descriptor) per client connection, encapsulating all per-connection state including the current query, transaction context, temporary tables, user variables, and execution statistics. The THD object serves as the central context passed through the entire SQL processing pipeline (parser → optimizer → executor → storage engine). Thread management uses a thread pool (configurable via thread_stack and thread_cache_size) with per-thread memory arenas to minimize allocation contention. Connection-level isolation is enforced through THD-scoped locks and transaction isolation levels (READ UNCOMMITTED through SERIALIZABLE).","intents":["Handle multiple concurrent client connections without cross-contamination of query state","Maintain per-connection transaction isolation and lock ownership","Manage temporary tables and session variables per connection","Enable graceful connection shutdown and resource cleanup"],"best_for":["Multi-user database deployments with concurrent client connections","Applications requiring strict transaction isolation","Systems with high connection churn (connection pooling scenarios)"],"limitations":["Each connection consumes thread_stack memory (default 256KB on 64-bit systems) — high connection counts require proportional memory","THD context switching adds ~5-10μs overhead per query due to TLS lookups and cache misses","No built-in connection pooling — applications must implement external pooling (e.g., ProxySQL, pgBouncer-style tools)","Thread-local storage lookups for THD can become bottleneck under extreme concurrency (10k+ connections)"],"requires":["Operating system with native thread support (pthreads on Linux, Windows threads on Windows)","Sufficient stack memory configured via thread_stack system variable","Client connection protocol support (TCP/IP or Unix socket)"],"input_types":["Client connection requests","SQL queries and commands"],"output_types":["Query results (row sets)","Status and error messages","Connection state metadata"],"categories":["automation-workflow","concurrency-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mariadb--server__cap_10","uri":"capability://code.generation.editing.prepared.statement.execution.with.parameter.binding.and.plan.caching","name":"prepared statement execution with parameter binding and plan caching","description":"MariaDB supports prepared statements (sql/sql_prepare.cc) that separate SQL parsing and optimization from execution. A prepared statement is parsed once and compiled into an execution plan, then executed multiple times with different parameter values. Parameters are bound via placeholders (?) in the SQL text, preventing SQL injection attacks. The prepared statement cache (sql_prepare_cache) stores compiled plans in memory, enabling fast re-execution without re-parsing. Prepared statements support both text protocol (PREPARE/EXECUTE statements) and binary protocol (COM_STMT_PREPARE, COM_STMT_EXECUTE). The optimizer generates a generic plan that works for all parameter values, or a specialized plan if parameter values significantly affect the plan (e.g., different indexes for different value ranges).","intents":["Execute the same query multiple times with different parameters efficiently","Prevent SQL injection attacks via parameter binding","Cache query plans to avoid re-parsing and re-optimization","Support both text and binary protocols for prepared statements"],"best_for":["Applications with repetitive queries (e.g., OLTP workloads)","Security-sensitive applications requiring SQL injection prevention","High-throughput systems where parsing overhead is significant"],"limitations":["Prepared statement cache is per-connection — plans are not shared across connections","Generic plans may be suboptimal for parameter values with different selectivity — no adaptive plan selection","Prepared statements cannot be used for DDL (CREATE TABLE, ALTER TABLE) — only DML and SELECT","Parameter binding is limited to scalar values — cannot bind table names or column names","Prepared statement cache size is limited (max_prepared_stmt_count) — large numbers of distinct statements can cause cache eviction","Plan caching adds memory overhead — each prepared statement consumes memory for the cached plan"],"requires":["Client support for prepared statements (most drivers support this)","System variables: max_prepared_stmt_count, sql_prepare_cache"],"input_types":["SQL text with parameter placeholders (?)","Parameter values (scalars)"],"output_types":["Prepared statement handle (statement ID)","Query results (rows)"],"categories":["code-generation-editing","query-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mariadb--server__cap_11","uri":"capability://code.generation.editing.stored.procedures.and.triggers.with.procedural.sql.execution","name":"stored procedures and triggers with procedural sql execution","description":"MariaDB supports stored procedures and triggers (sql/sp.cc, sql/sp_head.cc) that enable procedural SQL execution within the database. Stored procedures are compiled into an intermediate representation (Item tree) that is executed by a virtual machine (sp_instr_* classes). Procedures support control flow (IF, WHILE, LOOP, CASE), variables, cursors, and exception handling (DECLARE ... HANDLER). Triggers are automatically executed in response to table modifications (INSERT, UPDATE, DELETE) and can enforce business logic or maintain denormalized data. Both procedures and triggers are stored in the mysql.proc and mysql.trigger tables and are recompiled on first execution. The procedural engine is single-threaded (executes within the query thread) and does not support parallel execution.","intents":["Encapsulate business logic in the database for reusability and consistency","Enforce constraints and maintain data integrity via triggers","Reduce network round-trips by executing multiple SQL statements in a single procedure call","Support complex procedural logic (loops, conditionals, exception handling)"],"best_for":["Legacy applications with business logic in stored procedures","Systems requiring database-level enforcement of constraints","Workloads with complex multi-statement transactions"],"limitations":["Stored procedures are single-threaded — cannot parallelize execution across cores","Procedure compilation happens at first execution — adds latency to first call","Debugging stored procedures is difficult — limited debugging tools and error messages","Procedure performance is generally slower than equivalent application code — no JIT compilation","Cursor support is limited — cursors are forward-only and cannot be used in all contexts","Exception handling is basic (DECLARE ... HANDLER) — no stack traces or detailed error information"],"requires":["CREATE PROCEDURE or CREATE TRIGGER privilege","System tables: mysql.proc, mysql.trigger"],"input_types":["Procedure definition (SQL text with procedural syntax)","Procedure parameters (IN, OUT, INOUT)"],"output_types":["Procedure result sets (SELECT statements)","OUT parameter values","Trigger side effects (modified rows)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mariadb--server__cap_12","uri":"capability://data.processing.analysis.json.data.type.with.path.based.querying.and.manipulation","name":"json data type with path-based querying and manipulation","description":"MariaDB supports a native JSON data type (sql/json_*.cc) that stores JSON documents in a binary format for efficient storage and querying. JSON values are accessed via path expressions (e.g., json_col->'$.key.subkey') that navigate the JSON structure. The JSON type supports a rich set of functions for querying (JSON_EXTRACT, JSON_CONTAINS), manipulation (JSON_SET, JSON_REPLACE, JSON_REMOVE), and aggregation (JSON_ARRAYAGG, JSON_OBJECTAGG). JSON paths can be indexed via generated columns, enabling efficient queries on JSON fields. The JSON implementation uses a binary encoding that preserves the original JSON structure while enabling fast access to nested values without full parsing.","intents":["Store semi-structured data (JSON documents) in a relational database","Query JSON fields using path expressions","Manipulate JSON documents (add, remove, update fields)","Index JSON paths for efficient querying"],"best_for":["Applications with semi-structured data (e.g., API responses, configuration)","NoSQL-like use cases within a relational database","Systems requiring flexible schema evolution"],"limitations":["JSON indexing requires generated columns — adds storage overhead and maintenance cost","JSON path expressions are not as flexible as document databases (MongoDB) — no aggregation pipelines","JSON functions are limited compared to specialized JSON databases — no complex transformations","JSON validation is minimal — no schema validation (JSON Schema) support","JSON queries cannot use traditional SQL optimizations (e.g., join optimization) — treated as opaque values","JSON storage is binary — not human-readable in the database (requires JSON_PRETTY for display)"],"requires":["JSON data type support (MariaDB 10.2+)","System variables: json_* (for JSON function behavior)"],"input_types":["JSON text (valid JSON documents)","JSON path expressions"],"output_types":["JSON values (extracted from documents)","Modified JSON documents"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mariadb--server__cap_13","uri":"capability://data.processing.analysis.window.functions.with.frame.specification.and.partitioning","name":"window functions with frame specification and partitioning","description":"MariaDB supports SQL window functions (sql/window.cc) that perform calculations across a set of rows (window) related to the current row. Window functions include ranking (ROW_NUMBER, RANK, DENSE_RANK), aggregation (SUM, AVG, COUNT over windows), and offset functions (LAG, LEAD). Windows are defined via OVER clauses that specify partitioning (PARTITION BY) and ordering (ORDER BY). Frame specifications (ROWS BETWEEN ... AND ...) define the range of rows included in the window. Window functions are evaluated after GROUP BY but before ORDER BY, enabling complex analytical queries. The execution engine uses a streaming approach where rows are processed in order and window calculations are updated incrementally.","intents":["Perform ranking and numbering within partitions (ROW_NUMBER, RANK)","Calculate running totals and moving averages (SUM, AVG over windows)","Access previous/next rows (LAG, LEAD) for trend analysis","Implement complex analytical queries without self-joins"],"best_for":["Analytical and reporting queries","Time-series analysis (moving averages, running totals)","Ranking and leaderboard queries"],"limitations":["Window function execution is single-threaded — cannot parallelize across partitions","Large windows (millions of rows) can cause memory pressure — entire window must be buffered","Frame specifications with UNBOUNDED PRECEDING require buffering all rows — can be slow for large datasets","Window functions cannot be used in WHERE clauses — must use subqueries or CTEs for filtering","Nested window functions are not supported — cannot use window functions in window function definitions","Performance degrades with many partitions or complex frame specifications"],"requires":["MariaDB 10.2+ for window function support","OVER clause with PARTITION BY and/or ORDER BY"],"input_types":["Row data with partition and ordering columns","Window function specifications (OVER clauses)"],"output_types":["Window function results (rankings, aggregates, offset values)","Original row data"],"categories":["data-processing-analysis","query-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mariadb--server__cap_14","uri":"capability://data.processing.analysis.common.table.expressions.ctes.with.recursive.query.support","name":"common table expressions (ctes) with recursive query support","description":"MariaDB supports Common Table Expressions (CTEs) via the WITH clause, enabling named subqueries that can be referenced multiple times in a query. CTEs are useful for breaking complex queries into readable steps and avoiding code duplication. Recursive CTEs (WITH RECURSIVE) enable iterative computation — a base case (anchor member) is computed first, then the recursive member is applied repeatedly until no new rows are produced. Recursive CTEs are commonly used for hierarchical queries (organizational charts, category trees) and graph traversal. The execution engine uses a temporary table to store intermediate results from each iteration, with cycle detection to prevent infinite loops.","intents":["Break complex queries into readable named subqueries","Avoid code duplication by referencing CTEs multiple times","Traverse hierarchical data (organizational charts, category trees)","Implement graph algorithms (shortest path, connected components)"],"best_for":["Hierarchical data queries (organizational structures, category trees)","Graph traversal and analysis","Complex analytical queries requiring multiple steps"],"limitations":["Recursive CTEs can be slow for deep hierarchies (100+ levels) — each iteration requires a full table scan","Cycle detection adds overhead — requires tracking visited nodes to prevent infinite loops","CTE results are materialized (stored in temporary tables) — can consume significant memory for large result sets","Recursive CTEs cannot be optimized away — always execute the full recursion even if only a few rows are needed","No support for mutual recursion (multiple recursive CTEs referencing each other)","Performance degrades with complex recursive logic — no JIT compilation or optimization"],"requires":["MariaDB 10.2+ for CTE support","WITH clause syntax"],"input_types":["CTE definition (WITH clause)","Base case query (anchor member)","Recursive query (recursive member)"],"output_types":["CTE result set (rows from all iterations)","Final query result"],"categories":["data-processing-analysis","query-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mariadb--server__cap_2","uri":"capability://planning.reasoning.query.optimization.with.cost.based.join.ordering.and.range.analysis","name":"query optimization with cost-based join ordering and range analysis","description":"MariaDB's query optimizer (sql/opt_*.cc) implements a cost-based approach using table statistics (cardinality, index selectivity) to evaluate multiple join orderings and access paths. The optimizer performs range analysis (sql/opt_range.cc) to determine which index ranges satisfy WHERE clause predicates, then estimates I/O cost using a simplified model (random_page_read_cost, seq_read_cost system variables). Join ordering uses a greedy algorithm with branch-and-bound pruning to avoid exponential explosion on large joins. The optimizer also applies subquery flattening, derived table merging, and condition pushdown to simplify query plans before execution.","intents":["Automatically select efficient access paths (full table scan vs index range scan vs index merge)","Determine optimal join order for multi-table queries","Flatten subqueries and derived tables to enable better optimization","Estimate query cost to inform caching and execution decisions"],"best_for":["OLTP workloads with ad-hoc queries requiring automatic optimization","Applications with complex joins (5+ tables) where manual optimization is impractical","Systems where table statistics are regularly updated via ANALYZE TABLE"],"limitations":["Cost model is simplified (ignores CPU cost, assumes uniform random I/O) — estimates can be off by 2-10x for real workloads","Greedy join ordering can miss optimal plans for 10+ table joins (NP-hard problem)","Subquery optimization is conservative — some correlated subqueries cannot be flattened and execute as dependent subqueries","Statistics become stale if tables are not regularly analyzed — optimizer makes poor decisions on rapidly changing tables","No support for query hints to override optimizer decisions (unlike Oracle's /*+ */ syntax)"],"requires":["Table statistics populated via ANALYZE TABLE or automatic analysis","System variables configured: optimizer_search_depth, optimizer_prune_level","Access to table metadata (column types, index definitions)"],"input_types":["Parsed SQL query (SELECT_LEX tree)","Table statistics (cardinality, index selectivity)"],"output_types":["Query execution plan (JOIN_TAB array)","Access path decisions (index selection, join method)","Cost estimates"],"categories":["planning-reasoning","query-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mariadb--server__cap_3","uri":"capability://data.processing.analysis.innodb.storage.engine.with.acid.transactions.and.buffer.pool.management","name":"innodb storage engine with acid transactions and buffer pool management","description":"InnoDB is MariaDB's default transactional storage engine, implementing ACID guarantees through a multi-version concurrency control (MVCC) system, redo logging, and row-level locking. The buffer pool (configurable via innodb_buffer_pool_size) caches pages in memory using an LRU eviction policy with a young/old generation split to reduce scan pollution. Transactions are isolated via snapshot isolation (REPEATABLE READ) or locking (SERIALIZABLE), with deadlock detection via a wait-for graph. The redo log (innodb_log_file_size) enables crash recovery by replaying committed transactions. Data is organized in B+ tree indexes with clustered primary keys, enabling efficient range scans and point lookups.","intents":["Provide ACID-compliant data storage with crash recovery","Support concurrent transactions with isolation and consistency guarantees","Enable efficient point lookups and range scans via B+ tree indexes","Manage memory efficiently through intelligent buffer pool eviction"],"best_for":["OLTP applications requiring strong consistency and crash recovery","Multi-user systems with concurrent transactions","Applications with complex queries requiring efficient index access"],"limitations":["Buffer pool is global — no per-query or per-session memory limits, can cause OOM on large scans","MVCC overhead increases storage size (undo logs) and slows writes due to version chain traversal","Redo log is synchronous by default (innodb_flush_log_at_trx_commit=1) — adds ~5-10ms latency per commit","Deadlock detection is O(n²) in lock count — high-contention workloads can experience deadlock detection overhead","No built-in compression — large tables consume proportional disk space"],"requires":["Sufficient memory for innodb_buffer_pool_size (recommended: 50-80% of available RAM)","Disk space for redo logs (innodb_log_file_size × innodb_log_files_in_group)","Disk space for undo logs (proportional to transaction size and concurrency)"],"input_types":["INSERT, UPDATE, DELETE, SELECT statements","Transaction control commands (BEGIN, COMMIT, ROLLBACK)"],"output_types":["Row data (pages from buffer pool)","Transaction status (committed, rolled back)","Lock information (for deadlock detection)"],"categories":["data-processing-analysis","storage-engine"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mariadb--server__cap_4","uri":"capability://search.retrieval.full.text.search.indexing.and.query.execution","name":"full-text search indexing and query execution","description":"MariaDB implements full-text search (FTS) via inverted indexes that map keywords to document IDs and positions. FTS indexes are created on CHAR, VARCHAR, or TEXT columns and support boolean search (AND, OR, NOT operators), phrase search (quoted strings), and relevance ranking via BM25-style scoring. The FTS index is maintained incrementally during INSERT/UPDATE/DELETE operations, with a separate auxiliary table (FTS_*_INDEX_*) storing the inverted index. Query execution uses a two-phase approach: first, the FTS index identifies candidate documents; second, the main table is scanned to retrieve full rows and compute relevance scores. FTS supports both InnoDB and MyISAM storage engines with slightly different implementation details.","intents":["Index text columns for efficient keyword search without full table scans","Support complex search queries (boolean operators, phrase search, proximity)","Rank search results by relevance using statistical scoring","Enable natural language search with stop word filtering"],"best_for":["Content management systems with large text corpora","E-commerce platforms with product search","Document management systems requiring keyword search"],"limitations":["FTS index size is typically 20-30% of table size — significant storage overhead","Incremental index maintenance adds 5-15% write latency on INSERT/UPDATE/DELETE","Minimum word length (ft_min_word_len, default 4) excludes short keywords — not suitable for acronym search","No support for fuzzy matching or typo tolerance — requires external tools (Elasticsearch, Solr)","Relevance ranking is basic (BM25) — no machine learning or personalization","FTS index is not used for LIKE queries — requires explicit MATCH() syntax"],"requires":["FULLTEXT index created on target columns","Storage engine support (InnoDB or MyISAM)","System variables: ft_min_word_len, ft_max_word_len, ft_stopword_file"],"input_types":["Text data in CHAR, VARCHAR, or TEXT columns","MATCH() queries with boolean or natural language modes"],"output_types":["Matching document IDs","Relevance scores (MATCH() function result)","Row data from matched documents"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mariadb--server__cap_5","uri":"capability://automation.workflow.galera.cluster.replication.with.multi.master.synchronous.consistency","name":"galera cluster replication with multi-master synchronous consistency","description":"MariaDB Galera Cluster (via WSREP — Write Set Replication protocol) enables multi-master replication where all nodes can accept writes and maintain strong consistency. Write operations are executed locally first, then the write set (modified rows and their before/after images) is broadcast to all cluster nodes for certification (conflict detection) and application. Certification uses a deterministic algorithm to detect write conflicts (two transactions modifying the same row) and abort conflicting transactions on non-originating nodes. Replication is synchronous — a transaction commits only after all nodes acknowledge the write set, ensuring zero data loss. Cluster membership is managed via a gossip protocol with automatic node discovery and failure detection.","intents":["Replicate data across multiple database nodes with strong consistency","Enable multi-master writes without manual conflict resolution","Provide automatic failover and high availability","Detect and abort conflicting transactions deterministically"],"best_for":["High-availability deployments requiring zero data loss","Multi-region setups with local read/write access","Applications tolerating synchronous replication latency (typically 10-50ms)"],"limitations":["Synchronous replication adds 10-50ms latency per transaction (network round-trip to all nodes)","Write throughput is limited by slowest node (all nodes must apply write set) — not suitable for heterogeneous clusters","Certification overhead increases with write contention — high-conflict workloads can cause cascading aborts","Network partitions require manual intervention (quorum loss) — no automatic split-brain recovery","Galera replication is incompatible with traditional MySQL replication (cannot replicate to non-Galera nodes)","Large write sets (bulk inserts) can cause replication lag and memory pressure on receiving nodes"],"requires":["MariaDB 10.1+ with Galera plugin compiled","Network connectivity between all cluster nodes (low-latency, reliable)","Identical schema and data on all nodes (initial state synchronization via SST or IST)","System variables: wsrep_cluster_name, wsrep_cluster_address, wsrep_node_name"],"input_types":["Write operations (INSERT, UPDATE, DELETE)","Transaction control (BEGIN, COMMIT)"],"output_types":["Write set (modified rows with before/after images)","Certification result (committed or aborted)","Cluster membership changes"],"categories":["automation-workflow","replication-ha"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mariadb--server__cap_6","uri":"capability://tool.use.integration.pluggable.storage.engine.interface.with.handler.abstraction","name":"pluggable storage engine interface with handler abstraction","description":"MariaDB abstracts storage engine implementations behind a handler interface (sql/handler.h) that defines virtual methods for table operations (open, close, read, write, delete, scan). Storage engines implement the handler interface to provide their own data organization, indexing, and transaction semantics. The handler interface supports multiple storage engines coexisting in the same server instance, with per-table engine selection via the ENGINE clause in CREATE TABLE. The SQL layer communicates with storage engines through the handler API, passing query plans (JOIN_TAB) and receiving row data. Storage engines can register custom system variables, status variables, and information schema tables via the plugin system.","intents":["Support multiple storage engines (InnoDB, Aria, MyISAM, CONNECT) in the same server","Enable custom storage engine implementations without modifying core SQL layer","Provide a consistent interface for table operations across different engines","Allow per-table engine selection for workload-specific optimization"],"best_for":["Deployments with heterogeneous workloads (OLTP + analytics) requiring different engines","Developers building custom storage engines for specialized use cases","Systems requiring engine-specific features (e.g., InnoDB transactions, Aria compression)"],"limitations":["Handler interface is coarse-grained — engines cannot optimize for query-specific patterns without implementing full query execution","Cross-engine transactions are not supported — each engine manages its own transaction state","No standardized cost model — optimizer must estimate costs for each engine separately","Engine-specific features (e.g., InnoDB's online DDL) are not available for other engines","Switching engines requires table recreation (ALTER TABLE ENGINE=...) which locks the table"],"requires":["Storage engine compiled as a plugin or built into the server","Handler interface implementation (handler.h virtual methods)","Plugin registration via plugin_declare_plugin macro"],"input_types":["Table metadata (column definitions, indexes)","Query plans (JOIN_TAB with access paths)","Row data (INSERT, UPDATE, DELETE operations)"],"output_types":["Row data (from read operations)","Status information (row count, key statistics)","Error codes and messages"],"categories":["tool-use-integration","plugin-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mariadb--server__cap_7","uri":"capability://automation.workflow.binary.logging.for.replication.and.point.in.time.recovery","name":"binary logging for replication and point-in-time recovery","description":"MariaDB's binary log (sql/log.cc, sql/log_event.cc) records all data-modifying operations (INSERT, UPDATE, DELETE, DDL) in a sequential log file, enabling replication to slave nodes and point-in-time recovery (PITR). The binary log uses a row-based format (RBR) or statement-based format (SBF) to record changes. RBR logs the actual row modifications (before/after images), ensuring deterministic replication even with non-deterministic statements (e.g., RAND(), NOW()). SBF logs the original SQL statement, reducing log size but requiring deterministic execution. The binary log is organized into numbered files (mysql-bin.000001, mysql-bin.000002, etc.) with a corresponding index file. Replication slaves read the binary log and apply events to maintain a copy of the master's data.","intents":["Enable replication to slave nodes for read scaling and high availability","Support point-in-time recovery by replaying binary log events","Provide an audit trail of all data modifications","Enable backup and recovery workflows"],"best_for":["High-availability deployments with master-slave replication","Backup and recovery workflows requiring point-in-time restoration","Compliance scenarios requiring audit trails of data modifications"],"limitations":["Binary log is synchronous by default (binlog_format=ROW, sync_binlog=1) — adds 5-10ms latency per transaction","Row-based format can be verbose for bulk operations (e.g., INSERT ... SELECT) — log size can exceed statement-based format by 10-100x","Statement-based format is non-deterministic for certain operations (RAND(), NOW(), UDF calls) — can cause replication divergence","Binary log rotation is manual or time-based (expire_logs_days) — requires external management to prevent disk exhaustion","No built-in compression — binary logs consume significant disk space for high-volume workloads","Replication lag can occur if slave cannot keep up with master — no automatic throttling"],"requires":["Binary logging enabled (log_bin=ON)","Unique server ID (server_id system variable)","Sufficient disk space for binary logs (proportional to write volume)","System variables: binlog_format (ROW or STATEMENT), sync_binlog, expire_logs_days"],"input_types":["Data-modifying operations (INSERT, UPDATE, DELETE, DDL)","Transaction control (BEGIN, COMMIT, ROLLBACK)"],"output_types":["Binary log events (Query_log_event, Write_rows_event, Update_rows_event, etc.)","Binary log file positions (for replication offset tracking)"],"categories":["automation-workflow","replication-ha"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mariadb--server__cap_8","uri":"capability://data.processing.analysis.partitioning.with.automatic.partition.pruning.and.range.hash.list.strategies","name":"partitioning with automatic partition pruning and range/hash/list strategies","description":"MariaDB's partitioning system (sql/sql_partition_admin.h, sql/partition_info.cc) divides large tables into smaller partitions based on partition keys (columns or expressions). Supported partitioning strategies include RANGE (partition by value ranges), HASH (partition by hash function), LIST (partition by discrete values), and COLUMNS (partition by multiple columns). The optimizer performs partition pruning — analyzing WHERE clauses to determine which partitions contain matching rows — and only scans relevant partitions, reducing I/O. Partitions can be stored in different tablespaces or on different disks, enabling per-partition tuning. Partition maintenance operations (ADD PARTITION, DROP PARTITION, REORGANIZE PARTITION) are performed online without locking the entire table.","intents":["Divide large tables into smaller partitions for improved query performance","Prune partitions based on WHERE clauses to reduce I/O","Enable per-partition maintenance (archival, deletion, compression)","Distribute partitions across multiple disks for I/O parallelism"],"best_for":["Time-series data (e.g., logs, metrics) partitioned by date","Large tables (>100GB) where full table scans are prohibitive","Workloads with clear partition keys (e.g., customer_id, region)"],"limitations":["Partition pruning only works if WHERE clause references partition key — queries without partition key predicates scan all partitions","Partitioning adds complexity to query planning — optimizer must evaluate partition pruning for every query","Unique indexes must include partition key — cannot create unique indexes on non-partition columns","Cross-partition joins are inefficient — partitions are scanned independently without coordination","Partition maintenance (ADD, DROP, REORGANIZE) can be slow for large partitions","No automatic partition creation — requires manual management or external tools"],"requires":["Table created with PARTITION BY clause","Partition key column(s) defined in CREATE TABLE","System variables: default_storage_engine (for partition tablespaces)"],"input_types":["Partition key values (from WHERE clauses)","Partition maintenance commands (ALTER TABLE ... PARTITION)"],"output_types":["Partition list (from SHOW PARTITIONS)","Partition statistics (row count, size per partition)"],"categories":["data-processing-analysis","query-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mariadb--server__cap_9","uri":"capability://safety.moderation.authentication.and.access.control.with.role.based.permissions","name":"authentication and access control with role-based permissions","description":"MariaDB implements authentication via user accounts (user@host) with password verification and access control via a privilege system (sql/sql_acl.cc). Privileges are granted at multiple levels: global (all databases), database, table, column, and routine (stored procedures). The privilege system uses a role-based approach where roles are collections of privileges that can be assigned to users. Authentication supports multiple methods: native password hashing (mysql_native_password), SHA-256 hashing (sha256_password), and pluggable authentication (via auth plugins). Access control is enforced at query execution time — the SQL layer checks privileges before executing statements. Privilege information is cached in memory (acl_cache) for performance, with cache invalidation on privilege changes.","intents":["Authenticate users via password verification","Enforce fine-grained access control (table, column, routine level)","Manage roles and privilege inheritance","Support multiple authentication methods (passwords, LDAP, PAM)"],"best_for":["Multi-user deployments requiring fine-grained access control","Compliance scenarios requiring audit trails of privilege changes","Systems with complex organizational structures (roles, departments)"],"limitations":["Privilege checking is done at query execution time — no compile-time or plan-time optimization","Column-level privileges are expensive (checked for every column reference) — can add 5-10% overhead","Privilege cache is global — invalidation on any privilege change flushes entire cache, causing temporary slowdown","No support for dynamic privileges (privileges that depend on data values) — all privileges are static","Role inheritance is flat (no nested roles) — complex organizational structures require manual management","Password hashing is single-threaded — password verification can be bottleneck for high-concurrency authentication"],"requires":["User accounts created via CREATE USER or GRANT statements","Privileges granted via GRANT statements","System tables: mysql.user, mysql.db, mysql.tables_priv, mysql.columns_priv"],"input_types":["User credentials (username, password)","GRANT/REVOKE statements"],"output_types":["Authentication result (success/failure)","Privilege check result (allowed/denied)","Privilege information (SHOW GRANTS)"],"categories":["safety-moderation","access-control"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["C++ compiler with C++11 support","Bison 2.4+ for grammar compilation","MariaDB server initialization with sql_mode system variable set appropriately","Operating system with native thread support (pthreads on Linux, Windows threads on Windows)","Sufficient stack memory configured via thread_stack system variable","Client connection protocol support (TCP/IP or Unix socket)","Client support for prepared statements (most drivers support this)","System variables: max_prepared_stmt_count, sql_prepare_cache","CREATE PROCEDURE or CREATE TRIGGER privilege","System tables: mysql.proc, mysql.trigger"],"failure_modes":["Parser is monolithic bison grammar (~3000+ rules) making incremental syntax changes complex","No streaming parse mode — entire statement must be buffered before parsing begins","Dialect switching is per-connection, not per-statement, requiring reconnection for mode changes","Custom syntax extensions via plugins require careful keyword registration to avoid conflicts","Each connection consumes thread_stack memory (default 256KB on 64-bit systems) — high connection counts require proportional memory","THD context switching adds ~5-10μs overhead per query due to TLS lookups and cache misses","No built-in connection pooling — applications must implement external pooling (e.g., ProxySQL, pgBouncer-style tools)","Thread-local storage lookups for THD can become bottleneck under extreme concurrency (10k+ connections)","Prepared statement cache is per-connection — plans are not shared across connections","Generic plans may be suboptimal for parameter values with different selectivity — no adaptive plan selection","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6639427080000354,"quality":0.4,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-05-05T11:48:09.008Z","last_scraped_at":"2026-05-03T13:58:32.037Z","last_commit":"2026-05-03T09:37:10Z"},"community":{"stars":7527,"forks":2038,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mariadb--server","compare_url":"https://unfragile.ai/compare?artifact=mariadb--server"}},"signature":"5wbpoQfFu3+78xy3ZRhUFpDk+NGEbCV+c1rA0JTP3b0P0+pj0kBcDK9/ib+PXjkuWClLoKmlh666n8C+yqSaDg==","signedAt":"2026-06-21T11:00:19.789Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mariadb--server","artifact":"https://unfragile.ai/mariadb--server","verify":"https://unfragile.ai/api/v1/verify?slug=mariadb--server","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}