Query Parser Design and Implementation

Comprehensive low-level design for a robust query parsing system supporting SQL-like syntax with extensible grammar and efficient tokenization.

PublishedOctober 12, 2025
Length7,408 words
Reading time38 min read

Backend LLD

A query parser is a critical backend component that transforms human-readable query strings into structured parse trees for execution engines. This document presents a scalable, extensible design supporting complex query syntax with performance optimizations and error handling.

1. Problem Statement & Requirements

Clarifying questions

What type of queries are we parsing? Are we designing a parser for SQL, NoSQL (like MongoDB), or a custom DSL?
What is the expected scale of query volume? Are we targeting millions of queries per second or more?
What consistency requirements exist? Should parsing be strongly consistent, eventually consistent, or does it depend on query type?
What is the read/write ratio? Is parsing predominantly for reads, writes, or a balanced mix?
What are the latency sensitivity requirements? Are there strict SLAs for query parsing (e.g., <5ms p99)?
What authentication and authorization model is in place? Will all users have the same parsing permissions or will it be role-based?
What external systems integrate with this parser? Are there downstream systems that depend on parsing results (e.g., query optimizers, access control engines)?

Functional requirements

Parse structured query strings into internal representation objects (AST or similar)
Support multiple query formats (SQL, NoSQL, custom DSLs)
Validate parsed queries for syntax correctness and semantic validity
Transform parsed queries into executable query representations (e.g., AST, intermediate representation)
Handle complex query constructs like joins, subqueries, aggregations, and window functions
Support parameterized queries with proper escaping and binding
Provide detailed error messages for invalid query syntax or structure

Non-functional requirements

Latency targets: p50 < 2ms, p95 < 10ms, p99 < 50ms for query parsing
Throughput: Handle at least 100,000 queries per second (QPS) under load
Availability: 99.9% uptime with graceful degradation capability
Scalability: Support horizontal scaling across multiple instances
Memory usage: Max 50MB memory per parsing operation (avoid memory leaks)
Error handling: Parse errors should return structured error responses within 5ms

Constraints and assumptions

Parser will be part of a larger query engine or middleware system
Query strings are received as UTF-8 encoded strings (max 10KB per query)
Parsing must be deterministic and idempotent
No external dependencies beyond standard parsing libraries (e.g., ANTLR, Jison)
Parser will not handle query execution or optimization — only parsing and validation
Parsing is expected to be CPU-bound, not I/O bound

Out of scope

Query execution or optimization
Query result set processing or formatting
Real-time query monitoring or analytics
Query plan generation or visualization
Integration with specific database engines (e.g., PostgreSQL, MySQL)
Query rewriting or transformation for specific dialects (e.g., converting SQL to NoSQL)

2. API Design

API Style Selection

For a query parser system, I'll select REST API with JSON payloads for the following reasons:

Simplicity and wide adoption in backend systems
Clear HTTP semantics (GET/POST) for query operations
Easy integration with frontend and mobile clients
Standard tooling support (Swagger, Postman)
Better debugging experience compared to gRPC or GraphQL

Endpoint Definitions

Method	Path	Description	Auth
POST	`/v1/parse`	Parse a query string into structured format	JWT
GET	`/v1/validate`	Validate query syntax without parsing	JWT
POST	`/v1/transform`	Transform parsed query to different format (SQL, JSON)	JWT
GET	`/v1/syntax-tree`	Get AST representation of parsed query	JWT
POST	`/v1/validate-and-parse`	Validate and parse in single operation	JWT

Request and Response Schemas

Parse Request:

{
  "query": "SELECT * FROM users WHERE age > 25 AND status = 'active'",
  "format": "sql",
  "options": {
    "includeMetadata": true,
    "validateOnly": false
  }
}

Parse Response:

{
  "queryId": "uuid-12345",
  "parsedQuery": {
    "type": "SELECT",
    "fields": ["*"],
    "from": "users",
    "where": {
      "operator": "AND",
      "conditions": [
        {
          "field": "age",
          "operator": ">",
          "value": 25
        },
        {
          "field": "status",
          "operator": "=",
          "value": "active"
        }
      ]
    }
  },
  "metadata": {
    "parsedAt": "2023-10-15T14:30:00Z",
    "queryLength": 56,
    "tokensCount": 12
  }
}

Validation Response:

{
  "valid": true,
  "errors": [],
  "warnings": []
}

Transform Request:

{
  "parsedQuery": {
    "type": "SELECT",
    "fields": ["*"],
    "from": "users"
  },
  "targetFormat": "json"
}

HTTP Status Codes and Error Response Format

Standard HTTP status codes:

200: Success
400: Bad Request (invalid query format, validation errors)
401: Unauthorized (missing or invalid auth token)
403: Forbidden (insufficient permissions)
429: Rate Limited
500: Internal Server Error

Standardized error response:

{
  "error": {
    "code": "INVALID_QUERY",
    "message": "Query parsing failed: unexpected token at position 23",
    "details": {
      "position": 23,
      "token": "WHERE"
    },
    "timestamp": "2023-10-15T14:30:00Z"
  }
}

API Versioning Strategy

Versioning via path prefix: /v1/ to allow future evolution without breaking existing clients.

Pagination Design

Not applicable for query parsing operations since they are stateless and typically return single structured responses. However, if we were to support batch query parsing with large inputs:

Cursor-based pagination preferred over offset-based for better performance and consistency
Cursor would be a hash of the query or a token representing position in batch

Authentication and Authorization

Authentication: JWT tokens with standard OAuth2 flow
Authorization: RBAC model where users have roles like query_parser_user, admin, auditor
Scopes:
- query:read - read-only access to parse operations
- query:write - full access including validation and transformation

Rate Limiting Per Endpoint

Endpoint	Limit	Period	Enforcement
`/v1/parse`	1000 req/min	1 minute	In-memory or Redis-based
`/v1/validate`	2000 req/min	1 minute	In-memory or Redis-based
`/v1/transform`	500 req/min	1 minute	In-memory or Redis-based

Idempotency Design

For mutation endpoints, we'll implement idempotency key strategy:

Clients send X-Idempotency-Key header
Server stores the result for that key with TTL (e.g., 5 minutes)
If same key is used, return cached result instead of reprocessing
This is particularly useful for batch processing where same queries might be submitted multiple times

3. Data Model Design

Entity-Relationship Diagram

erDiagram
    QUERY_PARSER_CONFIG {
        string id PK
        string name
        string description
        jsonb parsing_rules
        boolean is_active
        timestamp created_at
        timestamp updated_at
        string created_by
        string updated_by
    }

    QUERY_TOKEN {
        string id PK
        string query_id FK
        string token_value
        string token_type
        integer position
        timestamp created_at
    }

    QUERY_AST_NODE {
        string id PK
        string query_id FK
        string parent_node_id FK
        string node_type
        string node_value
        integer depth
        timestamp created_at
    }

    QUERY_PARSE_RESULT {
        string id PK
        string query_id FK
        boolean is_valid
        jsonb parsed_ast
        string error_message
        timestamp created_at
    }

    QUERY_PARSER_CONFIG ||--o{ QUERY_TOKEN : "has"
    QUERY_PARSER_CONFIG ||--o{ QUERY_AST_NODE : "has"
    QUERY_PARSER_CONFIG ||--o{ QUERY_PARSE_RESULT : "produces"
    QUERY_TOKEN ||--o{ QUERY_AST_NODE : "transforms"

Table / Collection Schema Definitions

`query_parser_config` table

CREATE TABLE query_parser_config (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(255) NOT NULL,
    description TEXT,
    parsing_rules JSONB NOT NULL,
    is_active BOOLEAN DEFAULT TRUE,
    created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
    created_by VARCHAR(255),
    updated_by VARCHAR(255),
    
    -- Indexes
    INDEX idx_query_parser_config_name (name),
    INDEX idx_query_parser_config_active (is_active),
    INDEX idx_query_parser_config_created_at (created_at)
);

`query_token` table

CREATE TABLE query_token (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    query_id UUID NOT NULL REFERENCES query_parser_config(id) ON DELETE CASCADE,
    token_value TEXT NOT NULL,
    token_type VARCHAR(50) NOT NULL,
    position INTEGER NOT NULL,
    created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
    
    -- Indexes
    INDEX idx_query_token_query_id (query_id),
    INDEX idx_query_token_position (position),
    INDEX idx_query_token_type (token_type)
);

`query_ast_node` table

CREATE TABLE query_ast_node (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    query_id UUID NOT NULL REFERENCES query_parser_config(id) ON DELETE CASCADE,
    parent_node_id UUID REFERENCES query_ast_node(id),
    node_type VARCHAR(100) NOT NULL,
    node_value TEXT,
    depth INTEGER NOT NULL DEFAULT 0,
    created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
    
    -- Indexes
    INDEX idx_query_ast_node_query_id (query_id),
    INDEX idx_query_ast_node_parent_node_id (parent_node_id),
    INDEX idx_query_ast_node_node_type (node_type),
    INDEX idx_query_ast_node_depth (depth)
);

`query_parse_result` table

CREATE TABLE query_parse_result (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    query_id UUID NOT NULL REFERENCES query_parser_config(id) ON DELETE CASCADE,
    is_valid BOOLEAN NOT NULL DEFAULT FALSE,
    parsed_ast JSONB,
    error_message TEXT,
    created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
    
    -- Indexes
    INDEX idx_query_parse_result_query_id (query_id),
    INDEX idx_query_parse_result_is_valid (is_valid),
    INDEX idx_query_parse_result_created_at (created_at)
);

Primary Key Strategy

UUID as Primary Key - Selected for the following reasons:

Distributed system compatibility: UUIDs can be generated without coordination across services
Scalability: No need for centralized ID generation, avoiding single points of failure
Security: UUIDs don't reveal information about the creation order or system
Cross-service joins: Easier to join across distributed systems without ID conflicts

Index Design

Composite Indexes

query_token - (query_id, position) - For efficient token retrieval in order
query_ast_node - (query_id, depth) - For AST traversal by query context

Partial Indexes

query_parser_config - WHERE is_active = TRUE - For fast lookup of active parsers
query_parse_result - WHERE is_valid = TRUE - For fast lookup of valid parse results

Covering Indexes

query_token - (query_id, token_type, position) - Covers token type filtering queries
query_ast_node - (query_id, node_type) - Covers AST type filtering queries

Data Partitioning / Sharding Strategy

Time-based sharding by created_at field:

Partition by month to support query volume growth
Each partition stores data for a specific time range (e.g., 2024-01, 2024-02)
Allows for easy data lifecycle management and query performance optimization

Soft Delete vs. Hard Delete Decision

Soft Delete - Implemented:

All tables use deleted_at timestamp field (not shown in schema for brevity)
Soft delete allows for audit trail preservation and data recovery
Enables rollback of configuration changes without data loss
Supports compliance requirements for data retention

Audit Trail Design

All tables include:

created_at / updated_at timestamps
created_by / updated_by user identifiers
created_at timestamp with time zone for accurate audit logging

Data Archival Strategy and Retention Policy

Archival Strategy:

Active Parser Configurations: Retained indefinitely with version control
Parse Results:
- 30 days for successful parses (active queries)
- 90 days for failed parses (error analysis)
Token and AST data:
- 7 days for active query parsing (performance analysis)
- 30 days for historical analysis

Archival Process:

Scheduled nightly job to move old data to archive storage (S3 or similar)
Metadata retained in database for query performance
Archive data accessible via separate read-only endpoint

-- Example archival trigger logic (simplified)
CREATE OR REPLACE FUNCTION archive_old_parse_results()
RETURNS TRIGGER AS $$
BEGIN
    IF OLD.created_at < CURRENT_DATE - INTERVAL '30 days' THEN
        -- Move to archive table or external storage
        INSERT INTO query_parse_result_archive SELECT * FROM query_parse_result WHERE id = OLD.id;
        DELETE FROM query_parse_result WHERE id = OLD.id;
    END IF;
    RETURN NULL;
END;
$$ LANGUAGE plpgsql;

4. Class Design & Service Layer

classDiagram
    class QueryParser {
        +parse(String input)
        +validate(QueryAST ast)
        +transform(QueryAST ast)
    }
    
    class QueryAST {
        -nodes: List[ASTNode]
        +addNode(ASTNode node)
        +getNodes(): List[ASTNode]
    }
    
    class ASTNode {
        -type: NodeType
        -value: String
        -children: List[ASTNode]
        +addChild(ASTNode child)
    }
    
    class ParserException {
        -message: String
        -errorCode: String
    }
    
    class QueryValidator {
        +validate(QueryAST ast)
        -validateSyntax(QueryAST ast)
        -validateSemantics(QueryAST ast)
    }
    
    class QueryTransformer {
        +transform(QueryAST ast)
        -toSQL(QueryAST ast)
        -toGraphQL(QueryAST ast)
    }
    
    class QueryContext {
        -parameters: Map[String, Object]
        -userPermissions: Set[Permission]
        -queryType: QueryType
    }
    
    class NodeType {
        +SELECT
        +WHERE
        +JOIN
        +ORDER_BY
    }
    
    class QueryType {
        +SQL
        +GRAPHQL
        +MONGO
    }
    
    QueryParser --> QueryAST : builds
    QueryParser --> QueryValidator : validates
    QueryParser --> QueryTransformer : transforms
    QueryAST --> ASTNode : contains
    QueryValidator --> ParserException : throws
    QueryTransformer --> ParserException : throws
    QueryContext --> QueryType : supports

stateDiagram-v2
    [*] --> Idle
    Idle --> Parsing : parse()
    Parsing --> Validating : validate()
    Validating --> Transforming : transform()
    Transforming --> Success : success()
    Parsing --> Error : parseError()
    Validating --> Error : validationError()
    Transforming --> Error : transformationError()
    Error --> Idle : reset()

Service Class Structure and Responsibilities

The QueryParser service is the main entry point for query processing. It orchestrates parsing, validation, and transformation phases.

Key service classes:

QueryParser - Main orchestrator
QueryValidator - Syntax and semantic validation
QueryTransformer - Format conversion (SQL, GraphQL, etc.)
QueryContext - Request context and permissions

Interface / Abstract Class Design

public interface QueryParserStrategy {
    QueryAST parse(String input) throws ParserException;
    String getSupportedFormat();
}

public abstract class AbstractQueryParser implements QueryParserStrategy {
    protected final QueryValidator validator;
    protected final QueryTransformer transformer;
    
    public AbstractQueryParser(QueryValidator validator, QueryTransformer transformer) {
        this.validator = validator;
        this.transformer = transformer;
    }
    
    @Override
    public QueryAST parse(String input) throws ParserException {
        QueryAST ast = doParse(input);
        validator.validate(ast);
        return ast;
    }
    
    protected abstract QueryAST doParse(String input) throws ParserException;
}

public interface QueryTransformer {
    String transform(QueryAST ast, QueryContext context) throws ParserException;
    String getOutputFormat();
}

public interface QueryValidator {
    void validate(QueryAST ast) throws ParserException;
}

Business Logic Encapsulation Patterns

Strategy Pattern Implementation:

public class SQLQueryParser extends AbstractQueryParser {
    public SQLQueryParser(QueryValidator validator, QueryTransformer transformer) {
        super(validator, transformer);
    }
    
    @Override
    protected QueryAST doParse(String input) throws ParserException {
        // Implementation of SQL parsing logic
        return parseSQL(input);
    }
    
    @Override
    public String getSupportedFormat() {
        return "SQL";
    }
    
    private QueryAST parseSQL(String input) throws ParserException {
        // Tokenizer and AST construction logic
        return new QueryAST();
    }
}

public class GraphQLQueryParser extends AbstractQueryParser {
    public GraphQLQueryParser(QueryValidator validator, QueryTransformer transformer) {
        super(validator, transformer);
    }
    
    @Override
    protected QueryAST doParse(String input) throws ParserException {
        // Implementation of GraphQL parsing logic
        return parseGraphQL(input);
    }
    
    @Override
    public String getSupportedFormat() {
        return "GRAPHQL";
    }
    
    private QueryAST parseGraphQL(String input) throws ParserException {
        // Tokenizer and AST construction logic
        return new QueryAST();
    }
}

Critical-Path Method Implementation

public class QueryParserService {
    private final Map<String, QueryParserStrategy> parsers;
    private final QueryValidator validator;
    private final QueryTransformer transformer;
    private final EventPublisher eventPublisher;
    
    public String processQuery(String input, QueryContext context) throws ParserException {
        // Transaction boundary - all operations in single DB transaction
        try (Transaction tx = transactionManager.begin()) {
            // 1. Parse query into AST
            QueryAST ast = parseQuery(input, context.getFormat());
            
            // 2. Validate query with permissions check
            validateQuery(ast, context);
            
            // 3. Transform to target format
            String result = transformQuery(ast, context);
            
            // 4. Publish query event for audit/logging
            eventPublisher.publish(new QueryProcessedEvent(
                context.getUserId(), 
                context.getQueryType().toString(),
                input.length(),
                System.currentTimeMillis()
            ));
            
            // 5. Commit transaction
            tx.commit();
            
            return result;
        } catch (Exception e) {
            // Rollback on any failure
            throw new ParserException("Failed to process query", e);
        }
    }
    
    private QueryAST parseQuery(String input, String format) throws ParserException {
        QueryParserStrategy parser = parsers.get(format);
        if (parser == null) {
            throw new ParserException("Unsupported query format: " + format);
        }
        return parser.parse(input);
    }
    
    private void validateQuery(QueryAST ast, QueryContext context) throws ParserException {
        // Check user permissions
        if (!hasPermission(context, ast)) {
            throw new ParserException("Insufficient permissions for query");
        }
        
        // Validate AST structure and content
        validator.validate(ast);
    }
    
    private String transformQuery(QueryAST ast, QueryContext context) throws ParserException {
        return transformer.transform(ast, context);
    }
    
    private boolean hasPermission(QueryContext context, QueryAST ast) {
        // Implementation of permission checking logic
        return true;
    }
}

Transaction Boundary Design

Single transaction encompasses:

Query parsing and AST construction
Validation with permission checks
Format transformation
Event publishing (if using outbox pattern)
Commit/rollback based on success/failure

Service Interface Contracts

public interface QueryParsingService {
    String processQuery(String input, QueryContext context) throws ParserException;
    QueryAST parse(String input, String format) throws ParserException;
    void validate(QueryAST ast, QueryContext context) throws ParserException;
    String transform(QueryAST ast, QueryContext context) throws ParserException;
}

Saga / Orchestration Pattern

For distributed query processing:

Query parsing happens in a single transactional boundary
If external systems are involved (e.g., schema validation), use saga pattern
Each step is idempotent and can be retried

Domain Object / Value Object Design

public class QueryAST {
    private final List<ASTNode> nodes;
    private final String originalInput;
    
    public QueryAST(String originalInput) {
        this.originalInput = originalInput;
        this.nodes = new ArrayList<>();
    }
    
    public void addNode(ASTNode node) {
        this.nodes.add(node);
    }
    
    public List<ASTNode> getNodes() {
        return new ArrayList<>(nodes);
    }
}

public class ASTNode {
    private final NodeType type;
    private final String value;
    private final List<ASTNode> children;
    
    public ASTNode(NodeType type, String value) {
        this.type = type;
        this.value = value;
        this.children = new ArrayList<>();
    }
    
    public void addChild(ASTNode child) {
        this.children.add(child);
    }
    
    // Getters and equals/hashCode
}

public enum NodeType {
    SELECT, WHERE, JOIN, ORDER_BY, GROUP_BY, HAVING, LIMIT
}

public enum QueryType {
    SQL, GRAPHQL, MONGO, CQL
}

Implementation Notes

Thread Safety: Service instances are stateless and thread-safe
Performance: AST nodes are immutable to prevent race conditions
Extensibility: Strategy pattern allows easy addition of new query formats
Error Handling: All operations throw structured ParserException with error codes
Logging: Span and trace IDs included in all operation logs for observability

5. Concurrency & Thread Safety

Concurrency Model

The query parser system operates in a multi-threaded environment where multiple parsing requests can be processed concurrently. The concurrency model leverages Java's thread pool executor with a fixed-size thread pool to manage parsing tasks efficiently. Each parsing task is executed in a separate thread from the pool, ensuring that concurrent parsing operations don't block each other.

// Thread pool configuration for query parsing
ExecutorService parserThreadPool = Executors.newFixedThreadPool(
    Runtime.getRuntime().availableProcessors() * 2,
    new ThreadFactoryBuilder()
        .setNameFormat("query-parser-%d")
        .setDaemon(false)
        .build()
);

Race Condition Prevention Strategy

Race conditions are mitigated through proper synchronization mechanisms and immutable data structures. Parser state is maintained in thread-local variables to prevent cross-thread contamination. For shared resources like configuration caches or grammar rule sets, read-write locks are used to allow concurrent reads while ensuring exclusive access for writes.

Optimistic vs. Pessimistic Locking Selection

For the query parser, pessimistic locking is not required as parsing operations are primarily read-heavy and stateless. Each query parsing task operates on its own isolated input context, making optimistic concurrency control sufficient. The system uses immutable data structures for parsed query components to prevent any race conditions during concurrent parsing.

Deadlock Prevention Strategy

To prevent deadlocks in the parser system:

All locks are acquired in a consistent order across all parsing threads
Timeout mechanisms are implemented for lock acquisition to prevent indefinite blocking
Parser tasks are designed to minimize lock contention by using thread-local state where possible

Idempotency Implementation for Concurrent Duplicate Requests

For concurrent duplicate query parsing requests, the system implements idempotency through:

Query fingerprinting using hash-based identifiers
Concurrent cache lookup for already-parsed queries
Atomic operations for cache updates to prevent race conditions

// Idempotency implementation example
public class QueryParser {
    private final Map<String, ParseResult> cache = new ConcurrentHashMap<>();
    private final ReadWriteLock lock = new ReentrantReadWriteLock();
    
    public ParseResult parseQuery(String query) {
        String fingerprint = generateFingerprint(query);
        
        // Check cache first (read lock)
        ParseResult cached = cache.get(fingerprint);
        if (cached != null) {
            return cached;
        }
        
        // Acquire write lock for cache update
        lock.writeLock().lock();
        try {
            cached = cache.get(fingerprint);
            if (cached != null) {
                return cached;
            }
            
            ParseResult result = doParse(query);
            cache.put(fingerprint, result);
            return result;
        } finally {
            lock.writeLock().unlock();
        }
    }
}

The implementation uses a concurrent hash map for fast cache lookups and read-write locks for safe updates to prevent race conditions during cache population. This ensures that duplicate parsing requests return the same result without reprocessing, while maintaining thread safety for concurrent access.

6. Sequence Diagrams for Key Flows

sequenceDiagram
    participant Client
    participant QueryParser
    participant ASTNode
    participant Tokenizer
    participant ParserEngine

    Client->>QueryParser: Send query string "SELECT * FROM users WHERE age > 25"
    QueryParser->>Tokenizer: tokenize(queryString)
    Tokenizer-->>QueryParser: List<Token>
    QueryParser->>ParserEngine: parse(tokens)
    ParserEngine->>ASTNode: buildAST(tokens)
    ASTNode-->>ParserEngine: ASTRoot
    ParserEngine-->>QueryParser: ParsedQuery
    QueryParser-->>Client: ParsedQuery object

sequenceDiagram
    participant Client
    participant QueryParser
    participant ErrorLogger
    participant ExceptionHandler

    Client->>QueryParser: Send malformed query "SELECT * FROM users WHERE age >"
    QueryParser->>Tokenizer: tokenize(queryString)
    Tokenizer-->>QueryParser: TokenizationError
    QueryParser->>ExceptionHandler: handleException(tokenizationError)
    ExceptionHandler->>ErrorLogger: logError(tokenizationError)
    ErrorLogger-->>ExceptionHandler: error logged
    ExceptionHandler-->>QueryParser: return ParseError
    QueryParser-->>Client: ParseError response with status 400

sequenceDiagram
    participant Client
    participant QueryParser
    participant AsyncProcessor
    participant EventPublisher

    Client->>QueryParser: Send complex query "SELECT u.name, COUNT(*) FROM users u JOIN orders o ON u.id = o.user_id WHERE u.status = 'active' GROUP BY u.name HAVING COUNT(*) > 10"
    QueryParser->>AsyncProcessor: submitToAsyncProcessing(parsedQuery)
    AsyncProcessor->>EventPublisher: publishParsingEvent(parsedQuery)
    EventPublisher-->>AsyncProcessor: event published
    AsyncProcessor-->>QueryParser: async processing started
    QueryParser-->>Client: Accepted (202) with job ID
    AsyncProcessor->>QueryParser: notifyWhenComplete(jobId)
    QueryParser-->>Client: Final result when ready

7. Caching Design

Cache Strategy Selection

For query parsing, we'll use a cache-aside pattern with LRU eviction policy. This approach provides the best balance between performance and cache consistency for a query parser system where:

Query parsing is CPU-intensive but results are deterministic
Cache misses should be rare (90%+ hit rate target)
Query results are immutable once parsed
We need to handle cache warming for high-volume queries

Cache Key Design

Cache keys will follow a structured namespace pattern:

query-parser:{query_hash}:{version}:result

Where:

query_hash is a SHA-256 hash of the normalized query string
version is the parser version (e.g., "v1.2")
result indicates the cached result type

public class QueryCacheKey {
    private final String queryHash;
    private final String version;
    
    public QueryCacheKey(String query, String version) {
        this.queryHash = hashQuery(query);
        this.version = version;
    }
    
    public String getCacheKey() {
        return String.format("query-parser:%s:%s:result", queryHash, version);
    }
    
    private String hashQuery(String query) {
        return DigestUtils.sha256Hex(query.trim().toLowerCase());
    }
}

TTL Strategy

Data Type	TTL	Reason
Parsed Query AST	30 minutes	Frequent queries with stable parsing rules
Query Statistics	1 hour	Aggregated usage patterns change slowly
Parser Configuration	24 hours	Configuration changes are infrequent
Error Parsing Results	5 minutes	Failed queries may resolve quickly

Cache Eviction Policy

LRU (Least Recently Used) with probabilistic early expiry to prevent stampedes:

public class CacheEvictionManager {
    private final int maxEntries = 10000;
    private final double earlyExpiryProbability = 0.1;
    
    public boolean shouldEvict(String key) {
        return random.nextDouble() < earlyExpiryProbability;
    }
}

Cache Stampede Prevention

Implement probabilistic early expiry with distributed locks:

public class CacheStampedePrevention {
    private final RedisTemplate<String, String> redisTemplate;
    
    public boolean acquireLock(String key, int timeoutSeconds) {
        String lockKey = "lock:" + key;
        String lockValue = UUID.randomUUID().toString();
        
        Boolean acquired = redisTemplate.opsForValue()
            .setIfAbsent(lockKey, lockValue, timeoutSeconds, TimeUnit.SECONDS);
            
        return acquired != null && acquired;
    }
    
    public void releaseLock(String key, String lockValue) {
        String lockKey = "lock:" + key;
        String script = 
            "if redis.call('get', KEYS[1]) == ARGV[1] then " +
            "return redis.call('del', KEYS[1]) else return 0 end";
        
        redisTemplate.execute(
            new DefaultRedisScript<>(script, Long.class),
            Collections.singletonList(lockKey),
            lockValue
        );
    }
}

Distributed Cache Architecture

Using Redis Cluster with:

3 master nodes for read/write
3 replica nodes for high availability
100MB per node cache size limit
Redis Sentinel for automatic failover

Cache Invalidation Strategy

Event-driven invalidation with TTL fallback:

@Component
public class QueryCacheInvalidator {
    private final RedisTemplate<String, String> redisTemplate;
    private final EventBus eventBus;
    
    @Subscribe
    public void handleParserUpdate(ParserUpdateEvent event) {
        // Invalidate all cached queries for this parser version
        String pattern = "query-parser:*:" + event.getParserVersion() + ":*";
        Set<String> keys = redisTemplate.keys(pattern);
        
        if (!keys.isEmpty()) {
            redisTemplate.delete(keys);
        }
    }
    
    @Subscribe
    public void handleQueryChange(QueryChangeEvent event) {
        // Invalidate specific query cache using hash
        String key = new QueryCacheKey(event.getQuery(), event.getParserVersion()).getCacheKey();
        redisTemplate.delete(key);
    }
}

Cache Hit/Miss Tracking

@Component
public class QueryCacheMetrics {
    private final MeterRegistry meterRegistry;
    
    public void recordCacheHit(String queryHash) {
        Counter.builder("query_parser_cache_hits")
            .tag("query_hash", queryHash)
            .register(meterRegistry)
            .increment();
    }
    
    public void recordCacheMiss(String queryHash) {
        Counter.builder("query_parser_cache_misses")
            .tag("query_hash", queryHash)
            .register(meterRegistry)
            .increment();
    }
}

8. Repository / Data Access Layer

Repository Pattern Implementation

The repository pattern is implemented to abstract data access logic and provide a clean separation between domain objects and persistence concerns. Each entity will have its own repository interface and implementation.

public interface QueryParserRepository {
    Query parseQuery(String input);
    List<Query> findAll();
    void save(Query query);
    void delete(UUID id);
}

@Repository
public class QueryParserRepositoryImpl implements QueryParserRepository {
    
    private final EntityManager entityManager;
    private final QueryParserMapper queryParserMapper;
    
    public QueryParserRepositoryImpl(EntityManager entityManager, 
                                   QueryParserMapper queryParserMapper) {
        this.entityManager = entityManager;
        this.queryParserMapper = queryParserMapper;
    }
    
    @Override
    public Query parseQuery(String input) {
        // Implementation depends on query parsing logic
        return null;
    }
    
    @Override
    public List<Query> findAll() {
        TypedQuery<QueryEntity> query = entityManager.createQuery(
            "SELECT q FROM QueryEntity q", QueryEntity.class);
        return query.getResultList().stream()
            .map(queryParserMapper::toDomain)
            .collect(Collectors.toList());
    }
    
    @Override
    public void save(Query query) {
        QueryEntity entity = queryParserMapper.toEntity(query);
        entityManager.persist(entity);
    }
    
    @Override
    public void delete(UUID id) {
        QueryEntity entity = entityManager.find(QueryEntity.class, id);
        if (entity != null) {
            entityManager.remove(entity);
        }
    }
}

ORM vs Raw Query Decisions

For query parsing system, we'll use JPA/Hibernate as the ORM for its type safety and mapping capabilities. However, complex parsing operations will use raw SQL for performance:

JPA/Hibernate: Used for simple CRUD operations and basic queries
Raw SQL: Used for complex parsing logic that benefits from direct database optimization

// Raw query for performance-critical parsing operations
@Query(value = "SELECT * FROM parsed_queries WHERE status = :status AND created_at > :timestamp", 
       nativeQuery = true)
List<QueryEntity> findRecentParsedQueries(@Param("status") String status, 
                                         @Param("timestamp") LocalDateTime timestamp);

Read vs Write Separation

For high-throughput query parsing, we'll implement CQRS pattern:

public interface QueryReadRepository {
    Query findById(UUID id);
    List<Query> findByStatus(String status);
    Page<Query> findWithPagination(Pageable pageable);
}

public interface QueryWriteRepository {
    void save(Query query);
    void update(Query query);
    void delete(UUID id);
}

Connection Pool Configuration

Connection pool is configured with HikariCP for optimal performance:

@Configuration
public class DatabaseConfig {
    
    @Bean
    public HikariDataSource dataSource() {
        HikariConfig config = new HikariConfig();
        config.setJdbcUrl("jdbc:postgresql://localhost:5432/queryparser");
        config.setUsername("user");
        config.setPassword("password");
        config.setMaximumPoolSize(20);
        config.setMinimumIdle(5);
        config.setConnectionTimeout(30000);
        config.setIdleTimeout(600000);
        config.setMaxLifetime(1800000);
        config.setLeakDetectionThreshold(60000);
        return new HikariDataSource(config);
    }
}

N+1 Query Prevention Strategy

We implement eager loading and batch fetching to prevent N+1 queries:

@Entity
@Table(name = "parsed_queries")
@NamedEntityGraph(
    name = "Query.withDetails",
    attributeNodes = @NamedAttributeNode(value = "parsedTokens", fetchType = FetchType.EAGER)
)
public class QueryEntity {
    @Id
    private UUID id;
    
    @OneToMany(mappedBy = "query", cascade = CascadeType.ALL, fetch = FetchType.LAZY)
    @BatchSize(size = 20)
    private List<TokenEntity> parsedTokens;
    
    // Other fields...
}

// In repository query with eager loading
@Query("SELECT q FROM QueryEntity q JOIN FETCH q.parsedTokens WHERE q.id = :id")
QueryEntity findWithTokens(@Param("id") UUID id);

Batch Operation Design

Batch operations are implemented for bulk parsing:

@Repository
public class BatchQueryParserRepository {
    
    @PersistenceContext
    private EntityManager entityManager;
    
    public void batchSave(List<Query> queries) {
        int batchSize = 1000;
        
        for (int i = 0; i < queries.size(); i += batchSize) {
            int end = Math.min(i + batchSize, queries.size());
            List<Query> batch = queries.subList(i, end);
            
            // Batch persist using native SQL for better performance
            String sql = "INSERT INTO parsed_queries (id, query_string, status) VALUES (?, ?, ?)";
            try (PreparedStatement stmt = entityManager.unwrap(Connection.class).prepareStatement(sql)) {
                for (Query query : batch) {
                    stmt.setObject(1, query.getId());
                    stmt.setString(2, query.getOriginalString());
                    stmt.setString(3, query.getStatus().toString());
                    stmt.addBatch();
                }
                stmt.executeBatch();
            } catch (SQLException e) {
                throw new DataAccessException("Failed to batch save queries", e);
            }
        }
    }
}

9. Asynchronous Processing & Messaging

Event-driven boundaries in this feature — what triggers async vs. what stays synchronous, and why

For a query parser system, most operations should remain synchronous to maintain low latency for immediate query execution. However, asynchronous processing is beneficial for:

Query result caching invalidation - When query results change due to data updates, we asynchronously invalidate cached queries to prevent serving stale data
Audit logging and analytics - Query execution metadata should be logged asynchronously to avoid impacting query response times
Query optimization recommendations - Analyzing query performance patterns and generating optimization suggestions asynchronously

The synchronous flow handles immediate query parsing, validation, and execution. Asynchronous flows are used for secondary operations that don't block the primary user experience.

Message schema design with a sample payload (Avro / Protobuf / JSON Schema — with versioning strategy)

{
  "type": "object",
  "title": "QueryEvent",
  "version": "1.0.0",
  "properties": {
    "eventId": {
      "type": "string",
      "description": "Unique identifier for this event"
    },
    "eventType": {
      "type": "string",
      "enum": ["QUERY_EXECUTED", "QUERY_CACHE_INVALIDATED", "QUERY_OPTIMIZATION_SUGGESTED"]
    },
    "queryId": {
      "type": "string",
      "description": "Unique identifier for the query"
    },
    "userId": {
      "type": "string",
      "description": "User who executed the query"
    },
    "queryText": {
      "type": "string",
      "description": "Original query text"
    },
    "parsedQuery": {
      "type": "object",
      "description": "Parsed query structure"
    },
    "executionTimeMs": {
      "type": "integer",
      "description": "Query execution time in milliseconds"
    },
    "timestamp": {
      "type": "string",
      "format": "date-time"
    }
  },
  "required": ["eventId", "eventType", "queryId", "timestamp"]
}

Versioning strategy uses semantic versioning with backward compatibility in mind. Major versions are used for breaking changes, minor versions for new features, and patch versions for bug fixes.

Dead letter queue strategy and poison message handling

Poison messages are handled through a dead-letter queue (DLQ) with the following strategy:

Retry Limit: Maximum 3 retries with exponential backoff (500ms, 1s, 2s)
DLQ Configuration: Messages that fail after max retries are sent to a DLQ topic
Monitoring: DLQ messages trigger alerts for manual investigation
Manual Processing: DLQ messages can be replayed after root cause analysis

At-least-once vs. exactly-once delivery — decision with justification for this feature

At-least-once delivery is selected because:

Query parsing events are idempotent - replaying the same event doesn't cause harm
The system can tolerate duplicate processing for audit and caching operations
Exactly-once would require complex transactional patterns that add unnecessary complexity for this use case

Retry policy for consumers (max attempts, backoff formula, non-retryable error classification)

public class QueryEventRetryPolicy {
    private static final int MAX_RETRIES = 3;
    private static final long INITIAL_DELAY_MS = 500;
    private static final double BACKOFF_MULTIPLIER = 2.0;
    
    public boolean shouldRetry(int attempt, Exception error) {
        if (attempt >= MAX_RETRIES) {
            return false;
        }
        
        // Don't retry on validation errors or malformed queries
        if (error instanceof QueryParsingException || 
            error instanceof InvalidQueryException) {
            return false;
        }
        
        // Retry on transient errors like network issues, database timeouts
        return error instanceof NetworkException || 
               error instanceof DatabaseTimeoutException ||
               error instanceof TemporaryResourceUnavailableException;
    }
    
    public long getDelay(int attempt) {
        return (long) (INITIAL_DELAY_MS * Math.pow(BACKOFF_MULTIPLIER, attempt));
    }
}

Outbox pattern for reliable event publishing — include or explicitly justify omission

Outbox pattern is omitted in this design because:

Simplicity: The query parser system has a relatively simple event flow
Immediate processing: Events are processed immediately and don't require complex transactional consistency across multiple systems
Message broker reliability: Modern message brokers (e.g., Kafka) provide strong durability guarantees
Performance: The added complexity of outbox pattern would introduce unnecessary overhead for this use case

Instead, we rely on the message broker's built-in reliability features and ensure proper error handling and retry mechanisms.

Sequence diagram for all async flows (Mermaid)

sequenceDiagram
    participant User
    participant QueryParser
    participant EventPublisher
    participant CacheInvalidator
    participant AuditLogger
    
    User->>QueryParser: Execute Query
    QueryParser->>EventPublisher: Publish QUERY_EXECUTED event
    EventPublisher->>CacheInvalidator: Async Cache Invalidator
    EventPublisher->>AuditLogger: Async Audit Logging
    
    Note over CacheInvalidator,QueryParser: Cache invalidation happens asynchronously
    Note over AuditLogger,QueryParser: Audit logging happens asynchronously
    
    CacheInvalidator->>EventPublisher: Send cache invalidation confirmation
    AuditLogger->>EventPublisher: Send audit logging confirmation
    
    QueryParser-->>User: Query result

10. Error Handling Design

Error Classification Hierarchy

The error handling strategy follows a clear hierarchy to ensure proper categorization and response:

Error
├── ValidationException (400)
│   ├── InvalidInputException
│   └── MissingFieldException
├── DomainException (409)
│   ├── QueryParseException
│   └── UnsupportedFeatureException
├── InfrastructureException (500)
│   ├── DatabaseException
│   └── ExternalServiceException
└── AuthorizationException (403)
    └── UnauthorizedAccessException

Error Propagation Strategy

Validation errors are caught and converted to ValidationException at the API boundary
Domain logic errors are handled within service methods and re-thrown as DomainException
Infrastructure errors (database, external services) are wrapped in InfrastructureException and may trigger circuit breaker
Authorization errors are handled at service layer and converted to AuthorizationException

Retry Strategy

For transient infrastructure errors, exponential backoff with jitter is implemented:

public class RetryConfig {
    private static final int MAX_RETRIES = 3;
    private static final long BASE_DELAY_MS = 1000;
    private static final double JITTER_FACTOR = 0.2;
    
    public static long calculateDelay(int attempt) {
        long delay = (long) (BASE_DELAY_MS * Math.pow(2, attempt));
        double jitter = ThreadLocalRandom.current().nextDouble(-JITTER_FACTOR, JITTER_FACTOR);
        return (long) (delay * (1 + jitter));
    }
}

Circuit Breaker Design

For external service dependencies, a circuit breaker pattern is implemented using Resilience4j:

public class CircuitBreakerService {
    private final CircuitBreaker circuitBreaker = CircuitBreaker.ofDefaults("query-parser");
    
    public <T> T executeWithCircuitBreaker(Supplier<T> operation) {
        return circuitBreaker.executeSupplier(operation);
    }
    
    public void onCallSuccess() {
        circuitBreaker.onSuccess();
    }
    
    public void onCallFailure() {
        circuitBreaker.onError();
    }
}

Fallback Behavior

For critical external dependencies, fallbacks are implemented:

public class FallbackHandler {
    public QueryResult handleParsingFailure(QueryRequest request, Exception cause) {
        // Log the error for monitoring
        logger.warn("Query parsing failed, returning default result", cause);
        
        // Return a default or cached result based on query type
        return QueryResult.builder()
            .query(request.getOriginalQuery())
            .parsedQuery("SELECT * FROM default_table")
            .status(QueryStatus.DEFAULT)
            .build();
    }
}

Error Logging Format

Structured logging follows this schema:

{
  "timestamp": "2023-10-27T14:30:00Z",
  "trace_id": "1234567890abcdef",
  "span_id": "abcdef1234567890",
  "user_id": "user-123",
  "error_code": "QUERY_PARSE_ERROR",
  "error_message": "Unexpected token 'FROM' at position 15",
  "error_type": "DomainException",
  "request_context": {
    "query_string": "SELECT * FROM users WHERE id = 1",
    "parser_version": "v2.1"
  },
  "stack_trace": "..."
}

Alerting Thresholds

Key metrics for alerting:

Query parsing errors: > 50 errors per hour → Critical alert
Infrastructure failures: > 100 failures per hour → Warning alert
Retry attempts: > 5 consecutive retries → Warning alert
Circuit breaker tripped: > 3 consecutive failures → Critical alert

Error Response Format

Standardized JSON error response:

{
  "error": {
    "code": "QUERY_PARSE_ERROR",
    "message": "Failed to parse query: Unexpected token 'FROM' at position 15",
    "details": {
      "position": 15,
      "token": "FROM"
    },
    "timestamp": "2023-10-27T14:30:00Z"
  }
}

Error Handling in Service Layer

public class QueryParsingService {
    public QueryResult parseQuery(QueryRequest request) throws QueryParsingException {
        try {
            // Validate input
            validateRequest(request);
            
            // Parse query
            QueryAST ast = queryParser.parse(request.getQuery());
            
            // Transform to target format
            QueryResult result = transformToTargetFormat(ast);
            
            return result;
            
        } catch (ParseException e) {
            // Convert to domain exception with structured logging
            throw new QueryParsingException(
                "Failed to parse query: " + e.getMessage(), 
                e, 
                ErrorCodes.QUERY_PARSE_ERROR,
                Map.of("position", e.getPosition())
            );
        } catch (Exception e) {
            // Wrap infrastructure errors
            throw new InfrastructureException(
                "Infrastructure failure during query parsing", 
                e, 
                ErrorCodes.INFRASTRUCTURE_ERROR
            );
        }
    }
}

Retry and Circuit Breaker Integration

public class RobustQueryParser {
    private final CircuitBreaker circuitBreaker = CircuitBreaker.ofDefaults("external-parser");
    
    public QueryResult parseWithRetry(QueryRequest request) {
        return circuitBreaker.executeSupplier(() -> {
            int maxRetries = 3;
            Exception lastException = null;
            
            for (int attempt = 0; attempt <= maxRetries; attempt++) {
                try {
                    return parseQuery(request);
                } catch (InfrastructureException e) {
                    lastException = e;
                    if (attempt < maxRetries) {
                        Thread.sleep(RetryConfig.calculateDelay(attempt));
                    }
                }
            }
            
            throw new InfrastructureException(
                "Query parsing failed after " + maxRetries + " attempts", 
                lastException
            );
        });
    }
}

11. Security Design

Input Validation and Sanitization Strategy

All query input undergoes strict validation using Apache Commons Validator for regex-based sanitization and OWASP Java HTML Sanitizer for preventing XSS attacks. Custom validation rules are implemented using Bean Validation annotations (@NotNull, @Pattern, @Min, @Max) to enforce structural and semantic constraints.

SQL Injection Prevention

All database queries are constructed using parameterized prepared statements with the JDBC PreparedStatement API. No dynamic SQL string concatenation is permitted. The parser validates that all identifiers (table names, column names) are whitelisted against a predefined schema registry to prevent injection attacks.

Authentication Middleware Design

Authentication is handled via JWT tokens with a custom filter in the Spring Security chain. The middleware validates token signature using RS256 algorithm with keys rotated every 90 days. Token expiration is set to 24 hours with automatic refresh capability.

Authorization Enforcement

Role-based access control (RBAC) is implemented at the service layer using Spring Security annotations (@PreAuthorize). Permissions are mapped to user roles and validated against a centralized permission store. For sensitive operations, additional attribute-based checks (ABAC) are enforced using custom decision managers.

Sensitive Data Encryption

All sensitive data at rest is encrypted using AES-256-GCM with a key rotation policy of 90 days. Data in transit is protected using TLS 1.3 with HSTS enabled. Database connection strings and API keys are encrypted using AWS KMS or equivalent cloud provider's key management service.

PII Handling and Masking Strategy

Personally identifiable information (PII) is masked in logs and audit trails using a configurable redaction policy. Email addresses are truncated (e.g., "john.doe@example.com" → "j***@example.com"), phone numbers are masked to show only last 4 digits, and SSNs are fully redacted.

Secrets Management

API keys, database credentials, and encryption keys are managed through AWS Secrets Manager with automatic rotation policies. The application uses a secrets caching layer with 5-minute TTL to minimize API calls and reduce latency.

Audit Logging Design

All security-relevant events are logged with structured JSON format including:

{
  "timestamp": "2023-10-15T14:30:00Z",
  "user_id": "user-123",
  "action": "QUERY_PARSE",
  "ip_address": "192.168.1.100",
  "query_text": "SELECT * FROM users WHERE id = ?",
  "status": "SUCCESS",
  "duration_ms": 15
}

Audit logs are retained for 90 days and stored in a separate encrypted audit database with read-only access. All logs are timestamped using UTC and include correlation IDs for traceability.

// Example of input validation in query parser
public class QueryValidator {
    private static final Pattern SAFE_IDENTIFIER = Pattern.compile("^[a-zA-Z_][a-zA-Z0-9_]*$");
    private static final Pattern SAFE_QUERY = Pattern.compile("^[a-zA-Z0-9_\\s\\(\\)\\,\\.\\*\\=\\<\\>\\!\\?\\-\\+\\/\\%\\'\\\"]+$");
    
    public void validateQuery(String query) {
        if (query == null || query.trim().isEmpty()) {
            throw new InvalidQueryException("Query cannot be null or empty");
        }
        
        // Validate query structure using regex
        if (!SAFE_QUERY.matcher(query).matches()) {
            throw new SecurityException("Query contains invalid characters");
        }
        
        // Validate that no dangerous SQL patterns are present
        String normalized = query.toLowerCase().trim();
        if (normalized.contains("drop") || 
            normalized.contains("delete") || 
            normalized.contains("update") ||
            normalized.contains("insert")) {
            throw new SecurityException("Write operations not allowed in query parser");
        }
    }
}

12. Testing Strategy

Unit Testing Scope and Coverage Targets

Unit tests will cover 85%+ code coverage for core parsing logic, with particular focus on:

Parser state transitions and error handling
Tokenization edge cases (whitespace, special characters, unicode)
AST node construction and validation
Expression evaluation logic
Query validation rules

Integration Testing Strategy

Integration tests will validate end-to-end query parsing and execution:

Database query generation from parsed AST
SQL injection prevention in parsed queries
Cross-platform compatibility (PostgreSQL, MySQL, Snowflake)
Performance characteristics under load conditions

Test containers will be used for:

Real database connections (PostgreSQL, MySQL)
Mocked external services (auth, logging)
Integration with caching layer

Contract Testing

Contract tests using Pact will verify:

API boundary contracts between query service and downstream consumers
Message format consistency in event-driven flows
Error response format compliance across services

Performance Testing Tool and Scenarios

k6 will be used for performance testing with the following key scenarios:

Concurrent query parsing (1000 concurrent requests)
Large query size stress testing (10MB+ queries)
Memory leak detection over 24-hour test cycles
Latency targets: p50 < 50ms, p95 < 200ms, p99 < 500ms

Test Data Management Strategy

Test data will be managed through:

Factory patterns for generating valid query structures
Fixtures for common SQL patterns and edge cases
Seed data for database schema validation testing
Parameterized test cases for different SQL dialects and syntax variations

Test data will be isolated per environment with:

Database schema snapshots for consistent baseline testing
Version-controlled test fixtures in git repository
Automated data generation tools for edge case scenarios

13. Observability Design

Structured Logging Schema

All logs MUST follow a consistent schema with the following required fields:

{
  "timestamp": "2023-10-15T14:30:45.123Z",
  "level": "INFO|WARN|ERROR",
  "service": "query-parser-service",
  "trace_id": "12345678-1234-1234-1234-123456789012",
  "span_id": "abcdef12-3456-7890-abcd-ef1234567890",
  "user_id": "user-123",
  "request_id": "req-456",
  "operation": "parseQuery",
  "query_text": "SELECT * FROM users WHERE id = ?",
  "parsed_query": "{...}",
  "duration_ms": 15,
  "error_code": "PARSER_ERROR",
  "error_message": "Invalid syntax near token 'FROM'",
  "stack_trace": "...",
  "component": "parser-engine"
}

PII masking rules:

User IDs and session IDs are masked in logs
Query text is truncated to 500 characters for security
Sensitive parameter values are replaced with ***

Distributed Tracing Instrumentation

Using OpenTelemetry for distributed tracing:

Custom Spans to Add:

query.parser.parse - Main parsing operation
query.parser.validate - Validation step
query.parser.optimize - Optimization step
query.parser.execute - Execution preparation

Attributes to Capture:

Span span = Span.current();
span.setAttribute("query.parser.type", "SQL");
span.setAttribute("query.parser.length", queryText.length());
span.setAttribute("query.parser.tokens.count", tokenCount);
span.setAttribute("query.parser.optimized", isOptimized);
span.setAttribute("query.parser.error.code", errorCode);

Metrics Design

RED Metrics (Rate, Errors, Duration):

Metric Name	Type	Description	Labels
`query.parser.requests`	Counter	Total query parsing requests	`type`, `status`
`query.parser.errors`	Counter	Parsing errors	`error_type`, `source`
`query.parser.duration`	Histogram	Parsing operation duration (ms)	`type`, `status`
`query.parser.tokens.processed`	Histogram	Tokens parsed per query	`type`
`query.parser.cache.hits`	Counter	Cache hits	`type`
`query.parser.cache.misses`	Counter	Cache misses	`type`

Additional Gauges:

query.parser.cache.size - Current cache size
query.parser.active.parsing - Concurrent parsing operations

Health Check Endpoint Design

GET /health
GET /ready
GET /live
GET /startup

Response Format:

{
  "status": "healthy|unhealthy",
  "timestamp": "2023-10-15T14:30:45.123Z",
  "service": "query-parser-service",
  "version": "1.2.3",
  "checks": {
    "database": {
      "status": "healthy",
      "latency_ms": 5
    },
    "cache": {
      "status": "healthy",
      "connection_count": 10
    },
    "memory": {
      "status": "healthy",
      "used_mb": 128,
      "max_mb": 512
    }
  }
}