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Querying Databases

1. Overview

Queries abstract the underlying data structures in a database by providing a simplified, high-level interface to manipulate and retrieve data, masking the complexity of the physical data storage and retrieval processes.

2. Execution of a Query

The execution of a query involves multiple steps:

  1. Parsing: The query is checked for syntax errors. For example, SQL queries are parsed to ensure they follow proper syntax rules.
  2. Optimization: The query is optimized to determine the most efficient way to access the data. Tools like query planners and optimizers in databases like PostgreSQL play a crucial role here.
  3. Execution: The optimized query is executed using the chosen execution plan. The database engine performs operations such as searching, filtering, and joining tables.
  4. Result Retrieval: The data is fetched from the database and returned to the user or application that made the query.

3. Elaborated Types

3.0.1. 1. Relational Databases

  • SELECT: Used to retrieve data from the tables (e.g., SELECT * FROM users WHERE age > 25;).
  • INSERT: Used to add new rows to a table (e.g., INSERT INTO users (name, age) VALUES ('John', 30);).
  • UPDATE: Used to modify existing data within a table (e.g., UPDATE users SET age = 31 WHERE name = 'John';).
  • DELETE: Removes existing rows from a table (e.g., DELETE FROM users WHERE age < 18;).
  • DDL (Data Definition Language): Includes queries to define or alter schema objects like tables and indexes (e.g., CREATE TABLE users (id INT, name VARCHAR(100));).
  • DCL (Data Control Language): Manages access to the database, such as granting or revoking permissions (e.g., GRANT SELECT ON users TO read_only_user;).

3.0.2. 2. Graph Databases

  • Pattern Matching: Used to find specific patterns within the graph (e.g., in Cypher for Neo4j: MATCH (p:Person)-[:KNOWS]->(f:Friend) RETURN p, f;).
  • Path Queries: Queries that find paths between nodes (e.g., MATCH path = (start)-[*]->(end) RETURN path;).
  • Graph Traversal: Navigates through the graph based on node relationships (e.g., in Gremlin for Apache TinkerPop: g.V().has('name', 'John').out('KNOWS').values('name');).

3.0.3. 3. Document Stores

  • Find: Retrieves documents from a collection (e.g., in MongoDB: db.users.find({ age: { $gt: 25 } });).
  • Insert: Adds new documents to a collection (e.g., db.users.insert({ name: 'John', age: 30 });).
  • Update: Modifies existing documents (e.g., db.users.update({ name: 'John' }, { $set: { age: 31 } });).
  • Delete: Removes documents from a collection (e.g., db.users.remove({ age: { $lt: 18 } });).

3.0.4. 4. Key-Value Stores

  • Get: Retrieves the value for a given key (e.g., in Redis: GET user:1000:name).
  • Set: Adds or updates a value for a given key (e.g., SET user:1000:name "John").
  • Delete: Removes the value associated with a key (e.g., DEL user:1000:name).

3.0.5. 5. Columnar Databases

  • SELECT: Efficient retrieval of specific columns (e.g., in Cassandra: SELECT name, age FROM users WHERE age > 25;).
  • INSERT: Adds rows to a column family (e.g., INSERT INTO users (id, name, age) VALUES (1001, 'John', 30);).
  • UPDATE: Modifies existing columns within a row (e.g., UPDATE users SET age = 31 WHERE id = 1001;).
  • DELETE: Removes rows from a column family (e.g., DELETE FROM users WHERE age < 18;).

3.0.6. 6. In-Memory Databases

  • Get/Set: Similar to key-value stores, these rapidly access data stored in RAM (e.g., in Redis: GET user:1000:name and SET user:1000:name "John").
  • Atomic Operations: Execute operations like incrementing a counter (e.g., INCR page_view_count).
  • Pub/Sub: Implement publish/subscribe messaging patterns (e.g., SUBSCRIBE channel1).

3.0.7. 7. Time-Series Databases

  • Select: Retrieves time-series data points (e.g., in InfluxDB: SELECT mean(temperature) FROM weather WHERE time > now() - 1h GROUP BY time(10m);).
  • Insert: Adds new data points (e.g., INSERT INTO weather (time, temperature) VALUES (now(), 22.5);).
  • Aggregation: Perform time-based aggregations like averages, sums (e.g., SELECT SUM(usage) FROM electricity WHERE time > now() - 1d GROUP BY time(1h);).

3.0.8. 8. NewSQL Databases

  • SELECT: High-performance SQL queries (e.g., in CockroachDB: SELECT * FROM users WHERE age > 25;).
  • INSERT: Adding new rows with ACID compliance (e.g., INSERT INTO users (name, age) VALUES ('John', 30);).
  • UPDATE: Modifying data with distributed transactions (e.g., UPDATE users SET age = 31 WHERE name = 'John';).
  • DELETE: Removing rows consistently across nodes (e.g., DELETE FROM users WHERE age < 18;).

4. Building a Query Language's Inference Engine

Building a query language's inference engine is a complex task involving several key components:

  1. Lexical Analysis (Tokenization):
    • Converts query strings into tokens. Tools like ANTLR can assist with this process.
    • Example: Breaking down the SQL statement SELECT * FROM users; into tokens like SELECT, *, FROM, users, ;.
  2. Parsing:
    • Parses tokens into a syntax tree using a grammar that defines valid query structures.
    • Example: Using parser generators like Bison to create a parse tree from SQL queries.
  3. Semantic Analysis:
    • Checks for semantic errors like type mismatches and schema validations.
    • Example: Ensuring that the users table exists and that * is a valid column selection.
  4. Optimization:
    • Optimizes the query plan for efficient execution.
    • Techniques include query re-writing, join reordering, and indexing strategies.
    • Example: PostgreSQL’s query planner optimizes SELECT * FROM users WHERE age > 25; by using an index on the age column.
  5. Execution Plan Generation:
    • Translates the optimized query into an execution plan, detailing how to retrieve data.
    • Example: Deciding whether to perform a full table scan or utilize an index.
  6. Query Execution:
    • Executes the plan using the database engine, managing I/O and computation.
    • Example: Reading data blocks from disk into memory, applying filters, and returning results.
  7. Result Formatting:
    • Converts raw execution results into the desired output format.
    • Example: Formatting query results as JSON or CSV.
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