Gleam Lab · Blog Archive
Blog Page 20
Technical exploration and engineering notes, 655 articles in total.
MySQL Transactions: ACID Properties, Isolation Levels and Use Cases
MySQL database transaction is a set of SQL operations executed as a single logical unit of work, following ACID principles: Atomicity, Consistency, Isolation and Durabili...
MyCat schema.xml: Logical Database, Tables, Data Nodes Guide
schema.xml is MyCat's core configuration file managing logical databases, logical tables, data nodes and data hosts.
MySQL Transaction Issues: Dirty Read, Non-Repeatable Read and Phantom Read
Common issues in database transactions include dirty read, non-repeatable read, and phantom read. These issues are closely related to database isolation levels.
Spring Boot Transaction Propagation: From REQUIRED to NESTED
Transaction propagation is an important mechanism in Spring for handling transaction boundaries, defining how methods interact with transaction context in the call chain.
Spring Boot Declarative vs Programmatic Transactions
Introduces declarative transaction and programmatic transaction concepts, usage methods and comparative analysis.
Spring Transaction Pitfalls Part 2: Common Issues and Solutions
Spring transactions are implemented through AOP dynamic proxies. JDK dynamic proxy requires the target method to be public.
Spring Boot @Transactional: Propagation, Isolation and Rollback Rules
Detailed introduction to @Transactional annotation parameters in Spring Boot, including transaction propagation, isolation levels, and exception rollback strategies.
MyBatis Plugin Mechanism - Principles, Custom Plugins, Source Code Analysis
Generally, open-source frameworks provide plugins or extension points for developers to extend on their own.
Spring Transaction Pitfalls Part 1: Common Issues and Solutions
Spring transaction pitfalls commonly occur in scenarios like classes not managed by Spring, transaction manager not started, improper method modifiers (final, static...
Spring Transaction Pitfalls Part 3: Common Issues and Solutions
Spring transaction pitfalls include rollbackFor configuration errors, transaction annotation overrides, nested transaction issues and more.
MongoDB Basic Operations: Database, Collection & CRUD Guide
MongoDB common commands include show dbs to list databases, use to switch databases, db.createCollection() to create collections, show collections to list collections, db.
NoSQL Databases: MongoDB, Redis, HBase, Neo4j Comparison
NoSQL is a general term for non-relational databases, including document-based MongoDB, key-value Redis, column-store HBase, and graph database Neo4j, each with its own c...
MongoDB BSON Format: Core Storage Format vs JSON
BSON is MongoDB's core storage format, a binary-encoded JSON-like format. Compared to JSON, BSON supports more data types like Date, Binary, ObjectId, with better perform...
Spring Transaction Pitfalls Part 4: Multi-Threading and Exception Handling
Spring transaction pitfalls include multi-threaded calls, exception catching without rollback, manual exception throwing errors.
MongoDB Indexing: Types, Principles & Best Practices
MongoDB indexes are core mechanisms for improving query performance, including single field indexes, compound indexes, multi-key indexes, geospatial indexes...
MongoDB Index Management & explain Execution Plan Analysis
MongoDB index management includes createIndex, getIndexes, dropIndex operations.
MongoDB Aggregation: $match, $group, $project and $sort Pipelines
MongoDB aggregation framework processes documents through multi-stage pipelines, implementing complex operations similar to SQL GROUP BY, JOIN, SUM, AVG.
MongoDB Pagination: skip() + limit() + sort() & Performance
MongoDB implements pagination through combination of skip(), limit() and sort() methods.
MongoDB Data Insert, Batch Write and Logical Queries with AND/OR
MongoDB data operations include single insertOne() and batch insertMany(). Query operators include $eq, $ne, $gt, $lt, etc. Logical operators include $and, $or, $not.
MongoDB vs MySQL: B-Tree vs B+Tree Index Mechanisms
MongoDB uses B-tree indexes where nodes store both data and keys. MySQL uses B+tree indexes with all data concentrated in leaf nodes.