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The State of RDBMS in 2025: Recent Trends and Developments

October 15, 2025
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Rapydo

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Optimizations in Traditional RDBMS (PostgreSQL & MySQL)

PostgreSQL performance advances. Recent PostgreSQL releases have focused heavily on performance optimizations. PostgreSQL 16 introduced major boosts to query parallelism, bulk data loading, and logical replication throughput. Bulk inserts via COPY are significantly faster. The engine also adopted SIMD-accelerated operations on both x86 and ARM, improving text processing and snapshot checks. Other enhancements included more efficient parallel application for logical replication and support for logical decoding on standby servers. PostgreSQL 16 also added more SQL/JSON syntax and a new pg_stat_io view for granular I/O performance monitoring.

PostgreSQL 17 continued this trajectory with improvements for high concurrency and vacuum performance. A new internal vacuum memory structure (“TidStore”) reduces vacuum’s memory usage substantially, speeding up vacuum operations and reducing contention. PG17 also improved I/O efficiency for sequential scans and smarter query planning (better handling of IS [NOT] NULL and leveraging NOT NULL constraints). It introduced the SQL standard JSON_TABLE function for transforming JSON into relational form, aligning PostgreSQL with similar JSON handling features of MySQL. While not defined by one headline feature, the aggregate of many optimizations yields substantial gains in throughput and latency for real workloads.

MySQL release model and performance. Oracle introduced a new versioning model with quarterly Innovation Releases (8.1, 8.2, 8.3, etc.) and periodic Long-Term Support (LTS) releases (8.4 as the first LTS). MySQL 8.4 consolidated numerous performance improvements. Defaults were updated for better out-of-the-box performance: disabling InnoDB adaptive hash index and change buffering by default, switching to direct I/O, raising innodb_io_capacity, and enlarging the redo log buffer. These reflect modern best practices and reduce latching overheads. Subsequent 8.4.x releases addressed regressions and optimized internal data structures (e.g., replacing a bottlenecked map with a lock-free hash for binlog dependency tracking), improving throughput on write-heavy workloads. Improvements to complex JOINs and index range scans recovered performance lost in earlier 8.0 releases. In short, the ecosystem has re-centered on efficiency and predictable performance.

Other traditional RDBMS. Oracle Database 23c (“AI Database”) emphasizes converged data management—row, columnar, JSON, spatial, and vector—in one engine. Microsoft SQL Server continues enhancing columnstore and in-memory OLTP for hybrid workloads and is previewing built-in support for vector similarity search in its upcoming release. Across the board, mature RDBMSs exploit modern hardware, improve multi-core parallelism, and remove legacy bottlenecks to keep pace with growing workloads.

Polyglot Persistence: Combining SQL and NoSQL in Production

Modern architectures increasingly adopt polyglot persistence—multiple database technologies used side by side, each for what it does best. A typical pattern: a relational DB (PostgreSQL/MySQL) for orders and inventory, a document or key-value store for user sessions and catalog caches, and a search index for full-text search. Each component addresses a specific need: relational for ACID transactions and complex joins, NoSQL for flexible schemas and horizontal scaling, search for text relevance.

In microservices, “database per service” is common. One service might prefer a graph database, another a time-series store, alongside a relational backbone for core business data. This hybrid approach optimizes performance and resilience and can limit vendor lock-in. The trade-off is operational complexity: data integrity across systems, duplicate data, and the need for broader expertise.

Multi-model databases vs. polyglot. As an alternative to integrating multiple products, multi-model databases support several data models under one engine (e.g., document, graph, key-value). This simplifies operations but may not match the specialized performance of best-of-breed systems. Traditional RDBMSs also blurred lines by adding robust JSON storage and indexing. Many teams now store semi-structured JSON in PostgreSQL (JSONB) and get strong performance, reducing the need for a separate document store in some cases.

Real-world adoption. Large platforms mix relational stores with DynamoDB, Cassandra, Redis, or Elasticsearch. Cloud providers now offer integrated pipelines—e.g., “zero-ETL” from operational databases to warehouses—reducing friction and latency between OLTP and analytics. Event streaming (Kafka) is widely used to sync data across polyglot stores in near-real-time.

Emerging Tools & Libraries for RDBMS Performance, Scalability, and Analytics

  • Vitess (MySQL sharding middleware). CNCF-graduated, powers large MySQL fleets and services like PlanetScale. Presents a single logical MySQL endpoint while handling sharding, routing, failover, and resharding. Active development continues to improve SQL compatibility and observability.

  • Citus (distributed PostgreSQL). Shards tables across nodes, parallelizes queries, and underpins Azure Database for PostgreSQL Hyperscale. Recent releases improve compression, memory management, and alignment with core Postgres features.

  • Connection poolers and proxies. PgBouncer (Postgres) and ProxySQL (MySQL) remain essential for throughput and resource efficiency. Cloud providers increasingly bake in managed pooling.

  • Observability extensions. For Postgres, pg_stat_statements is standard; newer tools like pg_stat_monitor offer deeper query metrics (quantiles, plans). Native views like pg_stat_io add granular I/O visibility. MySQL’s performance schema continues to reduce overhead and improve instrumentation.

  • Analytics on RDBMS data. Spark and Trino commonly query replicas for analytics, but HTAP systems (e.g., SingleStore, MariaDB Xpand, AlloyDB) bridge OLTP/OLAP with mixed engines and vectorized execution. These reduce data movement and latency for analytics on operational data.

  • In-memory and caching layers. Incremental view maintenance (e.g., pg_ivm) and query-result caching proxies (e.g., ReadySet) reduce load by memoizing expensive queries and updating from change streams.

  • Lightweight analytical databases. DuckDB (and MotherDuck) provide fast, in-process OLAP for snapshots of operational data, avoiding warehouse overhead for many analytical tasks.

Integration of RDBMS with AI/ML Workflows (Vector Search & Hybrid Search)

RDBMSs are adding vector data and similarity search for AI applications like semantic search and RAG.

  • PostgreSQL and pgvector. The pgvector extension adds a VECTOR type, similarity operators, and ANN indexing (e.g., HNSW). Managed Postgres services across AWS, Azure, and GCP support pgvector, enabling AI features without introducing a new database. Timescale’s vector extensions (e.g., DiskANN) further accelerate vector workloads and integrate time-series filtering.

  • MySQL HeatWave Vector Store. Oracle’s managed MySQL HeatWave adds an in-database vector store, LLM inference, OCR, and AutoML. Users can load documents, generate embeddings, and run semantic queries inside MySQL HeatWave alongside OLTP and analytics.

  • Oracle Database vector search. Oracle 23c introduces native vector data types and vector indexes for approximate nearest neighbor, enabling hybrid queries that combine vector similarity with standard SQL predicates.

  • Microsoft SQL Server. SQL Server’s next release previews native vector types and VECTOR_SEARCH() in T-SQL, with Azure SQL support in preview.

Hybrid search. Databases increasingly support hybrid workflows—keyword filtering plus vector similarity—to improve search relevance. With vector support integrated, teams can keep data in one system and compose semantic and structured predicates in a single SQL query.

In-database ML. PostgreSQL’s PostgresML and MySQL HeatWave’s AutoML bring training and inference closer to the data. While most ML still occurs outside the DB, in-database scoring and simple pipelines are gaining adoption where data movement must be minimized.

Cloud-Native RDBMS Deployments and Managed Services Trends

  • Serverless and autoscaling. Aurora Serverless v2, Azure SQL serverless, and Postgres-as-a-service startups like Neon (separating storage/compute for sub-second cold starts and branching) enable true on-demand databases and developer-friendly workflows (ephemeral DBs, easy cloning).

  • Global and distributed deployments. Aurora Global Database, Spanner, Azure Cosmos DB for PostgreSQL (Citus), and CockroachDB offer multi-region capabilities with strong or low-lag consistency. Kubernetes operators (Crunchy, Zalando, Percona) mature on-prem/hybrid DB automation.

  • Managed open-source databases. Cloud vendors keep pace with new Postgres/MySQL versions while introducing value-add engines (e.g., AlloyDB). Some hosted services adjusted pricing or strategy, underscoring the importance of cloud-efficient architectures and cost control.

  • “Zero-ETL” and integrated analytics. Aurora’s zero-ETL to Redshift (GA), Azure Synapse Link for SQL, and BigQuery integrations minimize data movement and deliver near-real-time analytics on operational data.

Emerging Themes: HTAP, Serverless, LLM-Aware Databases, and Distributed SQL

  • HTAP. Systems like TiDB and SingleStore unify OLTP and OLAP in one engine. Vendors pursue instant analytics on fresh transactional data, either within one system (HTAP DBs) or via tightly integrated zero-ETL links.

  • Serverless databases and cost efficiency. Pay-per-use and auto-pause are spreading. Edge-friendly patterns (SQLite variants with global replication) show demand for ultra-lightweight serverless data.

  • LLM-aware and AI-native databases. Beyond vector storage, databases increasingly provide natural-language interfaces, automatic tuning, and hooks to external AI services, inching toward self-optimizing “AI-native” behavior.

  • Distributed SQL/NewSQL momentum. CockroachDB, YugabyteDB, TiDB, and Spanner continue to add enterprise features and vector capabilities. Industry interest grows in strongly consistent, horizontally scalable SQL. Rumored/announced paths to distributed SQL in mainstream managed services could accelerate adoption.

  • Community and licensing turbulence. License changes (e.g., Redis, Elasticsearch) sparked forks and debates, highlighting the strategic importance of open governance. PostgreSQL’s stable, community-driven model remains a key adoption factor.

Conclusion

The relational database landscape is evolving faster than ever, driven by innovations that reinforce its enduring importance in modern IT. Relational engines like MySQL and PostgreSQL – once seen as traditional workhorses – have continually reinvented themselves with new features and optimizations to meet emerging demands. The trends discussed above highlight a resilient yet rapidly changing ecosystem. For CTOs, engineers, and database professionals, the key takeaways include:

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  • ‍Continuous Core Improvements: Open-source RDBMS such as PostgreSQL and MySQL are steadily pushing the envelope on performance and flexibility. PostgreSQL, now even surpassing MySQL in developer adoption, exemplifies this with features like enhanced JSON/B support, parallel query execution, and built-in vector search capabilities for AI workloads. These advancements ensure that mature relational systems remain a cornerstone of enterprise data strategy.
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  • Polyglot Persistence to Multi-Model: SQL and NoSQL technologies frequently coexist in today’s architectures, each excelling in different use cases. To reduce this complexity, multi-model databases and hybrid platforms are emerging that unify relational, document, key-value, and even graph paradigms under one engine. This approach preserves specialized capabilities while eliminating the need for separate polyglot datastores, simplifying development and operations.
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  • New Tools for Scale and Performance: A new generation of tools and extensions is empowering organizations to achieve massive scale and speed. From distributed SQL/NewSQL systems to clustering frameworks, the focus is on horizontal scalability without sacrificing ACID guarantees. Even MySQL and PostgreSQL are embracing this direction – for example, through MySQL Group Replication and PostgreSQL extensions like Citus for sharding – foreshadowing a more distributed, cloud-native future for open-source databases. In practice, this means architectures that can grow on-demand and handle global workloads while maintaining consistency.
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  • Real-Time Analytics and HTAP: The line between operational transactions (OLTP) and analytics (OLAP) is blurring. Modern businesses expect fresh transactional data to be available instantly for analysis and AI models, without tedious ETL delays. This is driving interest in hybrid transactional/analytical processing (HTAP) and in-memory analytics features within RDBMS. New offerings like Oracle MySQL HeatWave integrate an analytics engine directly into the MySQL platform, and PostgreSQL gains similar capabilities via extensions (e.g. TimescaleDB) and foreign data wrappers. The result is that relational databases are increasingly able to handle mixed workloads, delivering fast insights on live data.
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  • AI/ML Integration (Vector Search and Automation): Artificial intelligence is being woven into database technology both in how databases operate and in what they offer to users. On the operations side, major vendors and open-source projects are embedding AI for self-tuning, indexing, and query optimization – reducing the manual workload on DBAs by enabling databases to auto-optimize based on workload patterns. On the feature side, databases are beginning to support AI-driven applications natively: for instance, storing vector embeddings for similarity search is now possible in PostgreSQL via the pgvector extension, and MySQL’s HeatWave has an in-database vector store for machine learning use cases. We even see early signs of “LLM-aware” databases – features like natural language querying (e.g. MySQL HeatWave’s GenAI) hint at a future where interacting with a database could feel like conversing with a smart assistant.
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  • Cloud-Native & Serverless Databases: The shift to cloud-managed databases has become mainstream. Most new database deployments are in the cloud, where providers offer fully managed, serverless or autoscaling RDBMS services that offload maintenance and seamlessly handle growth. Cloud-native relational databases (from AWS Aurora to Google Spanner and Azure’s SQL offerings) not only provide high availability and on-demand scale, but often introduce cutting-edge features in their cloud editions first. This trend is redefining expectations: teams can focus on data strategy and usage rather than infrastructure, and can adopt new capabilities faster as the cloud sets the pace for innovation.
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Future Outlook – Toward Unified and Intelligent Data Platforms: Collectively, these trends are shaping a database infrastructure that is more unified, intelligent, and agile than ever. We can expect relational databases to increasingly blend into one cohesive layer that handles diverse workloads and data types with minimal friction. Core systems will continue to embrace distributed and multi-model architectures, allowing a single platform to transact and analyze data in real time. At the same time, advances in AI will make databases more autonomous and intuitive – from automatically tuning themselves to perhaps even answering complex business questions in plain language. In essence, tomorrow’s databases will “just work,” scaling and optimizing in the background while human teams focus on higher-level innovation. For technical decision-makers, this outlook means that choosing database solutions is no longer just about picking SQL vs NoSQL or on-prem vs cloud – it’s about adopting platforms that are flexible enough to integrate AI, analytics, and distribution, and smart enough to manage themselves to a large extent. Organizations that stay ahead of these changes will be positioned to deliver faster insights, maintain reliability at scale, and adapt quickly to new application demands in an AI-driven era.

Rapydo’s Role in the Evolving Ecosystem: In this rapidly changing landscape, Rapydo’s database technology is strategically positioned to help organizations capitalize on these developments. As an AI-powered database management platform, Rapydo embodies many of the modern principles discussed above – it is cloud-native, intelligence-driven, and built for hybrid flexibility. Innovation is at Rapydo’s core: it provides an always-on “virtual DBA” that uses advanced algorithms to continuously monitor and optimize your AWS RDS and Aurora databases. This translates into performance tuning (like automatic query rewriting, indexing suggestions, and caching) and anomaly detection handled in seconds rather than hours, ensuring your databases can handle data-intensive and AI-driven applications with ease. Rapydo also brings flexibility and visibility across heterogeneous environments. In an era of polyglot and distributed data architectures, Rapydo offers a unified interface to observe and manage multiple database instances and types in one place, breaking down silos. Specialized observability platforms like Rapydo play an essential role by providing the cross-platform visibility and governance needed to operate complex database environments effectively – for example, Rapydo’s Scout AI and Cortex Proxy components can automatically detect query bottlenecks or anomalies and then close the loop by applying real-time optimizations, all without manual intervention. By automating these routine but critical tasks, Rapydo allows engineers and DBAs to concentrate on strategic initiatives (schema design, capacity planning, security) instead of firefighting performance issues. In short, Rapydo’s approach aligns tightly with the future of RDBMS: embracing intelligent automation, supporting hybrid architectures, and ensuring peak performance and cost-efficiency at scale.

Call to Action: As data infrastructure enters this new chapter, having the right tools and partners is key to staying ahead. We invite you to explore Rapydo’s capabilities and see how our platform can help your organization navigate these trends – from optimizing legacy SQL systems to leveraging the latest in cloud and AI integration. Visit our website or reach out to the Rapydo team to learn how we can support your journey in building a future-ready, resilient, and high-performance database strategy. Let’s shape the next generation of data-driven innovation together.

 

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Rapydo

In this blog, we explore the power of Rapydo in creating automatic user-defined rules within Amazon RDS MySQL. These rules allow proactive database management by responding to various triggers such as system metrics or query patterns. Key benefits include enhanced performance, strengthened security, and better resource utilization. By automating actions like query throttling, user rate-limiting, and real-time query rewriting, Rapydo transforms database management from reactive to proactive, ensuring optimized operations and SLA compliance.

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MySQL Optimizer: A Comprehensive Guide

August 18, 2024
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Rapydo

The blog provides a deep dive into the MySQL optimizer, crucial for expert DBAs seeking to improve query performance. It explores key concepts such as the query execution pipeline, optimizer components, cost-based optimization, and indexing strategies. Techniques for optimizing joins, subqueries, derived tables, and GROUP BY/ORDER BY operations are covered. Additionally, the guide emphasizes leveraging optimizer hints and mastering the EXPLAIN output for better decision-making. Practical examples illustrate each optimization technique, helping DBAs fine-tune their MySQL systems for maximum efficiency.

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Mastering MySQL Query Optimization: From Basics to AI-Driven Techniques

August 18, 2024
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Rapydo

This blog explores the vital role of query optimization in MySQL, ranging from basic techniques like indexing and query profiling to cutting-edge AI-driven approaches such as machine learning-based index recommendations and adaptive query optimization. It emphasizes the importance of efficient queries for performance, cost reduction, and scalability, offering a comprehensive strategy that integrates traditional and AI-powered methods to enhance database systems.

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Mastering MySQL Scaling: From Single Instance to Global Deployments

August 16, 2024
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Rapydo

Master the challenges of scaling MySQL efficiently from single instances to global deployments. This guide dives deep into scaling strategies, performance optimization, and best practices to build a high-performance database infrastructure. Learn how to manage multi-tenant environments, implement horizontal scaling, and avoid common pitfalls.

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Implementing Automatic Alert Rules in Amazon RDS MySQL

July 30, 2024
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Rapydo

Automatic alert rules in Amazon RDS MySQL are essential for maintaining optimal database performance and preventing costly downtime. Real-time alerts act as an early warning system, enabling rapid responses to potential issues, thereby preventing database crashes. User-defined triggers, based on key metrics and specific conditions, help manage resource utilization effectively. The proactive performance management facilitated by these alerts ensures improved SLA compliance and enhanced scalability. By incorporating real-time alerts, database administrators can maintain stability, prevent performance degradation, and ensure continuous service availability.

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Understanding Atomicity, Consistency, Isolation, and Durability (ACID) in MySQL

July 29, 2024
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Rapydo

ACID properties—Atomicity, Consistency, Isolation, and Durability—are crucial for ensuring reliable data processing in MySQL databases. This blog delves into each property, presenting common issues and practical MySQL solutions, such as using transactions for atomicity, enforcing constraints for consistency, setting appropriate isolation levels, and configuring durability mechanisms. By understanding and applying these principles, database professionals can design robust, reliable systems that maintain data integrity and handle complex transactions effectively.

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 AWS RDS Pricing: A Comprehensive Guide

July 24, 2024
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Rapydo

The blog “AWS RDS Pricing: A Comprehensive Guide” provides a thorough analysis of Amazon RDS pricing structures, emphasizing the importance of understanding these to optimize costs while maintaining high database performance. It covers key components like instance type, database engine, storage options, and deployment configurations, explaining how each impacts overall expenses. The guide also discusses different pricing models such as On-Demand and Reserved Instances, along with strategies for cost optimization like right-sizing instances, using Aurora Serverless for variable workloads, and leveraging automated snapshots. Case studies illustrate practical applications, and future trends highlight ongoing advancements in automation, serverless options, and AI-driven optimization. The conclusion underscores the need for continuous monitoring and adapting strategies to balance cost, performance, and security.

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AWS RDS vs. Self-Managed Databases: A Comprehensive Comparison

July 11, 2024
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Rapydo HQ

This blog provides a detailed comparison between AWS RDS (Relational Database Service) and self-managed databases. It covers various aspects such as cost, performance, scalability, management overhead, flexibility, customization, security, compliance, latency, and network performance. Additionally, it explores AWS Aurora Machine Learning and its benefits. The blog aims to help readers understand the trade-offs and advantages of each approach, enabling them to make informed decisions based on their specific needs and expertise. Whether prioritizing ease of management and automation with AWS RDS or opting for greater control and customization with self-managed databases, the blog offers insights to guide the choice.

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Optimizing Multi-Database Operations with Execute Query

May 15, 2024
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Tal Tamir

Execute Query - Blog Post Executing queries across multiple MySQL databases is essential for: 1. Consolidating Information: Combines data for comprehensive analytics. 2. Cross-Database Operations: Enables operations like joining tables from different databases. 3. Resource Optimization: Enhances performance using optimized databases. 4. Access Control and Security: Manages data across databases for better security. 5. Simplifying Data Management: Eases data management without complex migration. The Execute Query engine lets Dev and Ops teams run SQL commands or scripts across multiple servers simultaneously, with features like: - Selecting relevant databases - Using predefined or custom query templates - Viewing results in tabs - Detecting schema drifts and poor indexes - Highlighting top time-consuming queries - Canceling long-running queries This tool streamlines cross-database operations, enhancing efficiency and data management.

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Gain real time visiblity into hundreds of MySQL databases, and remediate on the spot

May 15, 2024
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Tal Tamir

MySQL servers are crucial for managing data in various applications but face challenges like real-time monitoring, troubleshooting, and handling uncontrolled processes. Rapydo's Processes & Queries View addresses these issues with features such as: 1. Real-Time Query and Process Monitoring: Provides visibility into ongoing queries, helping prevent bottlenecks and ensure optimal performance. 2. Detailed Visualizations: Offers table and pie chart views for in-depth analysis and easy presentation of data. 3. Process & Queries Management: Allows administrators to terminate problematic queries instantly, enhancing system stability. 4. Snapshot Feature for Retrospective Analysis: Enables post-mortem analysis by capturing and reviewing database activity snapshots. These tools provide comprehensive insights and control, optimizing MySQL server performance through both real-time and historical analysis.

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MySQL 5.7 vs. MySQL 8.0: New Features, Migration Planning, and Pre-Migration Checks

April 18, 2024
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Alon Kafry

This article compares MySQL 5.7 and MySQL 8.0, emphasizing the significant improvements in MySQL 8.0, particularly in database optimization, SQL language extensions, and administrative features. Key reasons to upgrade include enhanced query capabilities, support from cloud providers, and keeping up with current technology. MySQL 8.0 introduces window functions and common table expressions (CTEs), which simplify complex SQL operations and improve the readability and maintenance of code. It also features JSON table functions and better index management, including descending and invisible indexes, which enhance performance and flexibility in database management. The article highlights the importance of meticulous migration planning, suggesting starting the planning process at least a year in advance and involving thorough testing phases. It stresses the necessity of understanding changes in the optimizer and compatibility issues, particularly with third-party tools and applications. Security enhancements, performance considerations, and data backup strategies are also discussed as essential components of a successful upgrade. Finally, the article outlines a comprehensive approach for testing production-level traffic in a controlled environment to ensure stability and performance post-migration.

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How to Gain a Bird's-Eye View of Stressing Issues Across 100s of MySQL DB Instances

April 11, 2024
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Rapydo HQ

Rapydo Scout offers a unique solution for monitoring stress points across both managed and unmanaged MySQL database instances in a single interface, overcoming the limitations of native cloud vendor tools designed for individual databases. It features a Master-Dashboard divided into three main categories: Queries View, Servers View, and Rapydo Recommendations, which together provide comprehensive insights into query performance, server metrics, and optimization opportunities. Through the Queries View, users gain visibility into transaction locks, the slowest and most repetitive queries across their database fleet. The Servers View enables correlation of CPU and IO metrics with connection statuses, while Rapydo Recommendations deliver actionable insights for database optimization directly from the MySQL Performance Schema. Connecting to Rapydo Scout is straightforward, taking no more than 10 minutes, and it significantly enhances the ability to identify and address the most pressing issues across a vast database environment.

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Unveiling Rapydo

March 28, 2024
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Matan Nataf

Rapydo Emerges from Stealth: Revolutionizing Database Operations for a Cloud-Native World In today's rapidly evolving tech landscape, the role of in-house Database Administrators (DBAs) has significantly shifted towards managed services like Amazon RDS, introducing a new era of efficiency and scalability. However, this transition hasn't been without its challenges. The friction between development and operations teams has not only slowed down innovation but also incurred high infrastructure costs, signaling a pressing need for a transformative solution. Enter Rapydo, ready to make its mark as we step out of stealth mode.

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SQL table partitioning

October 17, 2023
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Eran Shaked

Using table partitioning, developers can split up large tables into smaller, manageable pieces. A database’s performance and scalability can be improved when users only have access to the data they need, not the whole table.

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Block queries from running on your database

April 5, 2023
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Eran Shaked

As an engineer, you want to make sure that your database is running smoothly, with no unexpected outages or lags in response-time. One of the best ways to do this is to make sure that only the queries you expect to run are being executed.

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Uncover the power of database log analysis

December 28, 2022
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Eran Shaked

Logs.They’re not exactly the most exciting things to deal with, and it’s easy to just ignore them and hope for the best. But here’s the thing: logs are actually super useful and can save you a ton of headaches in the long run.

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