DataGrip - Database IDE That Doesn't Completely Suck

I've been using DataGrip for about 2 years after getting fed up switching between pgAdmin, MySQL Workbench, and whatever MongoDB GUI tool we were pretending worked that month. Here's the real deal on what this thing actually does.

Multiple Databases Without Losing Your Sanity

DataGrip connects to basically any database that has a JDBC driver, which is most of them. Works with PostgreSQL, MySQL, SQL Server, Oracle (if you're unfortunate enough), MongoDB, Redis, and a bunch of other stuff you probably use.

The killer feature is having all your databases in one interface instead of Alt-tabbing between 5 different tools. Connection management actually works - it remembers your setups and reconnects properly when your laptop goes to sleep, unlike some tools that shall remain nameless.

AI That Sometimes Doesn't Hallucinate

The AI assistant is hit-or-miss but occasionally useful. I've had it correctly explain some cryptic PostgreSQL error messages, and it's decent at generating basic SELECT queries from English. Don't trust it for complex joins or anything involving window functions - it'll confidently suggest syntax that doesn't exist.

The 2025.2 update lets you attach specific tables to AI conversations, which helps it not suggest columns that don't exist. Still wouldn't use it for production queries without double-checking.

Code Completion That Actually Works

The autocomplete understands your schema and suggests real column names instead of generic bullshit. It knows about foreign key relationships and will suggest JOINs that make sense. Multi-cursor editing works like you'd expect from a JetBrains IDE.

Query formatting is customizable enough that you won't hate looking at your code, and the refactoring tools can rename columns across multiple queries without breaking everything. Mostly.

Git Integration and Team Sharing

Version control integration is solid - works with Git and doesn't randomly corrupt your query files like some tools. You can share database configurations through your repo, which is useful when onboarding new developers who don't want to spend their first day setting up connections.

SSH tunneling setup is straightforward and works reliably. SSL connections don't randomly break when you're trying to connect to production databases from your coffee shop.

Memory Usage and Performance Reality Check

Takes about 30-45 seconds to start up on my MacBook Pro, which is slower than I'd like but not terrible. Memory usage gets heavy if you have a lot of result tabs open - I've seen it using 2-3GB with a bunch of query results loaded.

Query execution is fast, but the UI can get sluggish if you're working with large schemas (like our 200+ table e-commerce database). Database introspection takes forever on big schemas - plan on 5+ minutes for initial connection to large databases.

Connection pooling works well, but VPN disconnects will kill your active transactions and there's no warning. Learned that one the hard way when I lost 30 minutes of data migration work.

So how does DataGrip actually stack up against the competition? Here's the honest comparison:

Beyond the marketing promises and feature lists, here's what DataGrip is actually like to use in production environments every day.

Database Support: What Actually Works

DataGrip talks to any database with a JDBC driver, which covers most of what you'll use. I've connected it to PostgreSQL, MySQL, SQL Server, Oracle (unfortunately), MongoDB, Redis, Snowflake, and ClickHouse without major issues.

The 2025.2 release improved PostgreSQL and SQL Server introspection, which means it loads schema information faster instead of taking 5 minutes on large databases like it used to.

NoSQL support is mixed: MongoDB integration works okay for basic queries, but don't expect the same level of IntelliSense you get with SQL databases. Redis support is basically just a key-value browser - fine for debugging but not great for complex operations.

Data Editing: The Good and the Annoying

You can edit query results directly in the grid, which is convenient until it isn't. The 2025.2 update made JOIN results editable, but it's still hit-or-miss on complex queries.

What works well:

• Foreign key navigation (click to jump between related tables)
• Grid heatmaps for spotting data patterns and outliers
• CSV/JSON export that doesn't break on large datasets
• Bulk data import that handles most common formats

What's frustrating:

• Large result sets (100k+ rows) make the UI sluggish
• Complex foreign key relationships confuse the edit detection
• No way to edit JSON fields in PostgreSQL without raw SQL

AI Assistant: Useful But Don't Trust It Blindly

The AI integration is better than I expected but not revolutionary. It's helpful for explaining cryptic error messages - PostgreSQL constraint violations make more sense with context.

Where AI actually helps:

• Explaining error messages with schema context
• Generating basic SELECT queries from English descriptions
• Suggesting column names during autocomplete

Where it fails:

• Complex JOINs often generate invalid syntax
• Window functions suggestions are frequently wrong
• Performance optimization advice is generic and sometimes counterproductive

I had the AI suggest using a correlated subquery that would have been a performance disaster on our 50M row table. Always double-check its optimization suggestions.

Schema Management and Migration Scripts

Schema comparison works well between environments. It'll generate DDL migration scripts by comparing two databases, which saves time over manual diffing.

Migration script generation is solid for:

• Adding/dropping columns and indexes
• Simple constraint changes
• Table renaming operations

Gets confused by:

• Complex foreign key dependency chains
• Custom PostgreSQL types and extensions
• Stored procedures with multiple versions

I've had it generate migration scripts that would fail on production due to dependency ordering. Always review the generated scripts before running them.

Performance and Resource Usage

System requirements reality check:

• Minimum 8GB RAM (the official 2GB minimum is bullshit)
• SSD storage highly recommended (database introspection on spinning disks is painful)
• Decent CPU helps with large schema loading and query execution

On my 2021 MacBook Pro (M1, 16GB RAM):

• Startup time: 35-40 seconds
• Memory usage: 1.5-2GB with normal usage, 3-4GB with lots of open result tabs
• Schema loading: 2-3 minutes for our main database (200+ tables)

Connection handling is generally reliable. SSH tunneling through bastion hosts works fine, and it doesn't drop connections when you switch WiFi networks (looking at you, pgAdmin).

Current version notes: As of DataGrip 2025.2, the tool now supports:

• Editable JOIN query results
• Improved PostgreSQL and SQL Server introspection performance
• Better AI context for database objects
• Enhanced floating toolbar for code editing

Integration with Development Workflow

Git integration works as expected if you're already using JetBrains IDEs. You can version control your queries and share database configurations through your repo.

Plugin ecosystem exists but isn't extensive. Most of the useful plugins are for specific database features rather than general productivity improvements.

Works well in Docker environments - connects to containerized databases without additional configuration once networking is set up properly.

After using DataGrip in production for 2+ years, I get the same questions from colleagues constantly. Here are the real answers to what people actually want to know: