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What pg_database_owner does in Postgres, why it was added, and how to use it without hitting the "cannot have explicit members" error.

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Amazon Relational Database Service (RDS) offers a fully managed database solution that simplifies setup, operation, and scaling of relational databases in the cloud. While it's easy and relative cheap to get started, the bill can accumulate pretty fast as this heated Reddit [Postgres RDS is too expensive](https://www.reddit.com/r/devops/comments/1gyjwoj/postgres_rds_is_too_expensive/) post suggests. Thus Understanding its pricing structure is essential for optimizing costs and making informed decisions for your database deployments. This article breaks down AWS RDS pricing components with specific examples from the US East (N. Virginia) region to help you better understand and manage your database costs.

> Looking for exact numbers? See the [Amazon RDS pricing table](/dbcost/rds-pricing/) — every instance priced per month, on-demand and reserved, sortable and filterable across regions and engines.

## Introduction to AWS RDS Pricing

AWS RDS follows a **pay-as-you-go** pricing model with no upfront costs or long-term commitments required. The pricing structure is designed to be flexible, allowing you to pay only for the resources you consume. However, this flexibility can make it **challenging to predict costs** without understanding the various pricing components.

The primary factors that influence your AWS RDS costs include:

1. Database instance type and size
2. Storage type and capacity
3. I/O operations
4. Data transfer
5. Backup storage
6. Deployment options (Single-AZ vs Multi-AZ)
7. Reserved Instances vs On-Demand pricing

Let's explore each of these components in detail with specific pricing examples from the US East (N. Virginia) region.

## AWS RDS Pricing Components

### 1. Database Instance Costs

Database costs depend on the instance type and size you select. AWS offers different families optimized for various workloads:

- **T-class instances**: Burstable performance, ideal for moderate workloads with occasional CPU spikes
- **M-class instances**: General-purpose, balanced for compute, memory, and network needs
- **R-class instances**: Memory-optimized, designed for memory-heavy applications
- **X-class instances**: Extreme memory-optimized, offering the highest memory-to-CPU ratio

**Example pricing (US East - N. Virginia):**

| Instance Type | vCPU | RAM (GiB) | On-Demand Price (Hourly) |
| :------------ | :--: | :-------: | :----------------------: |
| db.t3.micro   |  1   |     1     |         ~$0.017          |
| db.m5.large   |  2   |     8     |         ~$0.171          |
| db.r5.large   |  2   |    16     |         ~$0.226          |

T-class instances (T3, T4g) use a CPU credit model.  
If you exceed the baseline, additional CPU credits are billed at **$0.075 per vCPU-Hour**.

### 2. Storage Costs

AWS RDS offers three types of storage:

- **General Purpose SSD (gp2, gp3)**:  
  Cost-effective for most workloads.

- **Provisioned IOPS SSD (io1, io2)**:  
  Designed for I/O-intensive applications needing low-latency and consistent performance.

- **Magnetic Storage** (legacy):  
  Older option, mainly for backward compatibility — **not recommended** for new deployments.

**Example Pricing (US East - N. Virginia):**

| Storage Type                   | Price per GB-Month |                                      IOPS/Throughput Details                                       |
| :----------------------------- | :----------------: | :------------------------------------------------------------------------------------------------: |
| General Purpose SSD (gp3)      |      ~$0.115       | 3,000 baseline IOPS, 125 MB/s; extra IOPS: ~$0.08/IOPS-month; extra throughput: ~$0.065/MB/s-month |
| Provisioned IOPS SSD (io1/io2) |      ~$0.125       |                    Provision 1,000–256,000 IOPS; extra IOPS: ~$0.10/IOPS-month                     |
| Magnetic Storage (legacy)      |       ~$0.10       |                             Legacy option; not recommended for new use                             |

- **gp3** storage includes a generous baseline; extra IOPS and throughput cost extra.
- **Provisioned IOPS** allows fine-tuning performance, but at a higher price.
- **Magnetic Storage** should only be used for legacy systems.

### 3. Backup Storage Costs

AWS RDS provides automated backups and manual snapshots:

- Backup storage up to **100%** of your total database storage (per region) is **free**.
- Additional backup storage beyond the free limit costs **~$0.095 per GB-month** (US East - N. Virginia).
- Exporting snapshots to Amazon S3 costs **~$0.10 per GB**.

### 4. Data Transfer Costs

Data transfer costs depend on the source and destination:

- **RDS ↔ EC2 (same Availability Zone)**: Free
- **RDS Multi-AZ replication (between Availability Zones)**: Free
- **RDS ↔ EC2 (different Availability Zones, same region)**: ~$0.01 per GB
- **Internet data transfer in**: Free
- **Internet data transfer out**: Tiered pricing, starting at **~$0.09 per GB** for the first 10 TB per month

**Notes:**

- Transfers **within the same AZ** are free, minimizing costs.
- **Cross-AZ** transfers outside of Multi-AZ setups can add up if not carefully designed.
- Internet outbound traffic is the most expensive part — watch your usage if serving external clients.

### 5. Zero-ETL Integration Costs

For RDS for MySQL zero-ETL integration with Amazon Redshift:

- **CDC (Change Data Capture) data transfer**: ~$2.00 per GB (US East - N. Virginia)
- **Snapshot export**: ~$0.10 per GB

**Notes:**

- CDC charges are significantly higher than typical data transfer, reflecting the real-time replication cost.
- Snapshot export pricing aligns with regular RDS snapshot export costs.

## Deployment Options and Their Impact on Pricing

### Single-AZ vs Multi-AZ Deployments

- **Single-AZ**: Provides a database instance in a single Availability Zone
- **Multi-AZ**: Automatically provisions and maintains a synchronous standby replica in a different Availability Zone

Multi-AZ deployments cost twice as much as Single-AZ deployments for the instance hours, as you're essentially running two instances. However, there's no additional charge for data transfer between the primary and standby instances.

### Read Replicas

Read replicas allow you to scale read operations and can be deployed within the same region or cross-region:

- In-region read replicas are charged at the standard instance and storage rates
- Cross-region read replicas incur additional data transfer charges for replicating data across regions

## Cost Optimization Strategies

### 1. Right-sizing Your Instances

- Start with smaller instances and scale up based on workload needs.
- Monitor CPU, memory, and I/O utilization using AWS monitoring tools.
- Use **T-class burstable instances** for development, testing, or low-traffic production environments.

### 2. Leveraging Reserved Instances

Reserved Instances (RIs) offer up to **72% savings** over On-Demand pricing in exchange for a 1- or 3-year commitment:

- **All Upfront**: Full payment upfront for the highest discount.
- **Partial Upfront**: Partial upfront payment, balance paid monthly.
- **No Upfront**: Monthly payments with a smaller discount.

**Example (US East - N. Virginia, db.m5.large):**

| Purchase Option         | Effective Price |  Annual/Total Cost  | Savings |
| :---------------------- | :-------------: | :-----------------: | :-----: |
| On-Demand               |  ~$0.171/hour   |    ~$1,498/year     |    —    |
| 1-year RI (No Upfront)  |  ~$0.109/hour   |     ~$955/year      |  ~36%   |
| 3-year RI (All Upfront) |  ~$0.048/hour   | ~$1,259 for 3 years |  ~72%   |

### 3. Storage Optimization

- Choose **General Purpose SSD (gp3)** for the best price-performance balance.
- Reserve **Provisioned IOPS SSD** for workloads with consistently high I/O needs.
- Archive old or infrequently accessed data to cheaper storage tiers.

### 4. Backup Management

- Set a **backup retention period** that balances recovery needs and storage costs.
- Regularly delete unnecessary manual snapshots.
- Use **AWS Backup** for centralized backup management across services.

### 5. Multi-AZ Deployment Considerations

- Reserve **Multi-AZ deployments** for production workloads requiring high availability.
- Use **Single-AZ deployments** for development and testing to save costs.

### 6. Monitoring and Analyzing Costs

- Use **AWS Cost Explorer** to analyze RDS spending patterns.
- Set up **AWS Budgets** to receive alerts when spending exceeds thresholds.
- Leverage **AWS Trusted Advisor** for cost optimization recommendations.

## Real-World Pricing Examples

### Small Workload (Development/Testing)

**Setup:**

- **Instance:** db.t3.small (Single-AZ)
- **Storage:** 20 GB General Purpose SSD (gp3)
- **Backup Retention:** 7 days

**Monthly Cost Estimate:**

| Item           | Calculation             |       Cost        |
| :------------- | :---------------------- | :---------------: |
| Instance       | $0.034/hour × 730 hours |      ~$24.82      |
| Storage        | 20 GB × $0.115/GB       |      ~$2.30       |
| Backup Storage | Within free tier        |       Free        |
| **Total**      |                         | **~$27.12/month** |

### Medium Workload (Small Production)

**Setup:**

- **Instance:** db.m5.large (Multi-AZ)
- **Storage:** 100 GB General Purpose SSD (gp3)
- **Backup Retention:** 14 days

**Monthly Cost Estimate:**

| Item           | Calculation                 |        Cost        |
| :------------- | :-------------------------- | :----------------: |
| Instance       | $0.171/hour × 2 × 730 hours |      ~$249.66      |
| Storage        | 100 GB × $0.115/GB × 2      |      ~$23.00       |
| Backup Storage | Within free tier            |        Free        |
| **Total**      |                             | **~$272.66/month** |

### Large Workload (Enterprise Production)

**Setup:**

- **Instance:** db.r5.2xlarge (Multi-AZ)
- **Storage:** 1,000 GB Provisioned IOPS SSD (io1) with 5,000 IOPS
- **Backup Retention:** 30 days

**Monthly Cost Estimate:**

| Item             | Calculation                              |         Cost         |
| :--------------- | :--------------------------------------- | :------------------: |
| Instance         | $0.904/hour × 2 × 730 hours              |      ~$1,319.84      |
| Storage          | 1,000 GB × $0.125/GB × 2                 |       ~$250.00       |
| Provisioned IOPS | 5,000 IOPS × $0.10/IOPS-month × 2        |      ~$1,000.00      |
| Backup Storage   | 500 GB × $0.095/GB (first 1,000 GB free) |       ~$47.50        |
| **Total**        |                                          | **~$2,617.34/month** |

## Conclusion

Understanding AWS RDS pricing is crucial for optimizing your cloud database costs. By carefully selecting instance types, storage options, and deployment configurations — and by leveraging cost-saving options like Reserved Instances — you can significantly reduce expenses while maintaining the performance and availability your applications require.

AWS regularly updates its pricing — always verify with the [official AWS pricing pages](https://aws.amazon.com/rds/pricing/).

By applying the cost optimization strategies outlined in this guide and regularly reviewing your database usage patterns, you can maximize the value of your AWS RDS investment while keeping costs under control.



MySQL offers various timeout settings that are essential for ensuring system stability, preventing resource exhaustion, and maintaining performance, especially in high-traffic environments or when dealing with complex queries. Let's explore each of them.

## connect_timeout

[connect_timeout](https://dev.mysql.com/doc/refman/8.0/en/server-system-variables.html#sysvar_connect_timeout) defines the number of seconds that the MySQL server waits for a connect packet before responding with a "Bad handshake" error. This timeout prevents abandoned connection attempts from consuming server resources.

```plain
ERROR: Lost connection to MySQL server at 'reading initial communication packet', system error: [errno]
```

By default, MySQL sets `connect_timeout` to 10 seconds, which works well for most environments. For high-latency networks or connections routed through multiple proxies, you might need to increase this value to 15-30 seconds. Conversely, in environments vulnerable to denial-of-service attacks, a lower timeout value can help mitigate connection flooding by releasing resources more quickly.

You can configure this setting in the MySQL configuration file:

```ini
[mysqld]
connect_timeout=20
```

Or dynamically at runtime (requires `SYSTEM_VARIABLES_ADMIN` privilege):

```sql
SET GLOBAL connect_timeout = 20;
```

Note that changing this setting dynamically affects only new connection attempts; existing connections remain unaffected.

## max_execution_time

<HintBlock type="info">

`max_execution_time` only applies to naked SELECT statements. Other statement types like INSERT, UPDATE, and DELETE are not affected by this timeout mechanism. It's also ignored in stored procedures.

</HintBlock>

[max_execution_time](https://dev.mysql.com/doc/refman/8.0/en/server-system-variables.html#sysvar_max_execution_time) sets a limit on the maximum amount of time that a SELECT statement is permitted to execute before being terminated by the server. Introduced in MySQL 5.7.8, this setting helps prevent long-running queries from monopolizing system resources.

```plain
ERROR 3024 (HY000): Query execution was interrupted, maximum statement execution time exceeded
```

By default, `max_execution_time` is set to 0, which effectively disables the timeout mechanism. The value is specified **in milliseconds**, so setting it to 10000 would create a 10-second limit for queries.

You can set it globally in the configuration file:

```ini
[mysqld]
max_execution_time=10000
```

Or at the session level, affecting only queries in the current connection:

```sql
SET SESSION max_execution_time = 5000;
```

Or at the query level using an optimizer hint:

```sql
SELECT /*+ MAX_EXECUTION_TIME(3000) */ * FROM large_table WHERE complex_condition;
```

## innodb_lock_wait_timeout

[innodb_lock_wait_timeout](https://dev.mysql.com/doc/refman/8.0/en/innodb-parameters.html#sysvar_innodb_lock_wait_timeout) controls how long an InnoDB transaction will wait for a row lock before giving up and rolling back the current statement. This timeout prevents transactions from being perpetually blocked when they cannot acquire necessary locks.

```plain
ERROR 1205 (HY000): Lock wait timeout exceeded; try restarting transaction
```

By default, this value is set to 50 seconds. When a transaction exceeds this threshold, only the current statement is rolled back by default, not the entire transaction. This behavior allows applications to catch the timeout error, potentially retry the specific operation that failed, and continue with the remainder of the transaction.

You can modify this default behavior by starting the MySQL server with the `--innodb-rollback-on-timeout` option, which causes the entire transaction to be rolled back when a lock wait timeout occurs.

The timeout can be configured at the global level:

```ini
[mysqld]
innodb_lock_wait_timeout=30
```

Or dynamically at runtime:

```sql
SET GLOBAL innodb_lock_wait_timeout = 30;
```

MySQL also supports session-level configuration:

```sql
SET SESSION innodb_lock_wait_timeout = 10;
```

For highly interactive OLTP systems, shorter timeout values (10-30 seconds) often provide better responsiveness by quickly identifying and resolving lock contention issues. For batch processing systems, longer timeout values may be more appropriate.

The lock timeout mechanism interacts closely with MySQL's deadlock detection system (`innodb_deadlock_detect`). When deadlock detection is enabled (the default), InnoDB automatically identifies circular lock dependencies and immediately resolves them, often before the lock timeout is reached.

## interactive_timeout and wait_timeout

[interactive_timeout](https://dev.mysql.com/doc/refman/8.0/en/server-system-variables.html#sysvar_interactive_timeout) and [wait_timeout](https://dev.mysql.com/doc/refman/8.0/en/server-system-variables.html#sysvar_wait_timeout) control how long idle connections are maintained before being automatically closed by the server. These settings help prevent resource depletion from abandoned or forgotten connections.

```plain
ERROR: MySQL server has gone away
```

By default, both values are set to 28800 seconds (8 hours). The distinction between them is based on connection type:

- `interactive_timeout` applies to connections that use the `CLIENT_INTERACTIVE flag` (typically GUI tools and command-line clients)
- `wait_timeout` applies to non-interactive connections (application servers, scripts, automated processes)

When a new connection session is established, its effective wait timeout value is initialized from either `wait_timeout` or `interactive_timeout`, depending on whether the `CLIENT_INTERACTIVE` flag is used. After that, the session's timeout can be modified independently.

You can configure these settings in the MySQL configuration file:

```ini
[mysqld]
interactive_timeout=3600
wait_timeout=1800
```

Or dynamically at runtime:

```sql
SET GLOBAL interactive_timeout = 3600;
SET GLOBAL wait_timeout = 1800;
```

Session-level configuration is also supported:

```sql
SET SESSION wait_timeout = 7200;
```

For web applications with short, frequent interactions, shorter timeout values (60-300 seconds) often provide better resource utilization.

<HintBlock type="info">

For applications using connection pooling, configure the pool's maximum idle time to be slightly shorter than MySQL's `wait_timeout` to prevent "server has gone away" errors.

</HintBlock>

## transaction_timeout

To prevent long-running connection holding locks for too long, you usually set `wait_timeout` or `interactive_timeout`. However, the transaction will still be open if it consists of short statements and short pauses in between. Ideally, you want to have a `transaction_timeout`. Unfortunately, MySQL doesn't have this, only [MariaDB](https://mariadb.com/kb/en/transaction-timeouts/) and [PostgreSQL](https://www.postgresql.org/docs/devel/runtime-config-client.html#GUC-TRANSACTION-TIMEOUT) do.

## Best Practices

When configuring MySQL timeout settings, consider these practical recommendations:

### Environment-Specific Settings

| Setting                    | High-Traffic Web Applications | Data Processing Applications |
| -------------------------- | ----------------------------- | ---------------------------- |
| `wait_timeout`             | 60-300 seconds                | 3600-7200 seconds            |
| `interactive_timeout`      | 1800 seconds                  | 7200 seconds                 |
| `connect_timeout`          | 5-10 seconds                  | 10-20 seconds                |
| `innodb_lock_wait_timeout` | 10-30 seconds                 | 50-120 seconds               |
| `max_execution_time`       | 5000-10000 milliseconds       | 30000-60000 milliseconds     |

### Connection Pooling

When using connection pooling, configure the pool's maximum idle time to be 10-15% shorter than MySQL's `wait_timeout`. This ensures that the pool proactively refreshes connections before the database server terminates them.

For example, if `wait_timeout` is set to 3600 seconds (1 hour), set the connection pool's idle timeout to approximately 3200 seconds (53 minutes).

### Monitoring

Monitor idle connections using `SHOW PROCESSLIST` and look for connections in the `Sleep` state. A large number of sleeping connections might indicate that your application is not properly closing connections or that your timeout values need adjustment.

## References

- [MySQL Server System Variables](https://dev.mysql.com/doc/refman/8.0/en/server-system-variables.html)
- [InnoDB Parameters](https://dev.mysql.com/doc/refman/8.0/en/innodb-parameters.html)
- [Server-Side SELECT Statement Timeouts](https://dev.mysql.com/blog-archive/server-side-select-statement-timeouts/)



SQL (Structured Query Language) is the standard language for relational database management systems. SQL commands are divided into several categories based on their functionality. The two most fundamental categories are **Data Definition Language (DDL)** and **Data Manipulation Language (DML)**.

## What is Data Definition Language (DDL)?

Data Definition Language (DDL) refers to SQL commands that define, modify, or remove database objects such as tables, indexes, and schemas. DDL statements create the structure that will hold the data but do not manipulate the data itself.

### Common DDL Commands

1. **CREATE**: Establishes new database objects

   ```sql
   CREATE TABLE employees (
     id INT PRIMARY KEY,
     name VARCHAR(100) NOT NULL,
     department VARCHAR(50),
     salary DECIMAL(10,2)
   );
   ```

2. **ALTER**: Modifies existing database objects

   ```sql
   ALTER TABLE employees
   ADD COLUMN hire_date DATE;
   ```

3. **DROP**: Removes database objects

   ```sql
   DROP TABLE employees;
   ```

4. **TRUNCATE**: Removes all records from a table without logging individual row deletions

   ```sql
   TRUNCATE TABLE employees;
   ```

5. **RENAME**: Changes the name of an existing object
   ```sql
   RENAME TABLE employees TO staff;
   ```

### Characteristics of DDL

- DDL statements automatically commit the current transaction in most database systems
- Changes made by DDL are recorded in the data dictionary

## What is Data Manipulation Language (DML)?

Data Manipulation Language (DML) consists of SQL commands that manipulate the data stored within database objects. These statements allow you to insert, retrieve, modify, and delete data.

### Common DML Commands

1. **SELECT**: Retrieves data from one or more tables

   ```sql
   SELECT name, department, salary
   FROM employees
   WHERE salary > 50000;
   ```

2. **INSERT**: Adds new records into a table

   ```sql
   INSERT INTO employees (id, name, department, salary)
   VALUES (101, 'John Doe', 'Engineering', 75000);
   ```

3. **UPDATE**: Modifies existing records

   ```sql
   UPDATE employees
   SET salary = salary * 1.1
   WHERE department = 'Engineering';
   ```

4. **DELETE**: Removes records from a table

   ```sql
   DELETE FROM employees
   WHERE id = 101;
   ```

5. **MERGE**: Performs insert, update, or delete operations based on a condition
   ```sql
   MERGE INTO target_table
   USING source_table
   ON (target_table.id = source_table.id)
   WHEN MATCHED THEN UPDATE SET target_table.value = source_table.value
   WHEN NOT MATCHED THEN INSERT (id, value) VALUES (source_table.id, source_table.value);
   ```

### Characteristics of DML

- DML statements can be rolled back (except in auto-commit mode)
- DML operations are logged, enabling recovery and audit capabilities

## Key Differences Between DDL and DML

| Aspect                  | DDL                                      | DML                                   |
| ----------------------- | ---------------------------------------- | ------------------------------------- |
| **Purpose**             | Defines database structure               | Manipulates data within the structure |
| **Scope**               | Database objects (tables, indexes, etc.) | Data records                          |
| **Common Commands**     | CREATE, ALTER, DROP, TRUNCATE            | SELECT, INSERT, UPDATE, DELETE        |
| **Transaction Control** | Auto-commits (in most DBMSs)             | Can be rolled back                    |
| **Frequency of Use**    | Less frequent                            | More frequent                         |
| **Privilege Level**     | Higher (admin-level)                     | Lower (user-level)                    |
| **Impact**              | Structural changes                       | Data changes                          |

## Other SQL Language Categories: DCL, TCL, and DQL

Beyond DDL and DML, SQL commands are also categorized into:

- **DCL (Data Control Language)**: Commands like `GRANT` and `REVOKE` that manage user permissions and access control.
- **TCL (Transaction Control Language)**: Commands like `COMMIT`, `ROLLBACK`, and `SAVEPOINT` that manage database transactions.
- **DQL (Data Query Language)**: Some classifications separate `SELECT` from DML into its own category since it only retrieves data without modifying it.

## DDL vs DML: When to Use Each

| Scenario | Use DDL | Use DML |
|----------|---------|---------|
| Creating a new table | `CREATE TABLE` | — |
| Adding data to a table | — | `INSERT INTO` |
| Adding a new column | `ALTER TABLE ADD COLUMN` | — |
| Updating existing records | — | `UPDATE` |
| Removing a table entirely | `DROP TABLE` | — |
| Removing specific rows | — | `DELETE FROM` |
| Changing column data type | `ALTER TABLE ALTER COLUMN` | — |
| Querying data | — | `SELECT` |

## Best Practices for DDL and DML Operations

### DDL Best Practices

1. **Always back up before schema changes** — DDL operations like `DROP` and `TRUNCATE` are irreversible in most databases.
2. **Use version control for schema changes** — Track DDL statements in migration scripts to maintain a history of database structure changes.
3. **Test DDL in staging first** — Schema changes can impact application behavior and should be validated before production deployment.
4. **Use online DDL where possible** — For large tables, operations like `CREATE INDEX CONCURRENTLY` (PostgreSQL) avoid locking the table.

### DML Best Practices

1. **Use transactions for multi-statement operations** — Wrap related DML statements in transactions to maintain data consistency.
2. **Always include WHERE clauses in UPDATE and DELETE** — Omitting WHERE affects all rows in the table.
3. **Use parameterized queries** — Prevent SQL injection by avoiding string concatenation in DML statements.
4. **Monitor and optimize slow queries** — Use `EXPLAIN` to analyze query execution plans.

## Managing DDL and DML with Bytebase

Effective database change management requires proper control over both DDL and DML operations. Tools like Bytebase provide features such as [SQL review policies](https://docs.bytebase.com/sql-review), [version control for schema changes](https://docs.bytebase.com/gitops), [approval workflows](https://docs.bytebase.com/change-workflow), [automatic backups](https://docs.bytebase.com/backup-restore), and [audit capabilities](https://docs.bytebase.com/audit-logging) to ensure safe and efficient database changes.

## FAQ

### What is the difference between DDL and DML?

DDL (Data Definition Language) defines and modifies the structure of database objects like tables, indexes, and schemas using commands like CREATE, ALTER, and DROP. DML (Data Manipulation Language) manipulates the data within those objects using commands like SELECT, INSERT, UPDATE, and DELETE.

### What are the 5 DDL commands?

The five main DDL commands are: CREATE (creates new database objects), ALTER (modifies existing objects), DROP (removes objects), TRUNCATE (removes all data from a table), and RENAME (changes the name of an object).

### Is SELECT a DDL or DML command?

SELECT is a DML (Data Manipulation Language) command. It retrieves data from one or more tables. Some classifications place SELECT under a separate category called DQL (Data Query Language), but it is traditionally grouped with DML.

### Does DDL auto-commit?

In most database systems (MySQL, Oracle, SQL Server), DDL statements automatically commit the current transaction. However, PostgreSQL supports transactional DDL, meaning DDL statements can be included within a transaction and rolled back if needed.

### What is the difference between DELETE (DML) and TRUNCATE (DDL)?

DELETE is a DML command that removes specific rows based on a WHERE clause and can be rolled back. TRUNCATE is a DDL command that removes all rows from a table, auto-commits in most databases, and is faster because it does not log individual row deletions.


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