* And validators allow complex data schemas to be clearly and easily defined, checked and documented as JSON Schema.
    * You can have deeply **nested JSON** objects and have them all validated and annotated.
* **Extensible**:
    * Pydantic allows custom data types to be defined or you can extend validation with methods on a model decorated with the validator decorator.

This client could be a browser with a frontend, a code from someone else, an IoT device, etc.

You could need to tell the client that:

* The client doesn't have enough privileges for that operation.
* The client doesn't have access to that resource.
* The item the client was trying to access doesn't exist.
* etc.

          // Inline the Comparator implementation rather than transforming with a Function
          // to save code size.
          sort(
              entryArray,
              0,
              size,
              (e1, e2) -> {
                // requireNonNull is safe because the first `size` elements have been filled in.
                requireNonNull(e1);
                requireNonNull(e2);

   *
   * <p>Thus, two networks A and B are equal if <b>all</b> of the following are true:
   *
   * <ul>
   *   <li>A and B have equal {@link #isDirected() directedness}.
   *   <li>A and B have equal {@link #nodes() node sets}.
   *   <li>A and B have equal {@link #edges() edge sets}.
   *   <li>Every edge in A and B connects the same nodes in the same direction (if any).
   * </ul>
   *

 * any other implementation of this interface.
 *
 * <p>If you have your own graph implementation based around a custom node type {@code MyNode},
 * which has a method {@code getChildren()} that retrieves its successors in a graph:
 *
 * <pre>{@code
 * someGraphAlgorithm(startNode, MyNode::getChildren);
 * }</pre>
 *
 * <p>If you have some other mechanism for returning the successors of a node, or one that doesn't

## Steps to Reproduce (for bugs)
<!--- Provide a link to a live example, or an unambiguous set of steps to -->
<!--- reproduce this bug. Include code to reproduce, if relevant -->
<!--- and make sure you have followed https://github.com/minio/minio/tree/release/docs/debugging to capture relevant logs -->

1.
2.
3.
4.

## Context
<!--- How has this issue affected you? What are you trying to accomplish? -->

Doing this provides the following specific benefits:
- **Reduce the API surface** - We no longer need to maintain two methods.
- **Consistency** - All languages have consistent access to the same APIs and ergonomics in the DSL.
- **Reduce the size of the Gradle distribution** - We no longer need to carry multiple standard libraries for different languages.

## Decision

          @Override
          public void run() {
            assertFalse("previous tearDown should have run before setUp", setUpRan.getAndSet(true));
          }
        };
    Runnable tearDown =
        new Runnable() {
          @Override
          public void run() {
            assertTrue("setUp should have run", setUpRan.getAndSet(false));
          }
        };

### High availability

A stand-alone MinIO server would go down if the server hosting the drives goes offline. In contrast, a distributed MinIO setup with _m_ servers and _n_ drives will have your data safe as long as _m/2_ servers or _m*n_/2 or more drives are online.

## Overview

* Full-Featured ORM
* Associations (Has One, Has Many, Belongs To, Many To Many, Polymorphism, Single-table inheritance)
* Hooks (Before/After Create/Save/Update/Delete/Find)
* Eager loading with `Preload`, `Joins`
* Transactions, Nested Transactions, Save Point, RollbackTo to Saved Point
* Context, Prepared Statement Mode, DryRun Mode
* Batch Insert, FindInBatches, Find To Map