### Initialize Flink Quickstart Environment

Source: https://github.com/apache/iceberg/blob/main/site/docs/flink-quickstart.md

Clone the repository and start the required Docker containers for the Flink and Iceberg environment.

```sh
git clone https://github.com/apache/iceberg.git
cd iceberg
docker compose -f docker/iceberg-flink-quickstart/docker-compose.yml up -d --build
```

--------------------------------

### Install Daft and PyIceberg

Source: https://github.com/apache/iceberg/blob/main/site/docs/integrations/daft.md

Install the required libraries in your Python environment.

```bash
pip install daft pyiceberg
```

--------------------------------

### Install and Run Linkchecker

Source: https://github.com/apache/iceberg/blob/main/site/README.md

Commands to install the linkchecker tool and run it against a local development server. It also shows how to output the results to a CSV file for review.

```bash
pip install linkchecker

./linkchecker http://localhost:8000 -r1 -Fcsv/link_warnings.csv

cat ./link_warnings.csv
```

--------------------------------

### Start Docker containers

Source: https://github.com/apache/iceberg/blob/main/site/docs/spark-quickstart.md

Execute this command to initialize the services defined in the docker-compose.yml file.

```sh
docker-compose up
```

--------------------------------

### Snapshot Log Example

Source: https://github.com/apache/iceberg/blob/main/format/spec.md

Provides an example of the snapshot log, which records historical snapshots with their IDs and timestamps.

```json
[
  {
    "snapshot-id": 3051729675574597004,
    "timestamp-ms": 1515100955770
  }
]
```

--------------------------------

### Table Properties Example

Source: https://github.com/apache/iceberg/blob/main/format/spec.md

Illustrates the structure for table properties, which are key-value pairs used for configuration.

```json
{
  "write.format.default": "avro",
  "commit.retry.num-retries": "4"
}
```

--------------------------------

### SQL View Representation Examples

Source: https://github.com/apache/iceberg/blob/main/format/view-spec.md

Examples of SQL statements used to define or interact with views in Iceberg.

```sql
USE prod.default
```

```sql
CREATE OR REPLACE VIEW event_agg (
    event_count COMMENT 'Count of events',
    event_date) AS
SELECT
    COUNT(1), CAST(event_ts AS DATE)
FROM events
GROUP BY 2
```

--------------------------------

### Configure RCK via Environment Variables

Source: https://github.com/apache/iceberg/blob/main/open-api/README.md

Example of configuring the catalog client using environment variables with the CATALOG_ prefix.

```shell
CATALOG_URI=https://my_rest_server.io/                    ## -> uri=https://my_rest_server.io/
CATALOG_WAREHOUSE=test_warehouse                          ## -> warehouse=test_warehouse
CATALOG_IO__IMPL=org.apache.iceberg.aws.s3.S3FileIO       ## -> io-impl=org.apache.iceberg.aws.s3.S3FileIO
CATALOG_CREDENTIAL=<oauth_key>:<oauth_secret>             ## -> credential=<oauth_key>:<oauth_secret>
```

--------------------------------

### Example of system.bucket() Function

Source: https://github.com/apache/iceberg/blob/main/docs/docs/spark-queries.md

Illustrates the usage of the system.bucket() function, which returns an integer representing the bucket for a given column value and number of buckets.

```sql
SELECT system.bucket(16, id) FROM prod.db.table;
```

--------------------------------

### Snapshot Log Entry Example

Source: https://github.com/apache/iceberg/blob/main/site/docs/spec.md

Provides an example of a single entry in the snapshot log, containing snapshot ID and timestamp.

```json
[
  {
  "snapshot-id": 3051729675574597004,
  "timestamp-ms": 1515100955770
  }
]
```

--------------------------------

### Configure RCK via Java System Properties

Source: https://github.com/apache/iceberg/blob/main/open-api/README.md

Example of configuring the catalog client using Java system properties with the rck. prefix.

```shell
rck.uri=https://my_rest_server.io/                    ## -> uri=https://my_rest_server.io/
rck.warehouse=test_warehouse                          ## -> warehouse=test_warehouse
rck.io-impl=org.apache.iceberg.aws.s3.S3FileIO        ## -> io-impl=org.apache.iceberg.aws.s3.S3FileIO
rck.credential=<oauth_key>:<oauth_secret>             ## -> credential=<oauth_key>:<oauth_secret>
```

--------------------------------

### SQL Representation Metadata Example

Source: https://github.com/apache/iceberg/blob/main/site/docs/view-spec.md

Example of metadata for a SQL representation, showing the type, SQL query, and dialect.

```text
| Field name          | Value |
|---------------------|-------|
| `type`              | "sql" |
| `sql`               | "SELECT\n    COUNT(1), CAST(event_ts AS DATE)\nFROM events\nGROUP BY 2" |
| `dialect`           | "spark" |
```

--------------------------------

### Metadata Log Entry Example

Source: https://github.com/apache/iceberg/blob/main/site/docs/spec.md

An example of an entry in the metadata log, referencing a metadata file and its timestamp.

```json
[
  {
  "metadata-file": "s3://bucket/.../v1.json",
  "timestamp-ms": 1515100955770
  }
]
```

--------------------------------

### Metadata Log Example

Source: https://github.com/apache/iceberg/blob/main/format/spec.md

Demonstrates the structure of the metadata log, which tracks metadata file changes and their timestamps.

```json
[
  {
    "metadata-file": "s3://bucket/.../v1.json",
    "timestamp-ms": 1515100955770
  }
]
```

--------------------------------

### Start Spark Shell for Iceberg Verification

Source: https://github.com/apache/iceberg/blob/main/site/docs/how-to-release.md

Use this command to start a spark-shell configured with the necessary Iceberg runtime and catalog settings for verification.

```bash
spark-shell \
    --conf spark.jars.repositories=${MAVEN_URL} \
    --packages org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:{{ icebergVersion }} \
    --conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions \
    --conf spark.sql.catalog.local=org.apache.iceberg.spark.SparkCatalog \
    --conf spark.sql.catalog.local.type=hadoop \
    --conf spark.sql.catalog.local.warehouse=$PWD/warehouse \
    --conf spark.sql.catalog.local.default-namespace=default \
    --conf spark.sql.defaultCatalog=local
```

--------------------------------

### Initialize Flink SQL Client with SQL File

Source: https://github.com/apache/iceberg/blob/main/docs/docs/flink-ddl.md

Execute an initialization SQL file when starting the Flink SQL Client using the `-i` option.

```bash
/path/to/bin/sql-client.sh -i /path/to/init.sql
```

--------------------------------

### Build Iceberg Flink Quickstart Docker Image Locally

Source: https://github.com/apache/iceberg/blob/main/docker/iceberg-flink-quickstart/README.md

Build the Docker image locally using default versions. This command creates an image tagged as apache/iceberg-flink-quickstart.

```bash
docker build -t apache/iceberg-flink-quickstart docker/iceberg-flink-quickstart/
```

--------------------------------

### Build Iceberg Project Locally

Source: https://github.com/apache/iceberg/blob/main/site/docs/contribute.md

Use Gradle to build the Iceberg project and run tests. Ensure you have Java 17 or 21 installed.

```bash
./gradlew build
```

```bash
./gradlew build -x test -x integrationTest
```

```bash
./gradlew spotlessApply
```

```bash
./gradlew build -DsparkVersions=3.5,4.0 -DflinkVersions=1.20,2.0
```

--------------------------------

### Table Reference Example

Source: https://github.com/apache/iceberg/blob/main/format/spec.md

Illustrates the structure for table references, used for tags or other named snapshots.

```json
{
  "test": {
    "snapshot-id": 123456789000,
    "type": "tag",
    "max-ref-age-ms": 10000000
  }
}
```

--------------------------------

### Start Spark Session

Source: https://github.com/apache/iceberg/blob/main/examples/Convert table to Iceberg.ipynb

Initiates a Spark session. Ensure the iceberg-runtime JAR is added to your Spark environment.

```python
spark
```

--------------------------------

### Partition Field JSON Example

Source: https://github.com/apache/iceberg/blob/main/site/docs/spec.md

An example of a single partition field within a partition spec. It shows the source ID, field ID, name, and the transform applied.

```json
{
  "source-id": 4,
  "field-id": 1000,
  "name": "ts_day",
  "transform": "day"
}
```

--------------------------------

### Equality Delete File Example 2

Source: https://github.com/apache/iceberg/blob/main/format/spec.md

An example of an equality delete file that includes the full row data for the deleted row. This is an alternative way to represent the delete for 'id = 3'.

```text
equality_ids=[1]

 1: id | 2: category | 3: name
-------|-------------|---------
 3     | NULL        | Grizzly
```

--------------------------------

### Equality Delete File Example 3

Source: https://github.com/apache/iceberg/blob/main/format/spec.md

An example of an equality delete file used to delete rows where 'id' equals 4 AND 'category' is NULL. This demonstrates using multiple columns for equality matching.

```text
equality_ids=[1, 2]

 1: id | 2: category | 3: name
-------|-------------|---------
 4     | NULL        | Polar
```

--------------------------------

### Execute Flink Streaming Read

Source: https://github.com/apache/iceberg/blob/main/docs/docs/flink-queries.md

Configure streaming mode and use SQL hints to define monitoring intervals and snapshot start points.

```sql
-- Submit the flink job in streaming mode for current session.
SET execution.runtime-mode = streaming;

-- Enable this switch because streaming read SQL will provide few job options in flink SQL hint options.
SET table.dynamic-table-options.enabled=true;

-- Read all the records from the iceberg current snapshot, and then read incremental data starting from that snapshot.
SELECT * FROM sample /*+ OPTIONS('streaming'='true', 'monitor-interval'='1s')*/ ;

-- Read all incremental data starting from the snapshot-id '3821550127947089987' (records from this snapshot will be excluded).
SELECT * FROM sample /*+ OPTIONS('streaming'='true', 'monitor-interval'='1s', 'start-snapshot-id'='3821550127947089987')*/ ;
```

--------------------------------

### Sort Field JSON Example

Source: https://github.com/apache/iceberg/blob/main/site/docs/spec.md

An example of a single sort field within a sort order. It specifies the transform, source ID, sorting direction, and how null values should be ordered.

```json
{
  "transform": "identity",
  "source-id": 2,
  "direction": "asc",
  "null-order": "nulls-first"
}
```

--------------------------------

### Sort Order JSON Example

Source: https://github.com/apache/iceberg/blob/main/site/docs/spec.md

This is an example of a sort order serialized as a list of JSON objects. Each object defines a sort field with transform, source ID, direction, and null order.

```json
[
  {
    "transform": "identity",
    "source-id": 2,
    "direction": "asc",
    "null-order": "nulls-first"
  },
  {
    "transform": "bucket[4]",
    "source-id": 3,
    "direction": "desc",
    "null-order": "nulls-last"
  }
]
```

--------------------------------

### Equality Delete File Example 1

Source: https://github.com/apache/iceberg/blob/main/format/spec.md

An example of an equality delete file used to delete rows where the 'id' column equals 3. The 'equality_ids' metadata column specifies which table columns are used for matching.

```text
equality_ids=[1]

 1: id
-------
 3
```

--------------------------------

### Encryption Key Example

Source: https://github.com/apache/iceberg/blob/main/format/spec.md

Shows the format for encryption key information within the table metadata.

```json
[ {"key-id": "5f819b", "key-metadata": "aWNlYmVyZwo="} ]
```

--------------------------------

### Create a Partition Spec

Source: https://github.com/apache/iceberg/blob/main/docs/docs/java-api-quickstart.md

Build a PartitionSpec for a table's schema to define how records are grouped into data files. This example partitions by hour and identity.

```java
import org.apache.iceberg.PartitionSpec;

PartitionSpec spec = PartitionSpec.builderFor(schema)
      .hour("event_time")
      .identity("level")
      .build();
```

--------------------------------

### List Tables in a Catalog with pyiceberg

Source: https://github.com/apache/iceberg/blob/main/docker/iceberg-rest-fixture/README.md

To list tables within a specific catalog, provide the catalog name after the `list` command. This example lists tables in the 'nyc' catalog.

```bash
➜  ~ pyiceberg --uri http://localhost:8181 list nyc
nyc.taxis           
nyc.taxis3          
nyc.taxis4          
nyc.taxis_copy_maybe
yc.taxis_skeleton  
yc.taxis_skeleton2 
```

--------------------------------

### Snapshot Object Example

Source: https://github.com/apache/iceberg/blob/main/site/docs/spec.md

Shows the structure of a single snapshot object within the snapshots list in table metadata.

```json
[
  {
  "snapshot-id": 3051729675574597004,
  "timestamp-ms": 1515100955770,
  "summary": {
    "operation": "append"
  },
  "manifest-list": "s3://b/wh/.../s1.avro"
  "schema-id": 0
  }
]
```

--------------------------------

### SQL to Create Iceberg Destination Table

Source: https://github.com/apache/iceberg/blob/main/docs/docs/kafka-connect.md

Example SQL statement to create a destination table in Iceberg, partitioned by hours of a timestamp column.

```sql
CREATE TABLE default.events (
    id STRING,
    type STRING,
    ts TIMESTAMP,
    payload STRING)
PARTITIONED BY (hours(ts))
```

--------------------------------

### Initialize Table and Data

Source: https://github.com/apache/iceberg/blob/main/docs/docs/branching.md

Create a sample table and insert initial records.

```sql
CREATE TABLE db.table (id bigint, data string, col float);
INSERT INTO db.table VALUES (1, 'a', 1.0), (2, 'b', 2.0), (3, 'c', 3.0);
SELECT * FROM db.table;
1   a   1.0
2   b   2.0
3   c   3.0
```

--------------------------------

### Name Mapping Serialization Example

Source: https://github.com/apache/iceberg/blob/main/format/spec.md

This JSON structure defines how field names are mapped, including nested fields and alternative names.

```json
[
  {
    "field-id": 1,
    "names": ["id", "record_id"]
  },
  {
    "field-id": 2,
    "names": ["data"]
  },
  {
    "field-id": 3,
    "names": ["location"],
    "fields": [
      {
        "field-id": 4,
        "names": ["latitude", "lat"]
      },
      {
        "field-id": 5,
        "names": ["longitude", "long"]
      }
    ]
  }
]
```

--------------------------------

### Configure URL Connection Socket Timeout

Source: https://github.com/apache/iceberg/blob/main/docs/docs/aws.md

Example of configuring the socket timeout for the URL Connection HTTP Client when starting a Spark shell. This sets the timeout to 80 milliseconds.

```shell
--conf spark.sql.catalog.my_catalog.http-client.urlconnection.socket-timeout-ms=80
```

--------------------------------

### Quick Start: Table Maintenance Job

Source: https://github.com/apache/iceberg/blob/main/docs/docs/flink-maintenance.md

A comprehensive example demonstrating how to set up an automated table maintenance job in Flink, including options for external lock factories or Flink-managed locks.

```APIDOC
### Quick Start

The following example demonstrates the implementation of automated maintenance for an Iceberg table within a Flink environment.

```java
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

TableLoader tableLoader = TableLoader.fromCatalog(
    CatalogLoader.hive("my_catalog", configuration, properties),  
    TableIdentifier.of("database", "table")
);

Map<String, String> jdbcProps = new HashMap<>();
jdbcProps.put("jdbc.user", "flink");
jdbcProps.put("jdbc.password", "flinkpw");

//JdbcLockFactory Example
TriggerLockFactory lockFactory = new JdbcLockFactory(
    "jdbc:postgresql://localhost:5432/iceberg", // JDBC URL
    "catalog.db.table",                         // Lock ID (unique identifier)
    jdbcProps                                   // JDBC connection properties
);

// Option 1: With external lock factory (plan to deprecate this Option since 1.12)
TableMaintenance.forTable(env, tableLoader, lockFactory)
// Option 2: With Flink-managed lock (no external lock required)
TableMaintenance.forTable(env, tableLoader)
    .uidSuffix("my-maintenance-job")
    .rateLimit(Duration.ofMinutes(10))
    .lockCheckDelay(Duration.ofSeconds(10))
    .add(ExpireSnapshots.builder()
        .scheduleOnCommitCount(10)
        .maxSnapshotAge(Duration.ofMinutes(10))
        .retainLast(5)
        .deleteBatchSize(5)
        .parallelism(8))
    .add(RewriteDataFiles.builder()
        .scheduleOnDataFileCount(10)
        .targetFileSizeBytes(128 * 1024 * 1024)
        .partialProgressEnabled(true)
        .partialProgressMaxCommits(10))
    .append();

env.execute("Table Maintenance Job");
```
```

--------------------------------

### Flink Streaming Read from Latest Snapshot

Source: https://github.com/apache/iceberg/blob/main/docs/docs/flink-queries.md

Starts a streaming read from the latest table snapshot and polls for new snapshots every 60 seconds. This example uses the FLIP-27 source interface and does not support CDC read yet.

```java
StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironment();
TableLoader tableLoader = TableLoader.fromHadoopTable("hdfs://nn:8020/warehouse/path");

IcebergSource source = IcebergSource.forRowData()
    .tableLoader(tableLoader)
    .assignerFactory(new SimpleSplitAssignerFactory())
    .streaming(true)
    .streamingStartingStrategy(StreamingStartingStrategy.INCREMENTAL_FROM_LATEST_SNAPSHOT)
    .monitorInterval(Duration.ofSeconds(60))
    .build();

DataStream<RowData> stream = env.fromSource(
    source,
    WatermarkStrategy.noWatermarks(),
    "My Iceberg Source",
    TypeInformation.of(RowData.class));

// Print all records to stdout.
stream.print();

// Submit and execute this streaming read job.
env.execute("Test Iceberg Streaming Read");
```

--------------------------------

### Show Makefile Help

Source: https://github.com/apache/iceberg/blob/main/site/README.md

Displays a list of available Makefile recipes for managing the documentation site.

```bash
make help
```

--------------------------------

### Snapshot Object Example

Source: https://github.com/apache/iceberg/blob/main/format/spec.md

Shows the structure of a snapshot object within the table metadata, including its ID, timestamp, summary, manifest list location, and schema ID.

```json
[
  {
    "snapshot-id": 3051729675574597004,
    "timestamp-ms": 1515100955770,
    "summary": {
      "operation": "append"
    },
    "manifest-list": "s3://b/wh/.../s1.avro"
    "schema-id": 0
  }
]
```

--------------------------------

### Start Iceberg Flink Docker Compose Stack

Source: https://github.com/apache/iceberg/blob/main/docker/iceberg-flink-quickstart/README.md

Start the Docker containers defined in the docker-compose.yml file. The --build flag ensures that the image is built before starting the containers.

```bash
docker compose -f docker/iceberg-flink-quickstart/docker-compose.yml up -d --build
```

--------------------------------

### Create Table Like Existing Table

Source: https://github.com/apache/iceberg/blob/main/docs/docs/flink-ddl.md

Create a new table 'sample_like' with the same schema, partitioning, and properties as an existing table 'sample'.

```sql
CREATE TABLE `hive_catalog`.`default`.`sample` (
    id BIGINT COMMENT 'unique id',
    data STRING
);

CREATE TABLE  `hive_catalog`.`default`.`sample_like` LIKE `hive_catalog`.`default`.`sample`;
```

--------------------------------

### Avro Schema for Map with Key and Value IDs

Source: https://github.com/apache/iceberg/blob/main/format/spec.md

Example of an Avro schema for a map type, including 'key-id' and 'value-id'. This is used when keys are not strings and for schema evolution.

```json
{ "type": "map", "values": "int", "key-id": 10, "value-id": 11 }
```

--------------------------------

### Build and Stage Convenience Binaries

Source: https://github.com/apache/iceberg/blob/main/site/docs/how-to-release.md

Execute the stage-binaries.sh script to build and publish convenience binaries to a release staging repository.

```bash
dev/stage-binaries.sh
```

--------------------------------

### Install Apache Flink Dependency

Source: https://github.com/apache/iceberg/blob/main/docs/docs/flink.md

Use pip to install the required Apache Flink version.

```bash
pip install apache-flink=={{ flinkVersion }}
```

--------------------------------

### Example Stats Struct for an Integer Field

Source: https://github.com/apache/iceberg/blob/main/format/spec.md

Illustrates the structure of a statistics struct for a required integer field, showing common metrics like lower bound, upper bound, tight bounds, and value counts. Note that null_value_count, nan_value_count, and avg_value_size_in_bytes are omitted as they are not applicable to this field type.

```text
10_400: optional struct id (default null) {
  10_401: optional int lower_bound; // type matches the field type (int)
  10_402: optional int upper_bound; // type matches the field type (int)
  10_403: optional boolean tight_bounds;
  10_404: optional long value_count;

  // null_value_count is only used for optional fields
  // nan_value_count is only used for float and double
  // avg_value_size_in_bytes is only used for variable length types
}
```

--------------------------------

### SQL Representation Example

Source: https://github.com/apache/iceberg/blob/main/site/docs/view-spec.md

Example of a SQL representation for a view definition, including dialect and the SQL statement.

```json
{
  "type": "sql",
  "sql": "SELECT\n    COUNT(1), CAST(event_ts AS DATE)\nFROM events\nGROUP BY 2",
  "dialect": "spark"
}
```

--------------------------------

### Avro Schema for List Element with Element ID

Source: https://github.com/apache/iceberg/blob/main/format/spec.md

Example of an Avro schema for an array type, specifying an 'element-id' for the array's items. This is relevant for list schema evolution.

```json
{ "type": "array", "items": "int", "element-id": 9 }
```

--------------------------------

### Initialize Nessie Catalog in Java

Source: https://github.com/apache/iceberg/blob/main/docs/docs/nessie.md

Load the Nessie catalog programmatically using CatalogUtil with required warehouse, reference, and URI settings.

```java
Map<String, String> options = new HashMap<>();
options.put("warehouse", "/path/to/warehouse");
options.put("ref", "main");
options.put("uri", "https://localhost:19120/api/v2");
Catalog nessieCatalog = CatalogUtil.loadCatalog("org.apache.iceberg.nessie.NessieCatalog", "nessie", options, hadoopConfig);
```

--------------------------------

### Avro Schema for Struct Field with Field ID

Source: https://github.com/apache/iceberg/blob/main/format/spec.md

Example of an Avro record schema defining a struct field with a 'field-id'. This is used for schema evolution and identification.

```json
{ "type": "record", "name": "r1", "fields": [ { "name": "l", "type": ["null", "long"], "default": null, "field-id": 8 } ] }
```

--------------------------------

### Serve Site in Development Mode

Source: https://github.com/apache/iceberg/blob/main/site/README.md

Builds and serves only the 'nightly' and 'latest' versions for faster iterative development. Skips historical versions.

```bash
make serve-dev
```

--------------------------------

### Primitive Type Serialization

Source: https://github.com/apache/iceberg/blob/main/site/docs/spec.md

Primitive data types are represented as JSON strings. For example, 'int' is serialized as "int", and 'boolean' as "boolean".

```json
"unknown"
"boolean"
"int"
"long"
"float"
"double"
"date"
"time"
"timestamp"
"timestamptz"
"timestamp_ns"
"timestamptz_ns"
"string"
"uuid"
"binary"
"variant"
"geometry(srid:4326)"
"geography(srid:4326, spherical)"
```

--------------------------------

### LoggingMetricsReporter Commit Report Example

Source: https://github.com/apache/iceberg/blob/main/docs/docs/metrics-reporting.md

Example output of a CommitReport logged by the LoggingMetricsReporter. This report summarizes metrics from a commit operation.

```log
INFO org.apache.iceberg.metrics.LoggingMetricsReporter - Received metrics report: 
CommitReport{
    tableName=scan-planning-with-eq-and-pos-delete-files, 
    snapshotId=1, 
    sequenceNumber=1, 
    operation=append, 
    commitMetrics=CommitMetricsResult{
        totalDuration=TimerResult{timeUnit=NANOSECONDS, totalDuration=PT0.098429626S, count=1},
        attempts=CounterResult{unit=COUNT, value=1},
        addedDataFiles=CounterResult{unit=COUNT, value=1},
        removedDataFiles=null,
        totalDataFiles=CounterResult{unit=COUNT, value=1},
        addedDeleteFiles=null,
        addedEqualityDeleteFiles=null,
        addedPositionalDeleteFiles=null,
        removedDeleteFiles=null,
        removedEqualityDeleteFiles=null,
        removedPositionalDeleteFiles=null,
        totalDeleteFiles=CounterResult{unit=COUNT, value=0},
        addedRecords=CounterResult{unit=COUNT, value=1},
        removedRecords=null,
        totalRecords=CounterResult{unit=COUNT, value=1},
        addedFilesSizeInBytes=CounterResult{unit=BYTES, value=10},
        removedFilesSizeInBytes=null,
        totalFilesSizeInBytes=CounterResult{unit=BYTES, value=10},
        addedPositionalDeletes=null,
        removedPositionalDeletes=null,
        totalPositionalDeletes=CounterResult{unit=COUNT, value=0},
        addedEqualityDeletes=null,
        removedEqualityDeletes=null,
        totalEqualityDeletes=CounterResult{unit=COUNT, value=0}},
    metadata={
        iceberg-version=Apache Iceberg 1.4.0-SNAPSHOT (commit 4868d2823004c8c256a50ea7c25cff94314cc135)}}
```

--------------------------------

### Prefer Specific Method Names Over Getters

Source: https://github.com/apache/iceberg/blob/main/site/docs/contribute.md

Avoid using 'get' in method names unless the object is a Java bean. Replace 'get' with a more specific verb like 'find' or 'fetch'. If no specific verb applies or it's a simple getter, omit 'get' to shorten names.

```java
  // prefer exposing suppressFailure in method names
  public void sendMessageIgnoreFailure() {
    sendMessageInternal(true);
  }

  public void sendMessage() {
    sendMessageInternal(false);
  }

  private void sendMessageInternal(boolean suppressFailure) {
    ...
  }
```

--------------------------------

### Serve Iceberg Site Locally

Source: https://github.com/apache/iceberg/blob/main/site/README.md

Builds the site and runs a local development server at http://localhost:8000. Ideal for previewing changes.

```bash
make serve
```

--------------------------------

### Build Source Release Artifacts

Source: https://github.com/apache/iceberg/blob/main/site/docs/how-to-release.md

Run the source-release.sh script with the release version and candidate number to create source release artifacts. Ensure you replace '<YOUR KEY ID HERE>' with your actual key ID.

```bash
dev/source-release.sh -v 1.8.0 -r 0 -k <YOUR KEY ID HERE>
```

--------------------------------

### Flink Streaming Read from Start Tag

Source: https://github.com/apache/iceberg/blob/main/docs/docs/flink-queries.md

Initiates a streaming read from an Iceberg table starting from a specified tag using the FlinkSource API.

```java
StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironment();
TableLoader tableLoader = TableLoader.fromHadoopTable("hdfs://nn:8020/warehouse/path");
DataStream<RowData> batch = FlinkSource.forRowData()
    .env(env)
    .tableLoader(tableLoader)
    .streaming(true)
    .startTag("test-tag")
    .build();
```

--------------------------------

### LoggingMetricsReporter Scan Report Example

Source: https://github.com/apache/iceberg/blob/main/docs/docs/metrics-reporting.md

Example output of a ScanReport logged by the LoggingMetricsReporter. This report details metrics related to a table scan operation.

```log
INFO org.apache.iceberg.metrics.LoggingMetricsReporter - Received metrics report: 
ScanReport{
    tableName=scan-planning-with-eq-and-pos-delete-files, 
    snapshotId=2, 
    filter=ref(name="data") == "(hash-27fa7cc0)", 
    schemaId=0, 
    projectedFieldIds=[1, 2], 
    projectedFieldNames=[id, data], 
    scanMetrics=ScanMetricsResult{
        totalPlanningDuration=TimerResult{timeUnit=NANOSECONDS, totalDuration=PT0.026569404S, count=1},
        resultDataFiles=CounterResult{unit=COUNT, value=1},
        resultDeleteFiles=CounterResult{unit=COUNT, value=2},
        totalDataManifests=CounterResult{unit=COUNT, value=1},
        totalDeleteManifests=CounterResult{unit=COUNT, value=1},
        scannedDataManifests=CounterResult{unit=COUNT, value=1},
        skippedDataManifests=CounterResult{unit=COUNT, value=0},
        totalFileSizeInBytes=CounterResult{unit=BYTES, value=10},
        totalDeleteFileSizeInBytes=CounterResult{unit=BYTES, value=20},
        skippedDataFiles=CounterResult{unit=COUNT, value=0},
        skippedDeleteFiles=CounterResult{unit=COUNT, value=0},
        scannedDeleteManifests=CounterResult{unit=COUNT, value=1},
        skippedDeleteManifests=CounterResult{unit=COUNT, value=0},
        indexedDeleteFiles=CounterResult{unit=COUNT, value=2},
        equalityDeleteFiles=CounterResult{unit=COUNT, value=1},
        positionalDeleteFiles=CounterResult{unit=COUNT, value=1}},
    metadata={
        iceberg-version=Apache Iceberg 1.4.0-SNAPSHOT (commit 4868d2823004c8c256a50ea7c25cff94314cc135)}}
```