Terjemahan disediakan oleh mesin penerjemah. Jika konten terjemahan yang diberikan bertentangan dengan versi bahasa Inggris aslinya, utamakan versi bahasa Inggris.

# Python dan DAX
<a name="DAX.client.run-application-python"></a>

Ikuti prosedur ini untuk menjalankan aplikasi sampel Python pada instans Amazon EC2 Anda.

**Cara menjalankan sampel Python untuk DAX**

1. Instal klien Python DAX menggunakan utilitas `pip`.

   ```
   pip install amazon-dax-client
   ```

1. Unduh sampel kode sumber program (file `.zip`).

   ```
   wget http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/samples/TryDax.zip
   ```

   Setelah unduhan selesai, ekstrak file sumber.

   ```
   unzip TryDax.zip
   ```

1. Jalankan program Python berikut. Program pertama membuat tabel Amazon DynamoDB bernama `TryDaxTable`. Program kedua menulis data ke tabel.

   ```
   python 01-create-table.py
   python 02-write-data.py
   ```

1. Jalankan program Python berikut.

   ```
   python 03-getitem-test.py
   python 04-query-test.py
   python 05-scan-test.py
   ```

    Perhatikan informasi waktu, yaitu jumlah milidetik yang diperlukan untuk pengujian `GetItem`, `Query`, dan `Scan`.

1. Pada langkah sebelumnya, Anda menjalankan program terhadap titik akhir DynamoDB. Sekarang jalankan program lagi, tetapi kali ini, operasi `GetItem`, `Query` dan `Scan` diproses oleh klaster DAX Anda.

   Untuk menentukan titik akhir klaster DAX Anda, pilih salah satu dari berikut ini:
   + **Menggunakan konsol DynamoDB** — Pilih klaster DAX Anda. Titik akhir klaster ditampilkan pada konsol, seperti dalam contoh berikut.

     ```
     dax://my-cluster.l6fzcv.dax-clusters.us-east-1.amazonaws.com
     ```
   + **Menggunakan AWS CLI** — Masukkan perintah berikut.

     ```
     aws dax describe-clusters --query "Clusters[*].ClusterDiscoveryEndpoint"
     ```

     Titik akhir klaster ditampilkan pada output, seperti dalam contoh ini.

     ```
     {
         "Address": "my-cluster.l6fzcv.dax-clusters.us-east-1.amazonaws.com",
         "Port": 8111,
         "URL": "dax://my-cluster.l6fzcv.dax-clusters.us-east-1.amazonaws.com"
     }
     ```

   Jalankan program lagi, tetapi kali ini tentukan titik akhir klaster sebagai parameter baris perintah.

   ```
   python 03-getitem-test.py dax://my-cluster.l6fzcv.dax-clusters.us-east-1.amazonaws.com
   python 04-query-test.py dax://my-cluster.l6fzcv.dax-clusters.us-east-1.amazonaws.com
   python 05-scan-test.py dax://my-cluster.l6fzcv.dax-clusters.us-east-1.amazonaws.com
   ```

   Periksa sisa output dan perhatikan informasi waktu. Waktu berlalu untuk `GetItem`, `Query`, dan `Scan` harus jauh lebih rendah dengan DAX dibandingkan dengan DynamoDB.

1. Jalankan program Python berikut untuk menghapus `TryDaxTable`.

   ```
   python 06-delete-table.py
   ```

Untuk informasi selengkapnya tentang program tersebut, lihat bagian berikut:
+ [01-create-table.py](DAX.client.run-application-python.01-create-table.md)
+ [02-write-data.py](DAX.client.run-application-python.02-write-data.md)
+ [03-getitem-test.py](DAX.client.run-application-python.03-getitem-test.md)
+ [04-query-test.py](DAX.client.run-application-python.04-query-test.md)
+ [05-scan-test.py](DAX.client.run-application-python.05-scan-test.md)
+ [06-delete-table.py](DAX.client.run-application-python.06-delete-table.md)

# 01-create-table.py
<a name="DAX.client.run-application-python.01-create-table"></a>

Program `01-create-table.py` membuat tabel (`TryDaxTable`). Program Python yang tersisa di bagian ini bergantung pada tabel ini.

```
import boto3


def create_dax_table(dyn_resource=None):
    """
    Creates a DynamoDB table.

    :param dyn_resource: Either a Boto3 or DAX resource.
    :return: The newly created table.
    """
    if dyn_resource is None:
        dyn_resource = boto3.resource("dynamodb")

    table_name = "TryDaxTable"
    params = {
        "TableName": table_name,
        "KeySchema": [
            {"AttributeName": "partition_key", "KeyType": "HASH"},
            {"AttributeName": "sort_key", "KeyType": "RANGE"},
        ],
        "AttributeDefinitions": [
            {"AttributeName": "partition_key", "AttributeType": "N"},
            {"AttributeName": "sort_key", "AttributeType": "N"},
        ],
        "BillingMode": "PAY_PER_REQUEST",
    }
    table = dyn_resource.create_table(**params)
    print(f"Creating {table_name}...")
    table.wait_until_exists()
    return table


if __name__ == "__main__":
    dax_table = create_dax_table()
    print(f"Created table.")
```

# 02-write-data.py
<a name="DAX.client.run-application-python.02-write-data"></a>

Program `02-write-data.py` menulis data pengujian ke `TryDaxTable`.

```
import boto3


def write_data_to_dax_table(key_count, item_size, dyn_resource=None):
    """
    Writes test data to the demonstration table.

    :param key_count: The number of partition and sort keys to use to populate the
                      table. The total number of items is key_count * key_count.
    :param item_size: The size of non-key data for each test item.
    :param dyn_resource: Either a Boto3 or DAX resource.
    """
    if dyn_resource is None:
        dyn_resource = boto3.resource("dynamodb")

    table = dyn_resource.Table("TryDaxTable")
    some_data = "X" * item_size

    for partition_key in range(1, key_count + 1):
        for sort_key in range(1, key_count + 1):
            table.put_item(
                Item={
                    "partition_key": partition_key,
                    "sort_key": sort_key,
                    "some_data": some_data,
                }
            )
            print(f"Put item ({partition_key}, {sort_key}) succeeded.")


if __name__ == "__main__":
    write_key_count = 10
    write_item_size = 1000
    print(
        f"Writing {write_key_count*write_key_count} items to the table. "
        f"Each item is {write_item_size} characters."
    )
    write_data_to_dax_table(write_key_count, write_item_size)
```

# 03-getitem-test.py
<a name="DAX.client.run-application-python.03-getitem-test"></a>

Program `03-getitem-test.py` melakukan operasi `GetItem` di `TryDaxTable`. Contoh ini diberikan untuk Wilayah eu-west-1. 

```
import argparse
import sys
import time
import amazondax
import boto3


def get_item_test(key_count, iterations, dyn_resource=None):
    """
    Gets items from the table a specified number of times. The time before the
    first iteration and the time after the last iteration are both captured
    and reported.

    :param key_count: The number of items to get from the table in each iteration.
    :param iterations: The number of iterations to run.
    :param dyn_resource: Either a Boto3 or DAX resource.
    :return: The start and end times of the test.
    """
    if dyn_resource is None:
        dyn_resource = boto3.resource('dynamodb')

    table = dyn_resource.Table('TryDaxTable')
    start = time.perf_counter()
    for _ in range(iterations):
        for partition_key in range(1, key_count + 1):
            for sort_key in range(1, key_count + 1):
                table.get_item(Key={
                    'partition_key': partition_key,
                    'sort_key': sort_key
                })
                print('.', end='')
                sys.stdout.flush()
    print()
    end = time.perf_counter()
    return start, end


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
        'endpoint_url', nargs='?',
        help="When specified, the DAX cluster endpoint. Otherwise, DAX is not used.")
    args = parser.parse_args()

    test_key_count = 10
    test_iterations = 50
    if args.endpoint_url:
        print(f"Getting each item from the table {test_iterations} times, "
              f"using the DAX client.")
        # Use a with statement so the DAX client closes the cluster after completion.
        with amazondax.AmazonDaxClient.resource(endpoint_url=args.endpoint_url, region_name='eu-west-1') as dax:
            test_start, test_end = get_item_test(
                test_key_count, test_iterations, dyn_resource=dax)
    else:
        print(f"Getting each item from the table {test_iterations} times, "
              f"using the Boto3 client.")
        test_start, test_end = get_item_test(
            test_key_count, test_iterations)
    print(f"Total time: {test_end - test_start:.4f} sec. Average time: "
          f"{(test_end - test_start)/ test_iterations}.")
```

# 04-query-test.py
<a name="DAX.client.run-application-python.04-query-test"></a>

Program `04-query-test.py` melakukan operasi `Query` di `TryDaxTable`.

```
import argparse
import time
import sys
import amazondax
import boto3
from boto3.dynamodb.conditions import Key


def query_test(partition_key, sort_keys, iterations, dyn_resource=None):
    """
    Queries the table a specified number of times. The time before the
    first iteration and the time after the last iteration are both captured
    and reported.

    :param partition_key: The partition key value to use in the query. The query
                          returns items that have partition keys equal to this value.
    :param sort_keys: The range of sort key values for the query. The query returns
                      items that have sort key values between these two values.
    :param iterations: The number of iterations to run.
    :param dyn_resource: Either a Boto3 or DAX resource.
    :return: The start and end times of the test.
    """
    if dyn_resource is None:
        dyn_resource = boto3.resource("dynamodb")

    table = dyn_resource.Table("TryDaxTable")
    key_condition_expression = Key("partition_key").eq(partition_key) & Key(
        "sort_key"
    ).between(*sort_keys)

    start = time.perf_counter()
    for _ in range(iterations):
        table.query(KeyConditionExpression=key_condition_expression)
        print(".", end="")
        sys.stdout.flush()
    print()
    end = time.perf_counter()
    return start, end


if __name__ == "__main__":
    # pylint: disable=not-context-manager
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "endpoint_url",
        nargs="?",
        help="When specified, the DAX cluster endpoint. Otherwise, DAX is not used.",
    )
    args = parser.parse_args()

    test_partition_key = 5
    test_sort_keys = (2, 9)
    test_iterations = 100
    if args.endpoint_url:
        print(f"Querying the table {test_iterations} times, using the DAX client.")
        # Use a with statement so the DAX client closes the cluster after completion.
        with amazondax.AmazonDaxClient.resource(endpoint_url=args.endpoint_url) as dax:
            test_start, test_end = query_test(
                test_partition_key, test_sort_keys, test_iterations, dyn_resource=dax
            )
    else:
        print(f"Querying the table {test_iterations} times, using the Boto3 client.")
        test_start, test_end = query_test(
            test_partition_key, test_sort_keys, test_iterations
        )

    print(
        f"Total time: {test_end - test_start:.4f} sec. Average time: "
        f"{(test_end - test_start)/test_iterations}."
    )
```

# 05-scan-test.py
<a name="DAX.client.run-application-python.05-scan-test"></a>

Program `05-scan-test.py` melakukan operasi `Scan` di `TryDaxTable`.

```
import argparse
import time
import sys
import amazondax
import boto3


def scan_test(iterations, dyn_resource=None):
    """
    Scans the table a specified number of times. The time before the
    first iteration and the time after the last iteration are both captured
    and reported.

    :param iterations: The number of iterations to run.
    :param dyn_resource: Either a Boto3 or DAX resource.
    :return: The start and end times of the test.
    """
    if dyn_resource is None:
        dyn_resource = boto3.resource("dynamodb")

    table = dyn_resource.Table("TryDaxTable")
    start = time.perf_counter()
    for _ in range(iterations):
        table.scan()
        print(".", end="")
        sys.stdout.flush()
    print()
    end = time.perf_counter()
    return start, end


if __name__ == "__main__":
    # pylint: disable=not-context-manager
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "endpoint_url",
        nargs="?",
        help="When specified, the DAX cluster endpoint. Otherwise, DAX is not used.",
    )
    args = parser.parse_args()

    test_iterations = 100
    if args.endpoint_url:
        print(f"Scanning the table {test_iterations} times, using the DAX client.")
        # Use a with statement so the DAX client closes the cluster after completion.
        with amazondax.AmazonDaxClient.resource(endpoint_url=args.endpoint_url) as dax:
            test_start, test_end = scan_test(test_iterations, dyn_resource=dax)
    else:
        print(f"Scanning the table {test_iterations} times, using the Boto3 client.")
        test_start, test_end = scan_test(test_iterations)
    print(
        f"Total time: {test_end - test_start:.4f} sec. Average time: "
        f"{(test_end - test_start)/test_iterations}."
    )
```

# 06-delete-table.py
<a name="DAX.client.run-application-python.06-delete-table"></a>

Program `06-delete-table.py` menghapus `TryDaxTable`. Jalankan program ini setelah Anda selesai menguji fungsionalitas Amazon DynamoDB Accelerator (DAX).

```
import boto3


def delete_dax_table(dyn_resource=None):
    """
    Deletes the demonstration table.

    :param dyn_resource: Either a Boto3 or DAX resource.
    """
    if dyn_resource is None:
        dyn_resource = boto3.resource("dynamodb")

    table = dyn_resource.Table("TryDaxTable")
    table.delete()

    print(f"Deleting {table.name}...")
    table.wait_until_not_exists()


if __name__ == "__main__":
    delete_dax_table()
    print("Table deleted!")
```