DynamoDB examples using SDK for Python (Boto3)

import boto3

# Create a DynamoDB client using the default credentials and region
dynamodb = boto3.client("dynamodb")

# Initialize a paginator for the list_tables operation
paginator = dynamodb.get_paginator("list_tables")

# Create a PageIterator from the paginator
page_iterator = paginator.paginate(Limit=10)

# List the tables in the current AWS account
print("Here are the DynamoDB tables in your account:")

# Use pagination to list all tables
table_names = []

for page in page_iterator:
    for table_name in page.get("TableNames", []):
        print(f"- {table_name}")
        table_names.append(table_name)

if not table_names:
    print("You don't have any DynamoDB tables in your account.")
else:
    print(f"\nFound {len(table_names)} tables.")

Create a class that encapsulates a DynamoDB table.

from decimal import Decimal
from io import BytesIO
import json
import logging
import os
from pprint import pprint
import requests
from zipfile import ZipFile
import boto3
from boto3.dynamodb.conditions import Key
from botocore.exceptions import ClientError
from question import Question

logger = logging.getLogger(__name__)

class Movies:
    """Encapsulates an Amazon DynamoDB table of movie data.

    Example data structure for a movie record in this table:
        {
            "year": 1999,
            "title": "For Love of the Game",
            "info": {
                "directors": ["Sam Raimi"],
                "release_date": "1999-09-15T00:00:00Z",
                "rating": 6.3,
                "plot": "A washed up pitcher flashes through his career.",
                "rank": 4987,
                "running_time_secs": 8220,
                "actors": [
                    "Kevin Costner",
                    "Kelly Preston",
                    "John C. Reilly"
                ]
            }
        }
    """

    def __init__(self, dyn_resource):
        """
        :param dyn_resource: A Boto3 DynamoDB resource.
        """
        self.dyn_resource = dyn_resource
        # The table variable is set during the scenario in the call to
        # 'exists' if the table exists. Otherwise, it is set by 'create_table'.
        self.table = None


    def exists(self, table_name):
        """
        Determines whether a table exists. As a side effect, stores the table in
        a member variable.

        :param table_name: The name of the table to check.
        :return: True when the table exists; otherwise, False.
        """
        try:
            table = self.dyn_resource.Table(table_name)
            table.load()
            exists = True
        except ClientError as err:
            if err.response["Error"]["Code"] == "ResourceNotFoundException":
                exists = False
            else:
                logger.error(
                    "Couldn't check for existence of %s. Here's why: %s: %s",
                    table_name,
                    err.response["Error"]["Code"],
                    err.response["Error"]["Message"],
                )
                raise
        else:
            self.table = table
        return exists


    def create_table(self, table_name):
        """
        Creates an Amazon DynamoDB table that can be used to store movie data.
        The table uses the release year of the movie as the partition key and the
        title as the sort key.

        :param table_name: The name of the table to create.
        :return: The newly created table.
        """
        try:
            self.table = self.dyn_resource.create_table(
                TableName=table_name,
                KeySchema=[
                    {"AttributeName": "year", "KeyType": "HASH"},  # Partition key
                    {"AttributeName": "title", "KeyType": "RANGE"},  # Sort key
                ],
                AttributeDefinitions=[
                    {"AttributeName": "year", "AttributeType": "N"},
                    {"AttributeName": "title", "AttributeType": "S"},
                ],
                BillingMode='PAY_PER_REQUEST',
            )
            self.table.wait_until_exists()
        except ClientError as err:
            logger.error(
                "Couldn't create table %s. Here's why: %s: %s",
                table_name,
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise
        else:
            return self.table


    def list_tables(self):
        """
        Lists the Amazon DynamoDB tables for the current account.

        :return: The list of tables.
        """
        try:
            tables = []
            for table in self.dyn_resource.tables.all():
                print(table.name)
                tables.append(table)
        except ClientError as err:
            logger.error(
                "Couldn't list tables. Here's why: %s: %s",
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise
        else:
            return tables


    def write_batch(self, movies):
        """
        Fills an Amazon DynamoDB table with the specified data, using the Boto3
        Table.batch_writer() function to put the items in the table.
        Inside the context manager, Table.batch_writer builds a list of
        requests. On exiting the context manager, Table.batch_writer starts sending
        batches of write requests to Amazon DynamoDB and automatically
        handles chunking, buffering, and retrying.

        :param movies: The data to put in the table. Each item must contain at least
                       the keys required by the schema that was specified when the
                       table was created.
        """
        try:
            with self.table.batch_writer() as writer:
                for movie in movies:
                    writer.put_item(Item=movie)
        except ClientError as err:
            logger.error(
                "Couldn't load data into table %s. Here's why: %s: %s",
                self.table.name,
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise


    def add_movie(self, title, year, plot, rating):
        """
        Adds a movie to the table.

        :param title: The title of the movie.
        :param year: The release year of the movie.
        :param plot: The plot summary of the movie.
        :param rating: The quality rating of the movie.
        """
        try:
            self.table.put_item(
                Item={
                    "year": year,
                    "title": title,
                    "info": {"plot": plot, "rating": Decimal(str(rating))},
                }
            )
        except ClientError as err:
            logger.error(
                "Couldn't add movie %s to table %s. Here's why: %s: %s",
                title,
                self.table.name,
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise


    def get_movie(self, title, year):
        """
        Gets movie data from the table for a specific movie.

        :param title: The title of the movie.
        :param year: The release year of the movie.
        :return: The data about the requested movie.
        """
        try:
            response = self.table.get_item(Key={"year": year, "title": title})
        except ClientError as err:
            logger.error(
                "Couldn't get movie %s from table %s. Here's why: %s: %s",
                title,
                self.table.name,
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise
        else:
            return response["Item"]


    def update_movie(self, title, year, rating, plot):
        """
        Updates rating and plot data for a movie in the table.

        :param title: The title of the movie to update.
        :param year: The release year of the movie to update.
        :param rating: The updated rating to the give the movie.
        :param plot: The updated plot summary to give the movie.
        :return: The fields that were updated, with their new values.
        """
        try:
            response = self.table.update_item(
                Key={"year": year, "title": title},
                UpdateExpression="set info.rating=:r, info.plot=:p",
                ExpressionAttributeValues={":r": Decimal(str(rating)), ":p": plot},
                ReturnValues="UPDATED_NEW",
            )
        except ClientError as err:
            logger.error(
                "Couldn't update movie %s in table %s. Here's why: %s: %s",
                title,
                self.table.name,
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise
        else:
            return response["Attributes"]


    def query_movies(self, year):
        """
        Queries for movies that were released in the specified year.

        :param year: The year to query.
        :return: The list of movies that were released in the specified year.
        """
        try:
            response = self.table.query(KeyConditionExpression=Key("year").eq(year))
        except ClientError as err:
            logger.error(
                "Couldn't query for movies released in %s. Here's why: %s: %s",
                year,
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise
        else:
            return response["Items"]


    def scan_movies(self, year_range):
        """
        Scans for movies that were released in a range of years.
        Uses a projection expression to return a subset of data for each movie.

        :param year_range: The range of years to retrieve.
        :return: The list of movies released in the specified years.
        """
        movies = []
        scan_kwargs = {
            "FilterExpression": Key("year").between(
                year_range["first"], year_range["second"]
            ),
            "ProjectionExpression": "#yr, title, info.rating",
            "ExpressionAttributeNames": {"#yr": "year"},
        }
        try:
            done = False
            start_key = None
            while not done:
                if start_key:
                    scan_kwargs["ExclusiveStartKey"] = start_key
                response = self.table.scan(**scan_kwargs)
                movies.extend(response.get("Items", []))
                start_key = response.get("LastEvaluatedKey", None)
                done = start_key is None
        except ClientError as err:
            logger.error(
                "Couldn't scan for movies. Here's why: %s: %s",
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise

        return movies


    def delete_movie(self, title, year):
        """
        Deletes a movie from the table.

        :param title: The title of the movie to delete.
        :param year: The release year of the movie to delete.
        """
        try:
            self.table.delete_item(Key={"year": year, "title": title})
        except ClientError as err:
            logger.error(
                "Couldn't delete movie %s. Here's why: %s: %s",
                title,
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise


    def delete_table(self):
        """
        Deletes the table.
        """
        try:
            self.table.delete()
            self.table = None
        except ClientError as err:
            logger.error(
                "Couldn't delete table. Here's why: %s: %s",
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise

Create a helper function to download and extract the sample JSON file.

def get_sample_movie_data(movie_file_name):
    """
    Gets sample movie data, either from a local file or by first downloading it from
    the Amazon DynamoDB developer guide.

    :param movie_file_name: The local file name where the movie data is stored in JSON format.
    :return: The movie data as a dict.
    """
    if not os.path.isfile(movie_file_name):
        print(f"Downloading {movie_file_name}...")
        movie_content = requests.get(
            "https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/samples/moviedata.zip"
        )
        movie_zip = ZipFile(BytesIO(movie_content.content))
        movie_zip.extractall()

    try:
        with open(movie_file_name) as movie_file:
            movie_data = json.load(movie_file, parse_float=Decimal)
    except FileNotFoundError:
        print(
            f"File {movie_file_name} not found. You must first download the file to "
            "run this demo. See the README for instructions."
        )
        raise
    else:
        # The sample file lists over 4000 movies, return only the first 250.
        return movie_data[:250]

Run an interactive scenario to create the table and perform actions on it.

def run_scenario(table_name, movie_file_name, dyn_resource):
    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")

    print("-" * 88)
    print("Welcome to the Amazon DynamoDB getting started demo.")
    print("-" * 88)

    movies = Movies(dyn_resource)
    movies_exists = movies.exists(table_name)
    if not movies_exists:
        print(f"\nCreating table {table_name}...")
        movies.create_table(table_name)
        print(f"\nCreated table {movies.table.name}.")

    my_movie = Question.ask_questions(
        [
            Question(
                "title", "Enter the title of a movie you want to add to the table: "
            ),
            Question("year", "What year was it released? ", Question.is_int),
            Question(
                "rating",
                "On a scale of 1 - 10, how do you rate it? ",
                Question.is_float,
                Question.in_range(1, 10),
            ),
            Question("plot", "Summarize the plot for me: "),
        ]
    )
    movies.add_movie(**my_movie)
    print(f"\nAdded '{my_movie['title']}' to '{movies.table.name}'.")
    print("-" * 88)

    movie_update = Question.ask_questions(
        [
            Question(
                "rating",
                f"\nLet's update your movie.\nYou rated it {my_movie['rating']}, what new "
                f"rating would you give it? ",
                Question.is_float,
                Question.in_range(1, 10),
            ),
            Question(
                "plot",
                f"You summarized the plot as '{my_movie['plot']}'.\nWhat would you say now? ",
            ),
        ]
    )
    my_movie.update(movie_update)
    updated = movies.update_movie(**my_movie)
    print(f"\nUpdated '{my_movie['title']}' with new attributes:")
    pprint(updated)
    print("-" * 88)

    if not movies_exists:
        movie_data = get_sample_movie_data(movie_file_name)
        print(f"\nReading data from '{movie_file_name}' into your table.")
        movies.write_batch(movie_data)
        print(f"\nWrote {len(movie_data)} movies into {movies.table.name}.")
    print("-" * 88)

    title = "The Lord of the Rings: The Fellowship of the Ring"
    if Question.ask_question(
        f"Let's move on...do you want to get info about '{title}'? (y/n) ",
        Question.is_yesno,
    ):
        movie = movies.get_movie(title, 2001)
        print("\nHere's what I found:")
        pprint(movie)
    print("-" * 88)

    ask_for_year = True
    while ask_for_year:
        release_year = Question.ask_question(
            f"\nLet's get a list of movies released in a given year. Enter a year between "
            f"1972 and 2018: ",
            Question.is_int,
            Question.in_range(1972, 2018),
        )
        releases = movies.query_movies(release_year)
        if releases:
            print(f"There were {len(releases)} movies released in {release_year}:")
            for release in releases:
                print(f"\t{release['title']}")
            ask_for_year = False
        else:
            print(f"I don't know about any movies released in {release_year}!")
            ask_for_year = Question.ask_question(
                "Try another year? (y/n) ", Question.is_yesno
            )
    print("-" * 88)

    years = Question.ask_questions(
        [
            Question(
                "first",
                f"\nNow let's scan for movies released in a range of years. Enter a year: ",
                Question.is_int,
                Question.in_range(1972, 2018),
            ),
            Question(
                "second",
                "Now enter another year: ",
                Question.is_int,
                Question.in_range(1972, 2018),
            ),
        ]
    )
    releases = movies.scan_movies(years)
    if releases:
        count = Question.ask_question(
            f"\nFound {len(releases)} movies. How many do you want to see? ",
            Question.is_int,
            Question.in_range(1, len(releases)),
        )
        print(f"\nHere are your {count} movies:\n")
        pprint(releases[:count])
    else:
        print(
            f"I don't know about any movies released between {years['first']} "
            f"and {years['second']}."
        )
    print("-" * 88)

    if Question.ask_question(
        f"\nLet's remove your movie from the table. Do you want to remove "
        f"'{my_movie['title']}'? (y/n)",
        Question.is_yesno,
    ):
        movies.delete_movie(my_movie["title"], my_movie["year"])
        print(f"\nRemoved '{my_movie['title']}' from the table.")
    print("-" * 88)

    if Question.ask_question(f"\nDelete the table? (y/n) ", Question.is_yesno):
        movies.delete_table()
        print(f"Deleted {table_name}.")
    else:
        print(
            "Don't forget to delete the table when you're done or you might incur "
            "charges on your account."
        )

    print("\nThanks for watching!")
    print("-" * 88)


if __name__ == "__main__":
    try:
        run_scenario(
            "doc-example-table-movies", "moviedata.json", boto3.resource("dynamodb")
        )
    except Exception as e:
        print(f"Something went wrong with the demo! Here's what: {e}")

This scenario uses the following helper class to ask questions at a command prompt.

class Question:
    """
    A helper class to ask questions at a command prompt and validate and convert
    the answers.
    """

    def __init__(self, key, question, *validators):
        """
        :param key: The key that is used for storing the answer in a dict, when
                    multiple questions are asked in a set.
        :param question: The question to ask.
        :param validators: The answer is passed through the list of validators until
                           one fails or they all pass. Validators may also convert the
                           answer to another form, such as from a str to an int.
        """
        self.key = key
        self.question = question
        self.validators = Question.non_empty, *validators

    @staticmethod
    def ask_questions(questions):
        """
        Asks a set of questions and stores the answers in a dict.

        :param questions: The list of questions to ask.
        :return: A dict of answers.
        """
        answers = {}
        for question in questions:
            answers[question.key] = Question.ask_question(
                question.question, *question.validators
            )
        return answers

    @staticmethod
    def ask_question(question, *validators):
        """
        Asks a single question and validates it against a list of validators.
        When an answer fails validation, the complaint is printed and the question
        is asked again.

        :param question: The question to ask.
        :param validators: The list of validators that the answer must pass.
        :return: The answer, converted to its final form by the validators.
        """
        answer = None
        while answer is None:
            answer = input(question)
            for validator in validators:
                answer, complaint = validator(answer)
                if answer is None:
                    print(complaint)
                    break
        return answer

    @staticmethod
    def non_empty(answer):
        """
        Validates that the answer is not empty.
        :return: The non-empty answer, or None.
        """
        return answer if answer != "" else None, "I need an answer. Please?"

    @staticmethod
    def is_yesno(answer):
        """
        Validates a yes/no answer.
        :return: True when the answer is 'y'; otherwise, False.
        """
        return answer.lower() == "y", ""

    @staticmethod
    def is_int(answer):
        """
        Validates that the answer can be converted to an int.
        :return: The int answer; otherwise, None.
        """
        try:
            int_answer = int(answer)
        except ValueError:
            int_answer = None
        return int_answer, f"{answer} must be a valid integer."

    @staticmethod
    def is_letter(answer):
        """
        Validates that the answer is a letter.
        :return The letter answer, converted to uppercase; otherwise, None.
        """
        return (
            answer.upper() if answer.isalpha() else None,
            f"{answer} must be a single letter.",
        )

    @staticmethod
    def is_float(answer):
        """
        Validate that the answer can be converted to a float.
        :return The float answer; otherwise, None.
        """
        try:
            float_answer = float(answer)
        except ValueError:
            float_answer = None
        return float_answer, f"{answer} must be a valid float."

    @staticmethod
    def in_range(lower, upper):
        """
        Validate that the answer is within a range. The answer must be of a type that can
        be compared to the lower and upper bounds.
        :return: The answer, if it is within the range; otherwise, None.
        """

        def _validate(answer):
            return (
                answer if lower <= answer <= upper else None,
                f"{answer} must be between {lower} and {upper}.",
            )

        return _validate

class PartiQLBatchWrapper:
    """
    Encapsulates a DynamoDB resource to run PartiQL statements.
    """

    def __init__(self, dyn_resource):
        """
        :param dyn_resource: A Boto3 DynamoDB resource.
        """
        self.dyn_resource = dyn_resource


    def run_partiql(self, statements, param_list):
        """
        Runs a PartiQL statement. A Boto3 resource is used even though
        `execute_statement` is called on the underlying `client` object because the
        resource transforms input and output from plain old Python objects (POPOs) to
        the DynamoDB format. If you create the client directly, you must do these
        transforms yourself.

        :param statements: The batch of PartiQL statements.
        :param param_list: The batch of PartiQL parameters that are associated with
                           each statement. This list must be in the same order as the
                           statements.
        :return: The responses returned from running the statements, if any.
        """
        try:
            output = self.dyn_resource.meta.client.batch_execute_statement(
                Statements=[
                    {"Statement": statement, "Parameters": params}
                    for statement, params in zip(statements, param_list)
                ]
            )
        except ClientError as err:
            if err.response["Error"]["Code"] == "ResourceNotFoundException":
                logger.error(
                    "Couldn't execute batch of PartiQL statements because the table "
                    "does not exist."
                )
            else:
                logger.error(
                    "Couldn't execute batch of PartiQL statements. Here's why: %s: %s",
                    err.response["Error"]["Code"],
                    err.response["Error"]["Message"],
                )
            raise
        else:
            return output

import decimal
import json
import logging
import os
import pprint
import time
import boto3
from botocore.exceptions import ClientError

logger = logging.getLogger(__name__)
dynamodb = boto3.resource("dynamodb")

MAX_GET_SIZE = 100  # Amazon DynamoDB rejects a get batch larger than 100 items.


def do_batch_get(batch_keys):
    """
    Gets a batch of items from Amazon DynamoDB. Batches can contain keys from
    more than one table.

    When Amazon DynamoDB cannot process all items in a batch, a set of unprocessed
    keys is returned. This function uses an exponential backoff algorithm to retry
    getting the unprocessed keys until all are retrieved or the specified
    number of tries is reached.

    :param batch_keys: The set of keys to retrieve. A batch can contain at most 100
                       keys. Otherwise, Amazon DynamoDB returns an error.
    :return: The dictionary of retrieved items grouped under their respective
             table names.
    """
    tries = 0
    max_tries = 5
    sleepy_time = 1  # Start with 1 second of sleep, then exponentially increase.
    retrieved = {key: [] for key in batch_keys}
    while tries < max_tries:
        response = dynamodb.batch_get_item(RequestItems=batch_keys)
        # Collect any retrieved items and retry unprocessed keys.
        for key in response.get("Responses", []):
            retrieved[key] += response["Responses"][key]
        unprocessed = response["UnprocessedKeys"]
        if len(unprocessed) > 0:
            batch_keys = unprocessed
            unprocessed_count = sum(
                [len(batch_key["Keys"]) for batch_key in batch_keys.values()]
            )
            logger.info(
                "%s unprocessed keys returned. Sleep, then retry.", unprocessed_count
            )
            tries += 1
            if tries < max_tries:
                logger.info("Sleeping for %s seconds.", sleepy_time)
                time.sleep(sleepy_time)
                sleepy_time = min(sleepy_time * 2, 32)
        else:
            break

    return retrieved

class Movies:
    """Encapsulates an Amazon DynamoDB table of movie data.

    Example data structure for a movie record in this table:
        {
            "year": 1999,
            "title": "For Love of the Game",
            "info": {
                "directors": ["Sam Raimi"],
                "release_date": "1999-09-15T00:00:00Z",
                "rating": 6.3,
                "plot": "A washed up pitcher flashes through his career.",
                "rank": 4987,
                "running_time_secs": 8220,
                "actors": [
                    "Kevin Costner",
                    "Kelly Preston",
                    "John C. Reilly"
                ]
            }
        }
    """

    def __init__(self, dyn_resource):
        """
        :param dyn_resource: A Boto3 DynamoDB resource.
        """
        self.dyn_resource = dyn_resource
        # The table variable is set during the scenario in the call to
        # 'exists' if the table exists. Otherwise, it is set by 'create_table'.
        self.table = None


    def write_batch(self, movies):
        """
        Fills an Amazon DynamoDB table with the specified data, using the Boto3
        Table.batch_writer() function to put the items in the table.
        Inside the context manager, Table.batch_writer builds a list of
        requests. On exiting the context manager, Table.batch_writer starts sending
        batches of write requests to Amazon DynamoDB and automatically
        handles chunking, buffering, and retrying.

        :param movies: The data to put in the table. Each item must contain at least
                       the keys required by the schema that was specified when the
                       table was created.
        """
        try:
            with self.table.batch_writer() as writer:
                for movie in movies:
                    writer.put_item(Item=movie)
        except ClientError as err:
            logger.error(
                "Couldn't load data into table %s. Here's why: %s: %s",
                self.table.name,
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise

Create a table for storing movie data.

class Movies:
    """Encapsulates an Amazon DynamoDB table of movie data.

    Example data structure for a movie record in this table:
        {
            "year": 1999,
            "title": "For Love of the Game",
            "info": {
                "directors": ["Sam Raimi"],
                "release_date": "1999-09-15T00:00:00Z",
                "rating": 6.3,
                "plot": "A washed up pitcher flashes through his career.",
                "rank": 4987,
                "running_time_secs": 8220,
                "actors": [
                    "Kevin Costner",
                    "Kelly Preston",
                    "John C. Reilly"
                ]
            }
        }
    """

    def __init__(self, dyn_resource):
        """
        :param dyn_resource: A Boto3 DynamoDB resource.
        """
        self.dyn_resource = dyn_resource
        # The table variable is set during the scenario in the call to
        # 'exists' if the table exists. Otherwise, it is set by 'create_table'.
        self.table = None


    def create_table(self, table_name):
        """
        Creates an Amazon DynamoDB table that can be used to store movie data.
        The table uses the release year of the movie as the partition key and the
        title as the sort key.

        :param table_name: The name of the table to create.
        :return: The newly created table.
        """
        try:
            self.table = self.dyn_resource.create_table(
                TableName=table_name,
                KeySchema=[
                    {"AttributeName": "year", "KeyType": "HASH"},  # Partition key
                    {"AttributeName": "title", "KeyType": "RANGE"},  # Sort key
                ],
                AttributeDefinitions=[
                    {"AttributeName": "year", "AttributeType": "N"},
                    {"AttributeName": "title", "AttributeType": "S"},
                ],
                BillingMode='PAY_PER_REQUEST',
            )
            self.table.wait_until_exists()
        except ClientError as err:
            logger.error(
                "Couldn't create table %s. Here's why: %s: %s",
                table_name,
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise
        else:
            return self.table

class Movies:
    """Encapsulates an Amazon DynamoDB table of movie data.

    Example data structure for a movie record in this table:
        {
            "year": 1999,
            "title": "For Love of the Game",
            "info": {
                "directors": ["Sam Raimi"],
                "release_date": "1999-09-15T00:00:00Z",
                "rating": 6.3,
                "plot": "A washed up pitcher flashes through his career.",
                "rank": 4987,
                "running_time_secs": 8220,
                "actors": [
                    "Kevin Costner",
                    "Kelly Preston",
                    "John C. Reilly"
                ]
            }
        }
    """

    def __init__(self, dyn_resource):
        """
        :param dyn_resource: A Boto3 DynamoDB resource.
        """
        self.dyn_resource = dyn_resource
        # The table variable is set during the scenario in the call to
        # 'exists' if the table exists. Otherwise, it is set by 'create_table'.
        self.table = None


    def delete_movie(self, title, year):
        """
        Deletes a movie from the table.

        :param title: The title of the movie to delete.
        :param year: The release year of the movie to delete.
        """
        try:
            self.table.delete_item(Key={"year": year, "title": title})
        except ClientError as err:
            logger.error(
                "Couldn't delete movie %s. Here's why: %s: %s",
                title,
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise

You can specify a condition so that an item is deleted only when it meets certain criteria.

class UpdateQueryWrapper:
    def __init__(self, table):
        self.table = table


    def delete_underrated_movie(self, title, year, rating):
        """
        Deletes a movie only if it is rated below a specified value. By using a
        condition expression in a delete operation, you can specify that an item is
        deleted only when it meets certain criteria.

        :param title: The title of the movie to delete.
        :param year: The release year of the movie to delete.
        :param rating: The rating threshold to check before deleting the movie.
        """
        try:
            self.table.delete_item(
                Key={"year": year, "title": title},
                ConditionExpression="info.rating <= :val",
                ExpressionAttributeValues={":val": Decimal(str(rating))},
            )
        except ClientError as err:
            if err.response["Error"]["Code"] == "ConditionalCheckFailedException":
                logger.warning(
                    "Didn't delete %s because its rating is greater than %s.",
                    title,
                    rating,
                )
            else:
                logger.error(
                    "Couldn't delete movie %s. Here's why: %s: %s",
                    title,
                    err.response["Error"]["Code"],
                    err.response["Error"]["Message"],
                )
            raise

class Movies:
    """Encapsulates an Amazon DynamoDB table of movie data.

    Example data structure for a movie record in this table:
        {
            "year": 1999,
            "title": "For Love of the Game",
            "info": {
                "directors": ["Sam Raimi"],
                "release_date": "1999-09-15T00:00:00Z",
                "rating": 6.3,
                "plot": "A washed up pitcher flashes through his career.",
                "rank": 4987,
                "running_time_secs": 8220,
                "actors": [
                    "Kevin Costner",
                    "Kelly Preston",
                    "John C. Reilly"
                ]
            }
        }
    """

    def __init__(self, dyn_resource):
        """
        :param dyn_resource: A Boto3 DynamoDB resource.
        """
        self.dyn_resource = dyn_resource
        # The table variable is set during the scenario in the call to
        # 'exists' if the table exists. Otherwise, it is set by 'create_table'.
        self.table = None


    def delete_table(self):
        """
        Deletes the table.
        """
        try:
            self.table.delete()
            self.table = None
        except ClientError as err:
            logger.error(
                "Couldn't delete table. Here's why: %s: %s",
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise

class Movies:
    """Encapsulates an Amazon DynamoDB table of movie data.

    Example data structure for a movie record in this table:
        {
            "year": 1999,
            "title": "For Love of the Game",
            "info": {
                "directors": ["Sam Raimi"],
                "release_date": "1999-09-15T00:00:00Z",
                "rating": 6.3,
                "plot": "A washed up pitcher flashes through his career.",
                "rank": 4987,
                "running_time_secs": 8220,
                "actors": [
                    "Kevin Costner",
                    "Kelly Preston",
                    "John C. Reilly"
                ]
            }
        }
    """

    def __init__(self, dyn_resource):
        """
        :param dyn_resource: A Boto3 DynamoDB resource.
        """
        self.dyn_resource = dyn_resource
        # The table variable is set during the scenario in the call to
        # 'exists' if the table exists. Otherwise, it is set by 'create_table'.
        self.table = None


    def exists(self, table_name):
        """
        Determines whether a table exists. As a side effect, stores the table in
        a member variable.

        :param table_name: The name of the table to check.
        :return: True when the table exists; otherwise, False.
        """
        try:
            table = self.dyn_resource.Table(table_name)
            table.load()
            exists = True
        except ClientError as err:
            if err.response["Error"]["Code"] == "ResourceNotFoundException":
                exists = False
            else:
                logger.error(
                    "Couldn't check for existence of %s. Here's why: %s: %s",
                    table_name,
                    err.response["Error"]["Code"],
                    err.response["Error"]["Message"],
                )
                raise
        else:
            self.table = table
        return exists

Describe TTL configuration on an existing DynamoDB table using AWS SDK for Python (Boto3).

import boto3


def describe_ttl(table_name, region):
    """
    Describes TTL on an existing table, as well as a region.

    :param table_name: String representing the name of the table
    :param region: AWS Region of the table - example `us-east-1`
    :return: Time to live description.
    """
    try:
        dynamodb = boto3.resource("dynamodb", region_name=region)
        ttl_description = dynamodb.describe_time_to_live(TableName=table_name)
        print(
            f"TimeToLive for table {table_name} is status {ttl_description['TimeToLiveDescription']['TimeToLiveStatus']}"
        )

        return ttl_description
    except Exception as e:
        print(f"Error describing table: {e}")
        raise


# Enter your own table name and AWS region
describe_ttl("your-table-name", "us-east-1")

class PartiQLWrapper:
    """
    Encapsulates a DynamoDB resource to run PartiQL statements.
    """

    def __init__(self, dyn_resource):
        """
        :param dyn_resource: A Boto3 DynamoDB resource.
        """
        self.dyn_resource = dyn_resource


    def run_partiql(self, statement, params):
        """
        Runs a PartiQL statement. A Boto3 resource is used even though
        `execute_statement` is called on the underlying `client` object because the
        resource transforms input and output from plain old Python objects (POPOs) to
        the DynamoDB format. If you create the client directly, you must do these
        transforms yourself.

        :param statement: The PartiQL statement.
        :param params: The list of PartiQL parameters. These are applied to the
                       statement in the order they are listed.
        :return: The items returned from the statement, if any.
        """
        try:
            output = self.dyn_resource.meta.client.execute_statement(
                Statement=statement, Parameters=params
            )
        except ClientError as err:
            if err.response["Error"]["Code"] == "ResourceNotFoundException":
                logger.error(
                    "Couldn't execute PartiQL '%s' because the table does not exist.",
                    statement,
                )
            else:
                logger.error(
                    "Couldn't execute PartiQL '%s'. Here's why: %s: %s",
                    statement,
                    err.response["Error"]["Code"],
                    err.response["Error"]["Message"],
                )
            raise
        else:
            return output

class Movies:
    """Encapsulates an Amazon DynamoDB table of movie data.

    Example data structure for a movie record in this table:
        {
            "year": 1999,
            "title": "For Love of the Game",
            "info": {
                "directors": ["Sam Raimi"],
                "release_date": "1999-09-15T00:00:00Z",
                "rating": 6.3,
                "plot": "A washed up pitcher flashes through his career.",
                "rank": 4987,
                "running_time_secs": 8220,
                "actors": [
                    "Kevin Costner",
                    "Kelly Preston",
                    "John C. Reilly"
                ]
            }
        }
    """

    def __init__(self, dyn_resource):
        """
        :param dyn_resource: A Boto3 DynamoDB resource.
        """
        self.dyn_resource = dyn_resource
        # The table variable is set during the scenario in the call to
        # 'exists' if the table exists. Otherwise, it is set by 'create_table'.
        self.table = None


    def get_movie(self, title, year):
        """
        Gets movie data from the table for a specific movie.

        :param title: The title of the movie.
        :param year: The release year of the movie.
        :return: The data about the requested movie.
        """
        try:
            response = self.table.get_item(Key={"year": year, "title": title})
        except ClientError as err:
            logger.error(
                "Couldn't get movie %s from table %s. Here's why: %s: %s",
                title,
                self.table.name,
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise
        else:
            return response["Item"]

class Movies:
    """Encapsulates an Amazon DynamoDB table of movie data.

    Example data structure for a movie record in this table:
        {
            "year": 1999,
            "title": "For Love of the Game",
            "info": {
                "directors": ["Sam Raimi"],
                "release_date": "1999-09-15T00:00:00Z",
                "rating": 6.3,
                "plot": "A washed up pitcher flashes through his career.",
                "rank": 4987,
                "running_time_secs": 8220,
                "actors": [
                    "Kevin Costner",
                    "Kelly Preston",
                    "John C. Reilly"
                ]
            }
        }
    """

    def __init__(self, dyn_resource):
        """
        :param dyn_resource: A Boto3 DynamoDB resource.
        """
        self.dyn_resource = dyn_resource
        # The table variable is set during the scenario in the call to
        # 'exists' if the table exists. Otherwise, it is set by 'create_table'.
        self.table = None


    def list_tables(self):
        """
        Lists the Amazon DynamoDB tables for the current account.

        :return: The list of tables.
        """
        try:
            tables = []
            for table in self.dyn_resource.tables.all():
                print(table.name)
                tables.append(table)
        except ClientError as err:
            logger.error(
                "Couldn't list tables. Here's why: %s: %s",
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise
        else:
            return tables

class Movies:
    """Encapsulates an Amazon DynamoDB table of movie data.

    Example data structure for a movie record in this table:
        {
            "year": 1999,
            "title": "For Love of the Game",
            "info": {
                "directors": ["Sam Raimi"],
                "release_date": "1999-09-15T00:00:00Z",
                "rating": 6.3,
                "plot": "A washed up pitcher flashes through his career.",
                "rank": 4987,
                "running_time_secs": 8220,
                "actors": [
                    "Kevin Costner",
                    "Kelly Preston",
                    "John C. Reilly"
                ]
            }
        }
    """

    def __init__(self, dyn_resource):
        """
        :param dyn_resource: A Boto3 DynamoDB resource.
        """
        self.dyn_resource = dyn_resource
        # The table variable is set during the scenario in the call to
        # 'exists' if the table exists. Otherwise, it is set by 'create_table'.
        self.table = None


    def add_movie(self, title, year, plot, rating):
        """
        Adds a movie to the table.

        :param title: The title of the movie.
        :param year: The release year of the movie.
        :param plot: The plot summary of the movie.
        :param rating: The quality rating of the movie.
        """
        try:
            self.table.put_item(
                Item={
                    "year": year,
                    "title": title,
                    "info": {"plot": plot, "rating": Decimal(str(rating))},
                }
            )
        except ClientError as err:
            logger.error(
                "Couldn't add movie %s to table %s. Here's why: %s: %s",
                title,
                self.table.name,
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise

Query items by using a key condition expression.

class Movies:
    """Encapsulates an Amazon DynamoDB table of movie data.

    Example data structure for a movie record in this table:
        {
            "year": 1999,
            "title": "For Love of the Game",
            "info": {
                "directors": ["Sam Raimi"],
                "release_date": "1999-09-15T00:00:00Z",
                "rating": 6.3,
                "plot": "A washed up pitcher flashes through his career.",
                "rank": 4987,
                "running_time_secs": 8220,
                "actors": [
                    "Kevin Costner",
                    "Kelly Preston",
                    "John C. Reilly"
                ]
            }
        }
    """

    def __init__(self, dyn_resource):
        """
        :param dyn_resource: A Boto3 DynamoDB resource.
        """
        self.dyn_resource = dyn_resource
        # The table variable is set during the scenario in the call to
        # 'exists' if the table exists. Otherwise, it is set by 'create_table'.
        self.table = None


    def query_movies(self, year):
        """
        Queries for movies that were released in the specified year.

        :param year: The year to query.
        :return: The list of movies that were released in the specified year.
        """
        try:
            response = self.table.query(KeyConditionExpression=Key("year").eq(year))
        except ClientError as err:
            logger.error(
                "Couldn't query for movies released in %s. Here's why: %s: %s",
                year,
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise
        else:
            return response["Items"]

Query items and project them to return a subset of data.

class UpdateQueryWrapper:
    def __init__(self, table):
        self.table = table


    def query_and_project_movies(self, year, title_bounds):
        """
        Query for movies that were released in a specified year and that have titles
        that start within a range of letters. A projection expression is used
        to return a subset of data for each movie.

        :param year: The release year to query.
        :param title_bounds: The range of starting letters to query.
        :return: The list of movies.
        """
        try:
            response = self.table.query(
                ProjectionExpression="#yr, title, info.genres, info.actors[0]",
                ExpressionAttributeNames={"#yr": "year"},
                KeyConditionExpression=(
                    Key("year").eq(year)
                    & Key("title").between(
                        title_bounds["first"], title_bounds["second"]
                    )
                ),
            )
        except ClientError as err:
            if err.response["Error"]["Code"] == "ValidationException":
                logger.warning(
                    "There's a validation error. Here's the message: %s: %s",
                    err.response["Error"]["Code"],
                    err.response["Error"]["Message"],
                )
            else:
                logger.error(
                    "Couldn't query for movies. Here's why: %s: %s",
                    err.response["Error"]["Code"],
                    err.response["Error"]["Message"],
                )
                raise
        else:
            return response["Items"]

class Movies:
    """Encapsulates an Amazon DynamoDB table of movie data.

    Example data structure for a movie record in this table:
        {
            "year": 1999,
            "title": "For Love of the Game",
            "info": {
                "directors": ["Sam Raimi"],
                "release_date": "1999-09-15T00:00:00Z",
                "rating": 6.3,
                "plot": "A washed up pitcher flashes through his career.",
                "rank": 4987,
                "running_time_secs": 8220,
                "actors": [
                    "Kevin Costner",
                    "Kelly Preston",
                    "John C. Reilly"
                ]
            }
        }
    """

    def __init__(self, dyn_resource):
        """
        :param dyn_resource: A Boto3 DynamoDB resource.
        """
        self.dyn_resource = dyn_resource
        # The table variable is set during the scenario in the call to
        # 'exists' if the table exists. Otherwise, it is set by 'create_table'.
        self.table = None


    def scan_movies(self, year_range):
        """
        Scans for movies that were released in a range of years.
        Uses a projection expression to return a subset of data for each movie.

        :param year_range: The range of years to retrieve.
        :return: The list of movies released in the specified years.
        """
        movies = []
        scan_kwargs = {
            "FilterExpression": Key("year").between(
                year_range["first"], year_range["second"]
            ),
            "ProjectionExpression": "#yr, title, info.rating",
            "ExpressionAttributeNames": {"#yr": "year"},
        }
        try:
            done = False
            start_key = None
            while not done:
                if start_key:
                    scan_kwargs["ExclusiveStartKey"] = start_key
                response = self.table.scan(**scan_kwargs)
                movies.extend(response.get("Items", []))
                start_key = response.get("LastEvaluatedKey", None)
                done = start_key is None
        except ClientError as err:
            logger.error(
                "Couldn't scan for movies. Here's why: %s: %s",
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise

        return movies

Update an item by using an update expression.

class Movies:
    """Encapsulates an Amazon DynamoDB table of movie data.

    Example data structure for a movie record in this table:
        {
            "year": 1999,
            "title": "For Love of the Game",
            "info": {
                "directors": ["Sam Raimi"],
                "release_date": "1999-09-15T00:00:00Z",
                "rating": 6.3,
                "plot": "A washed up pitcher flashes through his career.",
                "rank": 4987,
                "running_time_secs": 8220,
                "actors": [
                    "Kevin Costner",
                    "Kelly Preston",
                    "John C. Reilly"
                ]
            }
        }
    """

    def __init__(self, dyn_resource):
        """
        :param dyn_resource: A Boto3 DynamoDB resource.
        """
        self.dyn_resource = dyn_resource
        # The table variable is set during the scenario in the call to
        # 'exists' if the table exists. Otherwise, it is set by 'create_table'.
        self.table = None


    def update_movie(self, title, year, rating, plot):
        """
        Updates rating and plot data for a movie in the table.

        :param title: The title of the movie to update.
        :param year: The release year of the movie to update.
        :param rating: The updated rating to the give the movie.
        :param plot: The updated plot summary to give the movie.
        :return: The fields that were updated, with their new values.
        """
        try:
            response = self.table.update_item(
                Key={"year": year, "title": title},
                UpdateExpression="set info.rating=:r, info.plot=:p",
                ExpressionAttributeValues={":r": Decimal(str(rating)), ":p": plot},
                ReturnValues="UPDATED_NEW",
            )
        except ClientError as err:
            logger.error(
                "Couldn't update movie %s in table %s. Here's why: %s: %s",
                title,
                self.table.name,
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise
        else:
            return response["Attributes"]

Update an item by using an update expression that includes an arithmetic operation.

class UpdateQueryWrapper:
    def __init__(self, table):
        self.table = table


    def update_rating(self, title, year, rating_change):
        """
        Updates the quality rating of a movie in the table by using an arithmetic
        operation in the update expression. By specifying an arithmetic operation,
        you can adjust a value in a single request, rather than first getting its
        value and then setting its new value.

        :param title: The title of the movie to update.
        :param year: The release year of the movie to update.
        :param rating_change: The amount to add to the current rating for the movie.
        :return: The updated rating.
        """
        try:
            response = self.table.update_item(
                Key={"year": year, "title": title},
                UpdateExpression="set info.rating = info.rating + :val",
                ExpressionAttributeValues={":val": Decimal(str(rating_change))},
                ReturnValues="UPDATED_NEW",
            )
        except ClientError as err:
            logger.error(
                "Couldn't update movie %s in table %s. Here's why: %s: %s",
                title,
                self.table.name,
                err.response["Error"]["Code"],
                err.response["Error"]["Message"],
            )
            raise
        else:
            return response["Attributes"]

Update an item only when it meets certain conditions.

class UpdateQueryWrapper:
    def __init__(self, table):
        self.table = table


    def remove_actors(self, title, year, actor_threshold):
        """
        Removes an actor from a movie, but only when the number of actors is greater
        than a specified threshold. If the movie does not list more than the threshold,
        no actors are removed.

        :param title: The title of the movie to update.
        :param year: The release year of the movie to update.
        :param actor_threshold: The threshold of actors to check.
        :return: The movie data after the update.
        """
        try:
            response = self.table.update_item(
                Key={"year": year, "title": title},
                UpdateExpression="remove info.actors[0]",
                ConditionExpression="size(info.actors) > :num",
                ExpressionAttributeValues={":num": actor_threshold},
                ReturnValues="ALL_NEW",
            )
        except ClientError as err:
            if err.response["Error"]["Code"] == "ConditionalCheckFailedException":
                logger.warning(
                    "Didn't update %s because it has fewer than %s actors.",
                    title,
                    actor_threshold + 1,
                )
            else:
                logger.error(
                    "Couldn't update movie %s. Here's why: %s: %s",
                    title,
                    err.response["Error"]["Code"],
                    err.response["Error"]["Message"],
                )
            raise
        else:
            return response["Attributes"]

Enable TTL on an existing DynamoDB table.

import boto3


def enable_ttl(table_name, ttl_attribute_name):
    """
    Enables TTL on DynamoDB table for a given attribute name
        on success, returns a status code of 200
        on error, throws an exception

    :param table_name: Name of the DynamoDB table
    :param ttl_attribute_name: The name of the TTL attribute being provided to the table.
    """
    try:
        dynamodb = boto3.client("dynamodb")

        # Enable TTL on an existing DynamoDB table
        response = dynamodb.update_time_to_live(
            TableName=table_name,
            TimeToLiveSpecification={"Enabled": True, "AttributeName": ttl_attribute_name},
        )

        # In the returned response, check for a successful status code.
        if response["ResponseMetadata"]["HTTPStatusCode"] == 200:
            print("TTL has been enabled successfully.")
        else:
            print(
                f"Failed to enable TTL, status code {response['ResponseMetadata']['HTTPStatusCode']}"
            )
        return response
    except Exception as ex:
        print("Couldn't enable TTL in table %s. Here's why: %s" % (table_name, ex))
        raise


# your values
enable_ttl("your-table-name", "expireAt")

Disable TTL on an existing DynamoDB table.

import boto3


def disable_ttl(table_name, ttl_attribute_name):
    """
    Disables TTL on DynamoDB table for a given attribute name
        on success, returns a status code of 200
        on error, throws an exception

    :param table_name: Name of the DynamoDB table being modified
    :param ttl_attribute_name: The name of the TTL attribute being provided to the table.
    """
    try:
        dynamodb = boto3.client("dynamodb")

        # Enable TTL on an existing DynamoDB table
        response = dynamodb.update_time_to_live(
            TableName=table_name,
            TimeToLiveSpecification={"Enabled": False, "AttributeName": ttl_attribute_name},
        )

        # In the returned response, check for a successful status code.
        if response["ResponseMetadata"]["HTTPStatusCode"] == 200:
            print("TTL has been disabled successfully.")
        else:
            print(
                f"Failed to disable TTL, status code {response['ResponseMetadata']['HTTPStatusCode']}"
            )
    except Exception as ex:
        print("Couldn't disable TTL in table %s. Here's why: %s" % (table_name, ex))
        raise


# your values
disable_ttl("your-table-name", "expireAt")

Create a table with either the DAX or Boto3 client.

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.")

Write test data to the table.

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)

Get items for a number of iterations for both the DAX client and the Boto3 client and report the time spent for each.

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__":
    # 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_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) 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}."
    )

Query the table for a number of iterations for both the DAX client and the Boto3 client and report the time spent for each.

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}."
    )

Scan the table for a number of iterations for both the DAX client and the Boto3 client and report the time spent for each.

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}."
    )

Delete the table.

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!")

Compare multiple values with a single attribute using AWS SDK for Python (Boto3).

import boto3
from boto3.dynamodb.conditions import Attr, Key
from typing import Any, Dict, List, Optional


def compare_multiple_values(
    table_name: str,
    attribute_name: str,
    values_list: List[Any],
    partition_key_name: Optional[str] = None,
    partition_key_value: Optional[str] = None,
) -> Dict[str, Any]:
    """
    Query or scan a DynamoDB table to find items where an attribute matches any value from a list.

    This function demonstrates the use of the IN operator to compare a single attribute
    against multiple possible values, which is more efficient than using multiple OR conditions.

    Args:
        table_name (str): The name of the DynamoDB table.
        attribute_name (str): The name of the attribute to compare against the values list.
        values_list (List[Any]): List of values to compare the attribute against.
        partition_key_name (Optional[str]): The name of the partition key attribute for query operations.
        partition_key_value (Optional[str]): The value of the partition key to query.

    Returns:
        Dict[str, Any]: The response from DynamoDB containing the matching items.
    """
    # Initialize the DynamoDB resource
    dynamodb = boto3.resource("dynamodb")
    table = dynamodb.Table(table_name)

    # Create the filter expression using the is_in method
    filter_expression = Attr(attribute_name).is_in(values_list)

    # If partition key is provided, perform a query operation
    if partition_key_name and partition_key_value:
        key_condition = Key(partition_key_name).eq(partition_key_value)
        response = table.query(
            KeyConditionExpression=key_condition, FilterExpression=filter_expression
        )
    else:
        # Otherwise, perform a scan operation
        response = table.scan(FilterExpression=filter_expression)

    # Handle pagination if there are more results
    items = response.get("Items", [])
    while "LastEvaluatedKey" in response:
        if partition_key_name and partition_key_value:
            response = table.query(
                KeyConditionExpression=key_condition,
                FilterExpression=filter_expression,
                ExclusiveStartKey=response["LastEvaluatedKey"],
            )
        else:
            response = table.scan(
                FilterExpression=filter_expression, ExclusiveStartKey=response["LastEvaluatedKey"]
            )
        items.extend(response.get("Items", []))

    # Return the complete result
    return {"Items": items, "Count": len(items)}


def compare_with_or_conditions(
    table_name: str,
    attribute_name: str,
    values_list: List[Any],
    partition_key_name: Optional[str] = None,
    partition_key_value: Optional[str] = None,
) -> Dict[str, Any]:
    """
    Alternative implementation using multiple OR conditions instead of the IN operator.

    This function is provided for comparison to show why using the IN operator is preferable.
    With many values, this approach becomes verbose and less efficient.

    Args:
        table_name (str): The name of the DynamoDB table.
        attribute_name (str): The name of the attribute to compare against the values list.
        values_list (List[Any]): List of values to compare the attribute against.
        partition_key_name (Optional[str]): The name of the partition key attribute for query operations.
        partition_key_value (Optional[str]): The value of the partition key to query.

    Returns:
        Dict[str, Any]: The response from DynamoDB containing the matching items.
    """
    # Initialize the DynamoDB resource
    dynamodb = boto3.resource("dynamodb")
    table = dynamodb.Table(table_name)

    # Create a filter expression with multiple OR conditions
    filter_expression = None
    for value in values_list:
        condition = Attr(attribute_name).eq(value)
        if filter_expression is None:
            filter_expression = condition
        else:
            filter_expression = filter_expression | condition

    # If partition key is provided, perform a query operation
    if partition_key_name and partition_key_value and filter_expression:
        key_condition = Key(partition_key_name).eq(partition_key_value)
        response = table.query(
            KeyConditionExpression=key_condition, FilterExpression=filter_expression
        )
    elif filter_expression:
        # Otherwise, perform a scan operation
        response = table.scan(FilterExpression=filter_expression)
    else:
        # Return empty response if no values provided
        return {"Items": [], "Count": 0}

    # Handle pagination if there are more results
    items = response.get("Items", [])
    while "LastEvaluatedKey" in response:
        if partition_key_name and partition_key_value:
            response = table.query(
                KeyConditionExpression=key_condition,
                FilterExpression=filter_expression,
                ExclusiveStartKey=response["LastEvaluatedKey"],
            )
        else:
            response = table.scan(
                FilterExpression=filter_expression, ExclusiveStartKey=response["LastEvaluatedKey"]
            )
        items.extend(response.get("Items", []))

    # Return the complete result
    return {"Items": items, "Count": len(items)}

Example usage of comparing multiple values with AWS SDK for Python (Boto3).

def example_usage():
    """Example of how to use the compare_multiple_values function."""
    # Example parameters
    table_name = "Products"
    attribute_name = "Category"
    values_list = ["Electronics", "Computers", "Accessories"]

    print(f"Searching for products in any of these categories: {values_list}")

    # Using the IN operator (recommended approach)
    print("\nApproach 1: Using the IN operator")
    response = compare_multiple_values(
        table_name=table_name, attribute_name=attribute_name, values_list=values_list
    )

    print(f"Found {response['Count']} products in the specified categories")

    # Using multiple OR conditions (alternative approach)
    print("\nApproach 2: Using multiple OR conditions")
    response2 = compare_with_or_conditions(
        table_name=table_name, attribute_name=attribute_name, values_list=values_list
    )

    print(f"Found {response2['Count']} products in the specified categories")

    # Example with a query operation
    print("\nQuerying a specific manufacturer's products in multiple categories")
    partition_key_name = "Manufacturer"
    partition_key_value = "Acme"

    response3 = compare_multiple_values(
        table_name=table_name,
        attribute_name=attribute_name,
        values_list=values_list,
        partition_key_name=partition_key_name,
        partition_key_value=partition_key_value,
    )

    print(f"Found {response3['Count']} Acme products in the specified categories")

    # Explain the benefits of using the IN operator
    print("\nBenefits of using the IN operator:")
    print("1. More concise expression compared to multiple OR conditions")
    print("2. Better readability and maintainability")
    print("3. Potentially better performance with large value lists")
    print("4. Simpler code that's less prone to errors")
    print("5. Easier to modify when adding or removing values")

Update TTL on on an existing DynamoDB Item in a table, with a condition.

from datetime import datetime, timedelta

import boto3
from botocore.exceptions import ClientError


def update_dynamodb_item_ttl(table_name, region, primary_key, sort_key, ttl_attribute):
    """
    Updates an existing record in a DynamoDB table with a new or updated TTL attribute.

    :param table_name: Name of the DynamoDB table
    :param region: AWS Region of the table - example `us-east-1`
    :param primary_key: one attribute known as the partition key.
    :param sort_key: Also known as a range attribute.
    :param ttl_attribute: name of the TTL attribute in the target DynamoDB table
    :return:
    """
    try:
        dynamodb = boto3.resource("dynamodb", region_name=region)
        table = dynamodb.Table(table_name)

        # Generate updated TTL in epoch second format
        updated_expiration_time = int((datetime.now() + timedelta(days=90)).timestamp())

        # Define the update expression for adding/updating a new attribute
        update_expression = "SET newAttribute = :val1"

        # Define the condition expression for checking if 'expireAt' is not expired
        condition_expression = "expireAt > :val2"

        # Define the expression attribute values
        expression_attribute_values = {":val1": ttl_attribute, ":val2": updated_expiration_time}

        response = table.update_item(
            Key={"primaryKey": primary_key, "sortKey": sort_key},
            UpdateExpression=update_expression,
            ConditionExpression=condition_expression,
            ExpressionAttributeValues=expression_attribute_values,
        )

        print("Item updated successfully.")
        return response["ResponseMetadata"]["HTTPStatusCode"]  # Ideally a 200 OK
    except ClientError as e:
        if e.response["Error"]["Code"] == "ConditionalCheckFailedException":
            print("Condition check failed: Item's 'expireAt' is expired.")
        else:
            print(f"Error updating item: {e}")
    except Exception as e:
        print(f"Error updating item: {e}")


# replace with your values
update_dynamodb_item_ttl(
    "your-table-name",
    "us-east-1",
    "your-partition-key-value",
    "your-sort-key-value",
    "your-ttl-attribute-value",
)

Demonstrate expression operator counting using AWS SDK for Python (Boto3).

import boto3
from botocore.exceptions import ClientError
from typing import Any, Dict, List, Optional, Tuple



def create_complex_filter_expression(
    attribute_name: str, values: List[Any], use_or: bool = True
) -> Tuple[str, Dict[str, Any], Dict[str, str], int]:
    """
    Create a complex filter expression with multiple conditions.

    This function demonstrates how to build a complex filter expression
    and count the number of operators used.

    Args:
        attribute_name (str): The name of the attribute to filter on.
        values (List[Any]): List of values to compare against.
        use_or (bool, optional): Whether to use OR between conditions. Defaults to True.

    Returns:
        Tuple[str, Dict[str, Any], Dict[str, str], int]: A tuple containing:
            - The filter expression string
            - Expression attribute values
            - Expression attribute names
            - The number of operators used
    """
    if not values:
        return "", {}, {}, 0

    # Initialize expression components
    filter_expression = ""
    expression_attribute_values = {}
    expression_attribute_names = {"#attr": attribute_name}
    operator_count = 0

    # Build the filter expression
    for i, value in enumerate(values):
        value_placeholder = f":val{i}"
        expression_attribute_values[value_placeholder] = value

        if i > 0:
            # Add OR or AND operator between conditions
            filter_expression += " OR " if use_or else " AND "
            operator_count += 1  # Count the OR/AND operator

        # Add the condition
        filter_expression += f"#attr = {value_placeholder}"
        operator_count += 1  # Count the = operator

    return (
        filter_expression,
        expression_attribute_values,
        expression_attribute_names,
        operator_count,
    )


def create_nested_filter_expression(
    depth: int, conditions_per_level: int
) -> Tuple[str, Dict[str, Any], Dict[str, str], int]:
    """
    Create a deeply nested filter expression with multiple conditions.

    This function demonstrates how to build a complex nested filter expression
    and count the number of operators used.

    Args:
        depth (int): The depth of nesting.
        conditions_per_level (int): Number of conditions at each level.

    Returns:
        Tuple[str, Dict[str, Any], Dict[str, str], int]: A tuple containing:
            - The filter expression string
            - Expression attribute values
            - Expression attribute names
            - The number of operators used
    """
    if depth <= 0 or conditions_per_level <= 0:
        return "", {}, {}, 0

    # Initialize expression components
    expression_attribute_values = {}
    expression_attribute_names = {}
    operator_count = 0

    def build_nested_expression(current_depth: int, prefix: str) -> str:
        nonlocal operator_count

        if current_depth <= 0:
            return ""

        # Build conditions at this level
        conditions = []
        for i in range(conditions_per_level):
            attr_name = f"attr{prefix}_{i}"
            attr_placeholder = f"#attr{prefix}_{i}"
            val_placeholder = f":val{prefix}_{i}"

            expression_attribute_names[attr_placeholder] = attr_name
            expression_attribute_values[val_placeholder] = i

            conditions.append(f"{attr_placeholder} = {val_placeholder}")
            operator_count += 1  # Count the = operator

        # Join conditions with AND
        level_expression = " AND ".join(conditions)
        operator_count += max(0, len(conditions) - 1)  # Count the AND operators

        # If not at the deepest level, add nested expressions
        if current_depth > 1:
            nested_expr = build_nested_expression(current_depth - 1, f"{prefix}_{current_depth}")
            if nested_expr:
                level_expression = f"({level_expression}) OR ({nested_expr})"
                operator_count += 1  # Count the OR operator

        return level_expression

    # Build the expression starting from the top level
    filter_expression = build_nested_expression(depth, "1")

    return (
        filter_expression,
        expression_attribute_values,
        expression_attribute_names,
        operator_count,
    )


def count_operators_in_update_expression(update_expression: str) -> int:
    """
    Count the number of operators in an update expression.

    This function demonstrates how to count operators in an update expression
    based on DynamoDB's rules.

    Args:
        update_expression (str): The update expression to analyze.

    Returns:
        int: The number of operators in the expression.
    """
    operator_count = 0

    # Count SET operations
    if "SET" in update_expression:
        set_section = (
            update_expression.split("SET")[1].split("REMOVE")[0].split("ADD")[0].split("DELETE")[0]
        )

        # Count assignment operators (=)
        operator_count += set_section.count("=")

        # Count arithmetic operators (+, -)
        operator_count += set_section.count("+")
        operator_count += set_section.count("-")

        # Count list_append function calls (each counts as 1 operator)
        operator_count += set_section.lower().count("list_append")

        # Count if_not_exists function calls (each counts as 1 operator)
        operator_count += set_section.lower().count("if_not_exists")

    # Count REMOVE operations (no additional operators)

    # Count ADD operations (each ADD counts as 1 operator)
    if "ADD" in update_expression:
        add_section = (
            update_expression.split("ADD")[1].split("DELETE")[0].split("SET")[0].split("REMOVE")[0]
        )
        operator_count += add_section.count(",") + 1

    # Count DELETE operations (each DELETE counts as 1 operator)
    if "DELETE" in update_expression:
        delete_section = (
            update_expression.split("DELETE")[1].split("SET")[0].split("ADD")[0].split("REMOVE")[0]
        )
        operator_count += delete_section.count(",") + 1

    return operator_count


def count_operators_in_condition_expression(condition_expression: str) -> int:
    """
    Count the number of operators in a condition expression.

    This function demonstrates how to count operators in a condition expression
    based on DynamoDB's rules.

    Args:
        condition_expression (str): The condition expression to analyze.

    Returns:
        int: The number of operators in the expression.
    """
    operator_count = 0

    # Count comparison operators
    comparison_operators = ["=", "<>", "<", "<=", ">", ">="]
    for op in comparison_operators:
        operator_count += condition_expression.count(op)

    # Count logical operators
    operator_count += condition_expression.upper().count(" AND ")
    operator_count += condition_expression.upper().count(" OR ")
    operator_count += condition_expression.upper().count("NOT ")

    # Count BETWEEN operator (counts as 2: BETWEEN + AND)
    between_count = condition_expression.upper().count(" BETWEEN ")
    operator_count += between_count * 2

    # Count IN operator (counts as 1 regardless of number of values)
    operator_count += condition_expression.upper().count(" IN ")

    # Count functions (each counts as 1 operator)
    functions = [
        "attribute_exists",
        "attribute_not_exists",
        "attribute_type",
        "begins_with",
        "contains",
        "size",
    ]
    for func in functions:
        operator_count += condition_expression.lower().count(func)

    return operator_count


# Note: This function is for demonstration purposes only and should be called from example_usage()
# It's not meant to be used directly as a test function
def _test_expression_limit(
    table_name: str, key: Dict[str, Any], operator_count: int, attribute_name: str = "TestAttribute"
) -> Tuple[bool, Optional[str]]:
    """
    Test if an expression with a specific number of operators exceeds the limit.

    This function demonstrates how to test the 300 operator limit by creating
    an expression with a specified number of operators.

    Args:
        table_name (str): The name of the DynamoDB table.
        key (Dict[str, Any]): The primary key of the item to update.
        operator_count (int): The number of operators to include in the expression.
        attribute_name (str, optional): The name of the attribute to update. Defaults to "TestAttribute".

    Returns:
        Tuple[bool, Optional[str]]: A tuple containing:
            - A boolean indicating if the operation succeeded
            - The error message if it failed, None otherwise
    """
    # Initialize the DynamoDB resource
    dynamodb = boto3.resource("dynamodb")
    table = dynamodb.Table(table_name)

    # Create an update expression with the specified number of operators
    update_expression = f"SET #{attribute_name} = :val0"
    expression_attribute_names = {f"#{attribute_name}": attribute_name}
    expression_attribute_values = {":val0": 0}

    # Add additional SET operations to reach the desired operator count
    # Each assignment adds 1 operator
    for i in range(1, operator_count):
        attr_name = f"{attribute_name}{i}"
        attr_placeholder = f"#attr{i}"
        val_placeholder = f":val{i}"

        update_expression += f", {attr_placeholder} = {val_placeholder}"
        expression_attribute_names[attr_placeholder] = attr_name
        expression_attribute_values[val_placeholder] = i

    try:
        # Attempt the update operation
        table.update_item(
            Key=key,
            UpdateExpression=update_expression,
            ExpressionAttributeNames=expression_attribute_names,
            ExpressionAttributeValues=expression_attribute_values,
        )
        return True, None
    except ClientError as e:
        error_message = e.response["Error"]["Message"]

        if "expression contains too many operators" in error_message.lower():
            return False, error_message
        else:
            # Other error occurred
            raise

Example usage of expression operator counting with AWS SDK for Python (Boto3).

def example_usage():
    """Example of how to use the expression operator counting functions."""

    print("Example 1: Creating a complex filter expression with multiple conditions")
    attribute_name = "Status"
    values = ["Active", "Pending", "Processing", "Shipped", "Delivered"]

    filter_expr, expr_attr_vals, expr_attr_names, op_count = create_complex_filter_expression(
        attribute_name=attribute_name, values=values, use_or=True
    )

    print(f"Filter Expression: {filter_expr}")
    print(f"Expression Attribute Values: {expr_attr_vals}")
    print(f"Expression Attribute Names: {expr_attr_names}")
    print(f"Operator Count: {op_count}")

    print("\nExample 2: Creating a nested filter expression")
    nested_expr, nested_vals, nested_names, nested_count = create_nested_filter_expression(
        depth=3, conditions_per_level=2
    )

    print(f"Nested Filter Expression: {nested_expr}")
    print(f"Operator Count: {nested_count}")

    print("\nExample 3: Counting operators in an update expression")
    update_expression = "SET #name = :name, #age = :age + :increment, #address.#city = :city, #status = if_not_exists(#status, :default_status) REMOVE #old_field ADD #counter :value DELETE #set_attr :set_val"
    update_op_count = count_operators_in_update_expression(update_expression)

    print(f"Update Expression: {update_expression}")
    print(f"Operator Count: {update_op_count}")

    print("\nExample 4: Counting operators in a condition expression")
    condition_expression = "(#status = :active OR #status = :pending) AND #price BETWEEN :min_price AND :max_price AND attribute_exists(#category) AND NOT (#stock <= :min_stock)"
    condition_op_count = count_operators_in_condition_expression(condition_expression)

    print(f"Condition Expression: {condition_expression}")
    print(f"Operator Count: {condition_op_count}")

    print("\nExample 5: Testing the 300 operator limit")

    # This is just for demonstration - in a real application, you would use your actual table
    # Note: This function is renamed to _test_expression_limit to avoid pytest trying to run it
    print("In a real application, you would test with _test_expression_limit function")
    print("Expression with 250 operators would be under the limit")
    print("Expression with 350 operators would exceed the 300 operator limit")

    print("\nOperator Counting Rules in DynamoDB:")
    print("1. Comparison Operators (=, <>, <, <=, >, >=): 1 operator each")
    print("2. Logical Operators (AND, OR, NOT): 1 operator each")
    print("3. BETWEEN: 2 operators (BETWEEN + AND)")
    print("4. IN: 1 operator (regardless of number of values)")
    print("5. Functions (attribute_exists, begins_with, etc.): 1 operator each")
    print("6. Arithmetic Operators (+, -): 1 operator each")
    print("7. SET assignments (=): 1 operator each")
    print("8. ADD and DELETE operations: 1 operator each")

    print("\nStrategies for Working Within the 300 Operator Limit:")
    print("1. Break operations into multiple requests")
    print("2. Use DynamoDB Transactions for complex operations")
    print("3. Optimize data model to reduce query complexity")
    print("4. Use application-side filtering for less critical filters")
    print("5. Consider using IN operator instead of multiple OR conditions")

Shows how to use AWS Chalice with the AWS SDK for Python (Boto3) to create a serverless REST API that uses Amazon API Gateway, AWS Lambda, and Amazon DynamoDB. The REST API simulates a system that tracks daily cases of COVID-19 in the United States, using fictional data. Learn how to:

For complete source code and instructions on how to set up and run, see the full example on GitHub.