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Created March 6, 2024 15:30
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Question :

you are aws python developer using AWS who is interested in speed, performance, and memory optimization. you have been given a file with 366,000 distinct account names, sorted alphabetically. the files is 10mb total. you have a front end website that needs to auto-complete on a text field, using a string contains filter. there should be a lambda web api that returns data to the front end. you can use lambda, s3, dynamodb, in memory cache, and browser side cache to solve this problem. describe several options to solve this problem.

Answer :

Solving this problem involves efficiently handling a large dataset for auto-completion with constraints on speed, performance, and memory optimization. Here are several approaches using AWS services:

1. Lambda with S3 and In-Memory Cache

  • Data Storage: Store the sorted account names file in S3. Given its size (10MB), it's feasible to read it directly in a Lambda function if needed.
  • Processing: Use AWS Lambda to handle the API requests. The Lambda function would load the file from S3 into memory upon initialization (not on every invocation, to save time and resources).
  • Caching: Implement an in-memory cache within the Lambda environment, such as using a global variable to hold frequently accessed data. Since Lambda functions can reuse the environment for multiple invocations, this cache could reduce the need to read from S3 for every request. However, note that this is ephemeral and depends on the Lambda container's lifecycle.
  • Search Optimization: For the string contains filter, considering the dataset is sorted, you could optimize the search using efficient algorithms (like binary search for prefix matches and then filtering for contains), though this approach is more suitable for prefix matching than substring searching.

2. DynamoDB with DAX (DynamoDB Accelerator)

  • Data Storage: Use DynamoDB to store the account names, taking advantage of its fast, key-value access pattern. You might structure the table with account names as the key.
  • Autocomplete Logic: Implement a scanning or querying mechanism that leverages DynamoDB's ability to filter records based on string matching. This would require careful consideration of read throughput and data modeling to ensure cost-effectiveness and performance.
  • Caching: Use DynamoDB Accelerator (DAX) for in-memory caching to significantly speed up read operations, especially useful for auto-complete features where the same queries are frequently executed.
  • Serverless Integration: AWS Lambda functions can be triggered to query DynamoDB and return results to the front-end, with DAX reducing the latency for repeated queries.

3. Elasticache for Autocomplete

  • Pre-processing: Process the dataset to generate potential autocomplete strings and store them in a Redis cluster managed by Amazon ElastiCache. This involves creating a suitable structure (like sorted sets) that can efficiently support the "contains" string operation.
  • Data Storage: Use S3 for storing the original dataset, and Lambda to load and preprocess this data into ElastiCache as needed.
  • Caching and Performance: Redis provides an efficient mechanism for autocomplete features through its in-memory data store, which can handle high throughput and low-latency access patterns.
  • API Gateway and Lambda: Use API Gateway to expose a RESTful endpoint that triggers a Lambda function. This Lambda function queries the Redis cluster to retrieve autocomplete suggestions and returns them to the front-end.

4. Browser-Side Cache

  • Initial Load: On the first visit, the front-end can call the Lambda function to fetch a subset of the dataset or specific entries based on user input, and store this data in the browser's local storage or session storage for quick access.
  • Cache Usage: Subsequent autocomplete queries can first check the browser's cache and only query the backend if the data is not found or is considered stale.

5. Incremental Data Loading

  • Data Segmentation: Segment the data alphabetically or according to another logical division and store these segments in S3.
  • Dynamic Loading: The front-end requests data from Lambda, which then loads only the relevant segment(s) from S3 based on the user's input. This reduces the amount of data to be processed and transferred over the network.

Choosing the Right Approach

The best solution depends on the specific requirements regarding latency, cost, data update frequency, and architectural complexity. For instance, the DynamoDB with DAX approach provides a balance between speed and scalability, with caching to handle frequent access patterns efficiently. Elasticache offers high performance for reads and complex querying capabilities, while the Lambda with S3 and in-memory cache is simpler but might face scalability limits. Browser-side caching reduces backend load but has limitations based on the client's storage capacity and privacy considerations. Incremental data loading minimizes initial load times but requires efficient backend logic to manage data segmentation.

Question :

can you please write python code for the first solution?

Answer :

Certainly! The first solution involves using AWS Lambda to serve autocomplete requests, with the data stored in an Amazon S3 bucket and leveraging in-memory caching within the Lambda function to optimize performance. Here’s how you can implement this solution:

  1. Store the Account Names in S3: Ensure your account names file is uploaded to an S3 bucket.
  2. Create a Lambda Function: This function will read the account names from S3 into memory on initialization, and then use this in-memory data for autocomplete queries.

Step 1: AWS Lambda Function Setup

Here's a Python example that demonstrates how to set up the Lambda function. This code assumes you have an AWS account and the AWS CLI configured with the necessary permissions.

import os
import boto3
import json

# Initialize S3 client outside of the handler to take advantage of container reuse
s3 = boto3.client('s3')

# Assuming the environment variables are set for the bucket and file name
BUCKET_NAME = os.environ['S3_BUCKET_NAME']
FILE_NAME = os.environ['S3_FILE_NAME']

# In-memory cache: This will be populated at the first invocation
# and reused in subsequent invocations if the Lambda container is reused
in_memory_cache = []

def load_data_from_s3():
    global in_memory_cache
    if not in_memory_cache:  # Load data if cache is empty
        response = s3.get_object(Bucket=BUCKET_NAME, Key=FILE_NAME)
        file_content = response['Body'].read().decode('utf-8')
        in_memory_cache = file_content.splitlines()  # Assuming each account name is on a new line

def autocomplete(query):
    # Simple case-insensitive filter for demonstration. This can be optimized.
    return [account for account in in_memory_cache if query.lower() in account.lower()]

def lambda_handler(event, context):
    # Load data from S3 into the in-memory cache if it's not already loaded
    load_data_from_s3()
    
    # Extract query string from the event. This depends on how the API Gateway is configured.
    query = event.get('queryStringParameters', {}).get('query', '')
    
    if not query:
        return {
            'statusCode': 400,
            'body': json.dumps({'message': 'Query parameter is required'})
        }
    
    results = autocomplete(query)
    
    return {
        'statusCode': 200,
        'body': json.dumps(results)
    }

Key Points:

  • Initialization: The S3 client and the environment variables for the S3 bucket name and file name are initialized outside the handler function. This approach utilizes Lambda's execution context reuse to minimize initialization time in subsequent invocations.
  • In-Memory Cache: The global variable in_memory_cache is used to store the account names after the first load, reducing the need to fetch the file from S3 on subsequent invocations.
  • Autocomplete Function: The autocomplete function demonstrates a basic string contains search. This is a simplified example; depending on your use case, you might need a more sophisticated search algorithm for performance and functionality.
  • Lambda Handler: This function is the entry point for the Lambda invocation. It loads the data from S3 into the in-memory cache if it's not already loaded, processes the query parameter, and returns the autocomplete results.

Deployment:

To deploy this solution, you would:

  1. Package your Lambda function, including any dependencies.
  2. Create an AWS Lambda function in the AWS Management Console, AWS CLI, or through the AWS SDKs.
  3. Set the S3_BUCKET_NAME and S3_FILE_NAME environment variables in your Lambda function configuration.
  4. Configure an API Gateway to trigger your Lambda function and pass in the query parameters.

This is a basic implementation to get you started. Depending on your needs, consider error handling, optimizing the search algorithm, and adjusting the Lambda function's memory and timeout settings for better performance.

Question :

this looks great can you add a bit of code to the beginning of the load_data_from_s3 function to update the distinct list of accounts using a query against a dynamodb table using a GSI on timestamp to add new accounts since the last time the search was run? this should use a file on s3 bucket to store the last timestamp that the file was updated and compare that against the GSI. the file on s3 with distinct accounts should be overwritten on s3 with a new timestamp file.

Answer :

To extend the provided load_data_from_s3 function with the capability to update the distinct list of accounts using a query against a DynamoDB table, we'll add steps to:

  1. Query a DynamoDB table using a Global Secondary Index (GSI) on a timestamp attribute to find new accounts added since the last update.
  2. Update the in-memory list of accounts with these new accounts.
  3. Overwrite the original file on S3 with the updated list and update the timestamp file to reflect the latest update.

We will assume that the DynamoDB table has a GSI on a timestamp attribute, and there's a separate file in the S3 bucket to store the last timestamp when the update was performed.

Here's how you can modify the load_data_from_s3 function:

import boto3
from datetime import datetime
import json

# Initialize DynamoDB client
dynamodb = boto3.client('dynamodb')
TABLE_NAME = 'YourDynamoDBTableName'  # replace with your table name
GSI_NAME = 'YourGSIName'  # replace with your GSI name

def update_account_list_with_dynamodb(last_update):
    # Prepare the query to find new accounts since the last update
    response = dynamodb.query(
        TableName=TABLE_NAME,
        IndexName=GSI_NAME,
        KeyConditionExpression='timestamp > :last_update',
        ExpressionAttributeValues={
            ':last_update': {'S': last_update}
        },
        ProjectionExpression='accountName',  # Assuming 'accountName' holds the account names
    )
    # Extract account names from the query response
    new_accounts = [item['accountName']['S'] for item in response['Items']]
    return new_accounts

def get_last_update_timestamp():
    # Try to fetch the last update timestamp from S3
    try:
        response = s3.get_object(Bucket=BUCKET_NAME, Key='last_update_timestamp.txt')
        last_update = response['Body'].read().decode('utf-8')
    except s3.exceptions.NoSuchKey:
        # If the file does not exist, assume this is the first run
        last_update = '1970-01-01T00:00:00Z'  # Use a default timestamp
    return last_update

def set_last_update_timestamp(timestamp):
    # Update the last update timestamp file on S3
    s3.put_object(Bucket=BUCKET_NAME, Key='last_update_timestamp.txt', Body=timestamp.encode('utf-8'))

def load_data_from_s3():
    global in_memory_cache
    if not in_memory_cache:  # Load data if cache is empty
        # Get the last update timestamp
        last_update = get_last_update_timestamp()
        
        # Fetch new accounts since the last update from DynamoDB
        new_accounts = update_account_list_with_dynamodb(last_update)
        
        # Load the current account list from S3
        response = s3.get_object(Bucket=BUCKET_NAME, Key=FILE_NAME)
        file_content = response['Body'].read().decode('utf-8')
        current_accounts = file_content.splitlines()
        
        # Update the list with new accounts
        updated_accounts = sorted(set(current_accounts + new_accounts))
        
        # Update the in-memory cache
        in_memory_cache = updated_accounts
        
        # Overwrite the S3 file with the updated account list
        s3.put_object(Bucket=BUCKET_NAME, Key=FILE_NAME, Body='\n'.join(updated_accounts).encode('utf-8'))
        
        # Update the last update timestamp
        current_timestamp = datetime.utcnow().isoformat() + 'Z'  # Assuming UTC and appending 'Z' to indicate Zulu time
        set_last_update_timestamp(current_timestamp)

This updated function includes the logic to:

  • Retrieve the last update timestamp from an S3 file.
  • Query DynamoDB for new accounts added since the last update.
  • Combine the new accounts with the existing ones, remove duplicates, and sort them.
  • Update the S3 file with the distinct account list.
  • Update the timestamp file on S3 to reflect the latest update.

This approach ensures that the account list is dynamically updated with new data from DynamoDB, leveraging both S3 and DynamoDB's capabilities to maintain an efficient and up-to-date autocomplete feature. Remember to replace placeholders like YourDynamoDBTableName and YourGSIName with your actual table and index names.

Question :

can you put the files into a sub-path on s3 called "account-search"

Answer :

To store and manage the files within a specific sub-path on the S3 bucket, you simply need to prefix your file keys with the desired path. In this case, we'll use "account-search/" as the prefix for both the account names file and the last update timestamp file. This organization not only helps in structuring the data within the S3 bucket but also aids in permission management and navigation.

Let's adjust the relevant parts of the code to accommodate this:

# Assuming these environment variables are set for the bucket and file name
BUCKET_NAME = os.environ['S3_BUCKET_NAME']
ACCOUNTS_FILE_KEY = 'account-search/' + os.environ['S3_ACCOUNTS_FILE_NAME']  # Prefix with sub-path
TIMESTAMP_FILE_KEY = 'account-search/last_update_timestamp.txt'  # Prefix with sub-path

def update_account_list_with_dynamodb(last_update):
    # The rest of the function remains unchanged
    ...

def get_last_update_timestamp():
    try:
        response = s3.get_object(Bucket=BUCKET_NAME, Key=TIMESTAMP_FILE_KEY)
        last_update = response['Body'].read().decode('utf-8')
    except s3.exceptions.NoSuchKey:
        last_update = '1970-01-01T00:00:00Z'  # Use a default timestamp if the file does not exist
    return last_update

def set_last_update_timestamp(timestamp):
    s3.put_object(Bucket=BUCKET_NAME, Key=TIMESTAMP_FILE_KEY, Body=timestamp.encode('utf-8'))

def load_data_from_s3():
    global in_memory_cache
    if not in_memory_cache:  # Load data if cache is empty
        last_update = get_last_update_timestamp()
        new_accounts = update_account_list_with_dynamodb(last_update)
        
        # Load the current account list from S3
        response = s3.get_object(Bucket=BUCKET_NAME, Key=ACCOUNTS_FILE_KEY)
        file_content = response['Body'].read().decode('utf-8')
        current_accounts = file_content.splitlines()
        
        updated_accounts = sorted(set(current_accounts + new_accounts))
        in_memory_cache = updated_accounts
        
        # Overwrite the S3 file with the updated account list
        s3.put_object(Bucket=BUCKET_NAME, Key=ACCOUNTS_FILE_KEY, Body='\n'.join(updated_accounts).encode('utf-8'))
        
        # Update the last update timestamp
        current_timestamp = datetime.utcnow().isoformat() + 'Z'
        set_last_update_timestamp(current_timestamp)

In this revised code:

  • ACCOUNTS_FILE_KEY is defined with the "account-search/" prefix added to the name of the accounts file, based on an environment variable for flexibility.
  • TIMESTAMP_FILE_KEY specifies the path and name of the last update timestamp file within the "account-search" sub-path.

This setup keeps your account-names file and the last_update_timestamp.txt file organized within the "account-search" directory in your S3 bucket. Make sure to update your environment variables or code to use the correct file names as needed. This approach enhances manageability and scalability of your file storage strategy on AWS S3.

Question :

can you add logging to output the duration that the autocomplete, update account list, and read account list steps take so i can query it with cloudwatch and show me a complete code sample?

Answer :

To incorporate logging into the AWS Lambda function, which will allow you to measure and output the duration of key steps such as autocomplete, updating the account list from DynamoDB, and reading the account list from S3, we'll use Python's built-in logging module and Python's time module to capture the start and end times of these operations. These logs can then be viewed in Amazon CloudWatch.

Below is the complete updated code sample with logging for the durations of the mentioned steps:

import os
import boto3
import json
import logging
import time
from datetime import datetime

# Initialize clients outside of the handler
s3 = boto3.client('s3')
dynamodb = boto3.client('dynamodb')

# Setup logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)

# Environment variables for S3 paths
BUCKET_NAME = os.environ['S3_BUCKET_NAME']
ACCOUNTS_FILE_KEY = 'account-search/' + os.environ['S3_ACCOUNTS_FILE_NAME']
TIMESTAMP_FILE_KEY = 'account-search/last_update_timestamp.txt'

TABLE_NAME = 'YourDynamoDBTableName'  # Your DynamoDB table name
GSI_NAME = 'YourGSIName'  # Your GSI name on the timestamp attribute

in_memory_cache = []

def update_account_list_with_dynamodb(last_update):
    start_time = time.time()
    response = dynamodb.query(
        TableName=TABLE_NAME,
        IndexName=GSI_NAME,
        KeyConditionExpression='timestamp > :last_update',
        ExpressionAttributeValues={':last_update': {'S': last_update}},
        ProjectionExpression='accountName',
    )
    new_accounts = [item['accountName']['S'] for item in response['Items']]
    duration = time.time() - start_time
    logger.info(f"Update account list from DynamoDB took {duration:.2f} seconds")
    return new_accounts

def get_last_update_timestamp():
    try:
        response = s3.get_object(Bucket=BUCKET_NAME, Key=TIMESTAMP_FILE_KEY)
        last_update = response['Body'].read().decode('utf-8')
    except s3.exceptions.NoSuchKey:
        last_update = '1970-01-01T00:00:00Z'
    return last_update

def set_last_update_timestamp(timestamp):
    s3.put_object(Bucket=BUCKET_NAME, Key=TIMESTAMP_FILE_KEY, Body=timestamp.encode('utf-8'))

def load_data_from_s3():
    global in_memory_cache
    if not in_memory_cache:
        last_update = get_last_update_timestamp()
        
        new_accounts = update_account_list_with_dynamodb(last_update)
        
        start_time = time.time()
        response = s3.get_object(Bucket=BUCKET_NAME, Key=ACCOUNTS_FILE_KEY)
        file_content = response['Body'].read().decode('utf-8')
        duration = time.time() - start_time
        logger.info(f"Read account list from S3 took {duration:.2f} seconds")
        
        current_accounts = file_content.splitlines()
        updated_accounts = sorted(set(current_accounts + new_accounts))
        in_memory_cache = updated_accounts
        
        # Overwrite the S3 file with the updated account list
        s3.put_object(Bucket=BUCKET_NAME, Key=ACCOUNTS_FILE_KEY, Body='\n'.join(updated_accounts).encode('utf-8'))
        
        current_timestamp = datetime.utcnow().isoformat() + 'Z'
        set_last_update_timestamp(current_timestamp)

def autocomplete(query):
    start_time = time.time()
    results = [account for account in in_memory_cache if query.lower() in account.lower()]
    duration = time.time() - start_time
    logger.info(f"Autocomplete operation took {duration:.2f} seconds")
    return results

def lambda_handler(event, context):
    load_data_from_s3()
    query = event.get('queryStringParameters', {}).get('query', '')
    
    if not query:
        return {'statusCode': 400, 'body': json.dumps({'message': 'Query parameter is required'})}
    
    results = autocomplete(query)
    
    return {'statusCode': 200, 'body': json.dumps(results)}

Key Changes for Logging:

  • Logging Setup: Configured at the beginning of the script, setting the log level to INFO. This will ensure that our logged messages will be outputted.
  • Timing Operations: Each critical operation (updating the account list from DynamoDB, reading the account list from S3, and performing the autocomplete operation) is wrapped with timing logic to measure its duration.
  • Logging Durations: After each operation, the duration is logged using the logging module. This information will be available in the CloudWatch logs for the Lambda function, allowing you to monitor performance.

Deployment Notes:

Ensure you have the appropriate IAM permissions for Lambda to access S3 and DynamoDB, as well as permissions to write logs to CloudWatch. Replace placeholders (YourDynamoDBTableName, YourGSIName, etc.) with your actual resource names and configure the necessary environment variables before deploying.

This setup provides insights into the performance of each component of your autocomplete feature, helping identify bottlenecks or areas for optimization.

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