System Design Ads Target Query Service

date

Mar 28, 2024

slug

design-ads-target

status

Published

Step 1: Understand the Problem and Establish Design Scope

Clarifying Questions:

Features Required: What are the essential features of the ads targeting system? Is it limited to querying based on gender and location, or are there more attributes?

Scale: How many ads merchants and users does the system need to support? What's the expected query volume?

Latency Requirements: What are the latency expectations for query responses?

Data Freshness: How up-to-date does the user information need to be? Is there a requirement for real-time data updates?

Integration: How will this system integrate with existing platforms or services?

Assumptions:

The system needs to support querying on multiple user attributes beyond gender and location (e.g., age, interests).

Initially, the system will support 1 million users and 10,000 ads merchants, with rapid scaling expected.

Low latency (<100ms) for query responses is critical for a good user experience.

Data freshness is important, but a slight delay (up to 1 minute) in reflecting updates is acceptable.

Step 2: Propose High-Level Design and Get Buy-in

Initial Blueprint:

Clients: Ads merchants interact with the system via a web interface or API to submit their queries.

API Gateway: Serves as the entry point for all requests, handling authentication, rate limiting, and routing.

Query Service: Parses and executes the queries against the user attributes store. This service is designed to be scalable and efficient in filtering and matching criteria.

KV Store: A distributed key-value store that maintains user attributes. This store needs to support high read throughput and efficient querying capabilities.

Cache: To improve query response times, frequently accessed data is cached.

Back-of-the-Envelope Calculations:

Assuming an average query touches 5% of the total user base, and with 1 million users, each query needs to efficiently filter through 50,000 user records.

Diagram Sketch:

Query Service

Functionality: Responsible for parsing and executing the queries submitted by ads merchants. It needs to efficiently handle complex query logic (e.g., combinations of AND, OR conditions across multiple attributes).

Optimization: Implement query optimization techniques such as query rewriting and predicate pushdown to minimize the amount of data fetched and processed. Use in-memory data structures to speed up frequent query patterns.

KV Store

Choice of Technology: A distributed KV store like Apache Cassandra or Amazon DynamoDB could be used, considering their ability to scale horizontally and manage large volumes of data with low latency.

Data Modeling: Data should be modeled to facilitate efficient attribute-based queries. This might involve denormalizing data and using composite keys that include the attributes being queried most often.

Cache

Caching Strategy: Implement a multi-level caching strategy where frequently accessed data is stored in an in-memory cache like Redis or Memcached. Consider using local caches on the query service instances for even faster access to hot data.

Invalidation: Implement an effective cache invalidation strategy to ensure data consistency, using techniques such as time-to-live (TTL), write-through, or write-behind caching.

Scalability and Performance

Sharding: The KV store should be sharded based on a well-chosen key that distributes the data evenly across the shards. This could be the user ID or a hash of it.

Load Balancing: Use load balancers to distribute incoming queries evenly across instances of the query service. This helps in efficiently utilizing resources and maintaining high availability.

Replication: Data in the KV store should be replicated across multiple nodes to ensure high availability and fault tolerance. Consistency levels need to be carefully chosen based on the use case.

Monitoring and Operation

Monitoring: Implement comprehensive monitoring of all components to track performance metrics, error rates, and system health. Use this data for alerting and to make informed decisions on scaling.

Deployment: Consider containerization and orchestration tools like Docker and Kubernetes for deploying the system components. This facilitates easy scaling, management, and deployment of updates.

Step 3.1: Design Deep Dive

Data Storage Layer

Technology Choices:

Distributed File System: Amazon S3 or Hadoop HDFS for storing raw user data and logs. These systems are highly durable, scalable, and optimized for high throughput.

Database: Apache Cassandra or Amazon DynamoDB for the KV store. These databases offer high performance, scalability, and are optimized for key-value data models. They support efficient data retrieval, which is crucial for attribute-based querying.

Design Considerations:

Data Partitioning: Data should be partitioned based on attributes that are frequently queried together. This could involve composite keys in Cassandra or careful selection of partition keys in DynamoDB.

Data Modeling: For Cassandra, use wide rows to store user attributes, enabling efficient retrieval of all attributes for a user. In DynamoDB, consider using secondary indexes for attributes that are frequently queried.

Data Querying/Processing Layer

Technology Choices:

In-memory Data Processing: Apache Spark or Flink for processing complex queries. These frameworks can handle large-scale data processing in near real-time, support complex operations, and can read from various data sources like S3, HDFS, or Cassandra.

Caching: Redis or Memcached for caching hot data and intermediate query results. This reduces the need to access the database frequently, thereby reducing latency.

Design Considerations:

Distributed Computing: Leverage Spark's or Flink's distributed computing capabilities to parallelize query processing. This involves breaking down the query into smaller tasks that can be executed across multiple nodes.

State Management: For streaming data processing (using Flink), efficiently manage state across streams to handle complex queries that span multiple records or require aggregations.

Ensuring Performance

Query Optimization:

Predicate Pushdown: Leverage predicate pushdown to minimize the amount of data scanned by moving filtering operations closer to the data storage layer.

Indexing: Use appropriate indexing strategies in the database to speed up data retrieval. Secondary indexes in Cassandra or DynamoDB can significantly reduce query times for non-primary key attributes.

Scalability and Fault Tolerance:

Horizontal Scaling: Both the data storage and processing layers should be designed to scale horizontally. Adding more nodes to the Cassandra cluster or Spark/Flink cluster should increase the system's capacity without downtime.

Replication and Checkpointing: Ensure data in Cassandra or DynamoDB is replicated across multiple nodes to prevent data loss. Use checkpointing in Spark or Flink to recover from failures without data loss.

Monitoring and Tuning:

Performance Monitoring: Implement detailed monitoring of query times, system throughput, and error rates using tools like Prometheus or AWS CloudWatch.

Continuous Optimization: Regularly analyze performance metrics to identify bottlenecks. Optimize query patterns, update indexing strategies, and adjust resource allocations based on observed performance.

Step 3.2 Design Proposal

Data Storage Layer

Apache Cassandra for the primary datastore:

Pros:

Highly Scalable: Cassandra excels at handling large volumes of data across many commodity servers, providing linear scalability and fault tolerance.
Write Efficiency: It's designed for high write throughput, making it suitable for scenarios with heavy write loads.
Decentralized Design: There are no single points of failure, enhancing the system's resilience and availability.

Cons:

Complex Tuning: Cassandra requires careful tuning of its configuration parameters to achieve optimal performance, which can be complex and time-consuming.
Read Performance: While write operations are highly efficient, read operations can be slower, especially if not properly optimized.

Amazon S3 for storing raw user data and logs:

Pros:

Durability and Availability: Offers 99.999999999% durability and 99.99% availability, ensuring data is safe and accessible.
Scalability: Can store an unlimited amount of data, scaling seamlessly as storage needs grow.
Cost-Effective: Pay-as-you-go model and various storage classes to optimize costs.

Cons:

Latency: While fast, accessing data can have higher latency compared to block storage or local disks.
Operational Complexity: Managing lifecycle policies and storage classes can add operational complexity.

Data Processing and Querying Layer

Apache Spark for distributed data processing:

Pros:

Performance: Utilizes in-memory computing, which is significantly faster than disk-based processing for certain operations.
Flexibility: Can process data from a variety of sources (e.g., HDFS, S3, Cassandra) and supports multiple languages (Scala, Python, Java).
Advanced Analytics: Supports SQL queries, machine learning, graph processing, and streaming data.

Cons:

Resource Intensive: In-memory processing can be resource-intensive, requiring significant RAM to achieve optimal performance.
Complexity: There's a learning curve to effectively utilize its full capabilities and optimize for specific use cases.

Or Flink

Pros of Using Apache Flink

Stream Processing Capabilities: Flink is fundamentally designed for stream processing, enabling it to handle real-time data streams efficiently. This is crucial for ads targeting systems that need to process user interactions and behaviors as they happen.

Event Time Processing: Flink supports event time processing, which allows for more accurate handling of out-of-order events or late data. This is essential for ensuring that time-based analytics are accurate and reliable.

Fault Tolerance: Flink provides strong consistency guarantees through its checkpointing mechanism. This ensures that state is maintained accurately across the distributed system, even in the event of failures, making the system more resilient.

Scalability: Like Spark, Flink is designed to scale horizontally, allowing you to add more nodes to your Flink cluster to handle increased loads. This is essential for a system that needs to scale to support 10 million users.

Low Latency: Flink's architecture enables it to provide low-latency processing, which is critical for applications requiring immediate insights or actions based on incoming data streams.

Flexible Windowing: Flink offers flexible windowing mechanisms, including sliding windows, tumbling windows, and session windows, which are valuable for time-based aggregations and analytics.

Cons of Using Apache Flink

Operational Complexity: Managing and operating a Flink cluster, especially at scale, can be complex. Ensuring optimal performance and fault tolerance requires a deep understanding of Flink's architecture and configuration options.

Resource Intensity: While Flink is designed for efficiency, its in-memory state management and processing capabilities can be resource-intensive. Proper resource allocation and management are necessary to prevent bottlenecks and ensure smooth operation.

Learning Curve: Flink's comprehensive feature set and API can present a steep learning curve for teams new to stream processing. Adequate training and experience are required to leverage Flink's capabilities fully.

Ecosystem and Community: While Flink's community is active and growing, it is generally considered smaller than that of Apache Spark. This can affect the availability of third-party integrations, extensions, and support resources.

Apache Kafka for real-time data ingestion and stream processing:

Pros:

High Throughput: Can handle thousands of messages per second, making it suitable for real-time data processing needs.
Scalability: Easily scales horizontally, and partitions data across multiple brokers for fault tolerance.
Ecosystem: Integrates well with other data processing frameworks like Spark and Flink for complex analytics.

Cons:

Operational Overhead: Managing a Kafka cluster, especially at scale, can be complex and requires dedicated operational expertise.
Data Retention: By default, stores data for a limited time, requiring additional storage solutions for long-term data retention.

Other Alternatives for ingestion

Amazon Kinesis

Amazon Kinesis is a managed service offered by AWS designed for real-time processing of streaming data at scale. It can continuously capture gigabytes of data per second from hundreds of thousands of sources such as website clickstreams, database event streams, financial transactions, social media feeds, IT logs, and location-tracking events.

Pros:

Fully Managed: As a managed service, it reduces the operational overhead of managing the infrastructure.
Integration with AWS Ecosystem: Seamless integration with other AWS services for analytics, storage, and machine learning.
Scalability: Automatically scales to accommodate the throughput of data without the need to manage servers.

Cons:

Cost: Can become expensive at scale, especially for high throughput or large data volumes.
Vendor Lock-in: Being an AWS product, there's a potential for vendor lock-in, which might limit flexibility if you want to migrate to another cloud provider.

Google Cloud Pub/Sub

Google Cloud Pub/Sub is a fully managed messaging service that allows you to asynchronously send and receive messages between independent applications. It's designed to provide durable event ingestion and delivery for analytics pipelines and event-driven systems.

Pros:

Global Service: Provides a global messaging service that automatically scales with your data.
Integration: Offers strong integration with Google Cloud's big data tools and services.
Simplicity: Simplifies the architecture by providing a single service for both messaging and event streaming.

Cons:

Google Cloud Dependency: Similar to Amazon Kinesis, there's potential for vendor lock-in with Google Cloud.
Cost at Scale: While it offers a generous free tier, costs can grow with increased usage.

Apache Pulsar

Apache Pulsar is an open-source distributed pub-sub messaging system originally created at Yahoo and now part of the Apache Software Foundation. It's designed for high-performance, persistent messaging and stream processing.

Pros:

Built-in Multi-Tenancy: Supports multi-tenancy out of the box, allowing for logical separation of data and configuration.
Persistent Storage: Uses Apache BookKeeper for durable storage, ensuring no data loss.
Geo-Replication: Native support for geo-replication enables deploying a globally distributed messaging and streaming platform.

Cons:

Operational Complexity: While powerful, Pulsar can be complex to deploy and manage, especially in large-scale environments.
Ecosystem and Community: Though growing, Pulsar's ecosystem and community are smaller compared to Kafka's.

Apache NiFi

Apache NiFi is a data logistics platform designed to automate the movement of data between disparate systems. It provides real-time control that makes it easy to manage the movement of data between any source and any destination.

Pros:

User-Friendly Interface: Offers a web-based user interface for designing, controlling, and monitoring data flows.
Flexibility: Supports a wide range of data sources and destinations, and provides fine-grained control over data flows.
Data Provenance: Tracks data flow from source to destination, providing visibility into the data pipeline.

Cons:

Throughput: While highly flexible, it may not match the raw throughput capabilities of systems like Kafka or Pulsar for purely streaming use cases.
Complexity for Simple Pipelines: For straightforward streaming tasks, NiFi's feature set can be overkill, introducing unnecessary complexity.

Caching and Session Management

Redis for caching hot data and session management:

Pros:

Performance: Extremely fast, with the ability to serve thousands of requests per second, reducing latency for frequently accessed data.
Data Structures: Supports a variety of data structures, enabling complex applications beyond simple key-value storage.
Persistence: Offers options for data durability, with mechanisms to save in-memory data to disk.

Cons:

Memory Cost: Being in-memory, it can become costly for large datasets due to the need for significant RAM.
Data Size Limitation: Each Redis instance has a maximum data size limit based on the available memory.

Or Memcached

Simplicity and Specific Use Case

Use Case Specificity: If your application requires a straightforward, in-memory key-value store without the need for advanced data types (lists, sets, sorted sets, etc.) or features (persistence, replication, transactions, etc.), Memcached's simplicity can be a significant advantage. It does one thing — caching — and does it well.

Memory Efficiency

Memory Usage: Memcached can be more memory-efficient for certain workloads. It uses a slab allocation mechanism that can be more efficient in memory usage for storing small objects, which might make it a better choice if your cache primarily stores small and uniformly sized data.

Multithreading

Concurrency: Memcached has been designed with a multithreaded architecture from the start. It can efficiently handle a high number of concurrent connections and operations, making full use of multi-core CPUs without much effort. Redis, until version 6, handled connections in a single-threaded manner for most operations, which could lead to bottlenecks. Although Redis introduced some multithreading capabilities in version 6, Memcached might still have an edge in scenarios where high concurrency and connection rates are critical, and the workload is primarily cache gets and sets.

Large Cache Clusters

Scaling Pattern: In scenarios where you plan to scale out your cache across many nodes, Memcached's simplicity could be an advantage. Since Memcached does not support built-in replication or persistence, it can be easier to scale out to many nodes without worrying about the overhead of managing these features. This can be particularly useful in environments where losing cached data is acceptable and can easily be reconstructed from the primary data store.

Development and Operational Simplicity

Ease of Use: For development teams familiar with Memcached or in environments where the operational expertise for Memcached is stronger, it might make sense to use it over Redis. The simplicity of Memcached can also translate to fewer operational complexities when it comes to deployment, monitoring, and scaling.

Conclusion

While Redis is often preferred for its rich set of features and versatility, there are valid scenarios where Memcached's simplicity, memory efficiency, and performance characteristics for straightforward caching scenarios make it the better choice. Ultimately, the decision should be based on your specific application needs, existing infrastructure, and team expertise.

Infrastructure and Operations

Kubernetes for container orchestration:

Pros:

Scalability: Automatically scales the number of containers based on demand, improving resource utilization.
Portability: Containers can be deployed across various environments, simplifying deployment and testing.
Ecosystem: Large ecosystem with tools and integrations for monitoring, logging, and security.

Cons:

Complexity: Managing a Kubernetes cluster and its configurations can be complex, requiring specialized knowledge.
Resource Overhead: The orchestration layer itself requires resources, which can be significant depending on the cluster size.

Prometheus and Grafana for monitoring and visualization:

Pros:

Comprehensive Monitoring: Prometheus provides a powerful data model and query language for collecting time-series data, while Grafana offers rich visualization options.
Open Source: Both tools are open-source with large communities, providing extensive support and plugins.

Cons:

Storage Scaling: Prometheus, while scalable, can require additional configuration and storage solutions for long-term data retention at scale.
Complexity: Creating advanced dashboards and alerts in Grafana can be complex and time-consuming.

Step 4: Wrap Up and Follow ups

Some questions interviewer may ask

1. How would you handle data consistency in your distributed system?

Sample Answer: In a distributed system like an ads targeting platform, ensuring data consistency is crucial for delivering accurate targeting and analytics. I would use a combination of techniques based on the CAP theorem and specific use cases. For eventual consistency in user data updates, I'd leverage Cassandra's tunable consistency levels, opting for quorum reads and writes to balance consistency and availability. For real-time bidding or ad serving, where immediate consistency might be critical, I'd consider using Apache Kafka with strict ordering guarantees and exactly-once processing semantics to ensure that events are processed in a consistent manner across the system.

2. How do you plan to scale your database to handle 10 million users?

Sample Answer: Scaling a database to handle 10 million users involves several strategies. First, I'd ensure that the database schema is optimized for the most common queries, using techniques like denormalization for read efficiency. For a NoSQL database like Cassandra, I'd use appropriate partitioning keys to distribute data evenly across nodes, preventing hotspots. Additionally, implementing sharding to distribute the load across multiple clusters can help. To handle read scalability, I'd use read replicas. For write scalability, techniques like batching writes or using a write-behind cache can reduce the load on the database. Auto-scaling based on load metrics would also be essential to adjust resources dynamically.

3. How would you ensure low latency in your system?

Sample Answer: Ensuring low latency in an ads targeting system requires optimizing both the data path and the processing path. On the data path, using in-memory data stores like Redis for frequently accessed data can drastically reduce read latency. For the processing path, employing a stream processing framework like Apache Flink, which is designed for low-latency processing, is crucial. Additionally, deploying the system across geographically distributed data centers closer to the user base can reduce network latency. Implementing efficient data serialization and deserialization, and optimizing network protocols between services, also contributes to lower latency.

4. How do you plan to monitor and ensure the reliability of your system?

Sample Answer: Monitoring and ensuring the reliability of the system involves collecting and analyzing metrics, logs, and traces from all components. I'd use Prometheus for metric collection and alerting, combined with Grafana for dashboards. For logs, a centralized logging solution like ELK Stack (Elasticsearch, Logstash, Kibana) would allow for aggregating and querying logs across services. Distributed tracing with Jaeger or Zipkin can help in understanding request flows and latencies across microservices. Setting up alerting based on key performance indicators (KPIs) like error rates, response times, and system throughput is crucial for proactively identifying and addressing issues. Implementing chaos engineering practices, like injecting failures into the system to test its resilience, is also a strategy I'd consider for ensuring reliability.

5. How would you handle data privacy and security in your system?

Sample Answer: Data privacy and security are paramount, especially in a system dealing with user data for ad targeting. I'd start by ensuring all data in transit is encrypted using TLS and data at rest is encrypted using AES-256 or similar. Access control using OAuth 2.0 for APIs and role-based access control (RBAC) for internal systems would ensure that only authorized entities can access or modify data. For user data, implementing strict data retention policies and providing users with control over their data (e.g., the ability to delete their data) is essential for compliance with regulations like GDPR. Regular security audits and penetration testing would help identify and mitigate vulnerabilities.

These questions and answers are examples of the depth and breadth of knowledge expected in system design interviews, especially for scalable, distributed systems. Tailor your answers to reflect your experience, the specific technologies you're familiar with, and the requirements of the role you're interviewing for.