Big Data and Modern Database Systems

Big Data Systems Use Case - Search Engines

• Search engines began in the early 1990s, replacing yellow pages-style indexes to address the growing number of web pages.
• Around 2000, Google became dominant, achieving a 90% market share.
• A fundamental element involves indexing, where data is organized for efficient retrieval.
• The basic web search interaction involves users inputting queries that are processed by the index, directing them to the relevant document store.
• Search engines use an inverted index to identify documents containing specific keywords that reflect the user's query.
• In the inverted index, each word is a key and the list of documents containing it is the value.
• Building an inverted index involves tokenizing documents to extract words, creating lists of documents that contain each word and storing pointers to the document and the word's position.

Search Engine Architecture

• A search engine comprises three core components: crawler, indexer, and search.
• The crawler collects and stores relevant documents from the internet, while indexing documents to create a searchable index.
• The search component returns relevant URLs to the user queries on the index.
• The search engine's performance is crucial as it handles millions of queries and documents.

Key-Value Stores

• Key-value stores are scalable containers for key-value pairs in non-relational databases, crucial for big data applications.
• They prioritize speed, scalability, and flexibility, often used at web-scale.
• They offer simpler syntax and semantics compared to traditional relational databases.
• The fundamental operations for key-value stores are put (key, value), get (key), and delete (key).
• Often, simpler in structure compared to relational databases.

Infrastructure and Monitoring

• Search engine infrastructure includes hardware like servers and storage devices, along with various networking components.
• Virtualization technologies, such as containers, offer scalability and efficiency benefits with different methods of managing machines.
• Scheduling and workload management is vital for performance.
• Effective monitoring systems track server performance, network traffic, and storage utilization to ensure optimal search engine operation.
• Monitoring encompasses a range of activities.

MapReduce

• MapReduce is a distributed data processing programming model, inspired by the map and reduce functions in functional programming languages.
• This model is highly scalable, well-suited for large and distributed data processing tasks
• The core idea is a map function that transforms data and a reduce function that aggregates the results.
• It automatically handles tasks like partitioning, scheduling, and fault tolerance on a large cluster of machines.

Smarter Result Ranking

• Ranking systems are essential for determining which results to display to users based on their search terms, which directly impacts user experience.
• Ranking utilizes factors like frequency of terms in results and other factors to sort relevant results for user searches.
• PageRank is a prominent approach for ranking web pages.

Serving Requests

• Serving requests involves retrieving relevant documents for user queries based on the inverted index.
• The user query is the input and the output is a list of URLs that match the query.
• It also involves substantial requirements.

More on Interaction

• Modern internet applications require efficient retrieval of information, handling rapidly changing data definitions, and accommodating increasing numbers of users and data volumes.
• Large volumes demand scalability and speed.

Big Data System Stack

• Big data solutions involve a complex stack of technologies, each with specific responsibilities and interactions.
• This includes tools for storage, data processing, and other components.

Hadoop Stack

• Hadoop is a distributed processing framework, built on a system based on Google's approach.
• Its core elements include a distributed file system (HDFS), MapReduce, YARN, and others.

HBase

• HBase, a BigTable clone, provides a key-value storage system built on top of Hadoop Distributed File System (HDFS).
• HDFS manages replication, metadata, and storage, while HBase handles row storage and structured data.

Hadoop MapReduce

• Hadoop MapReduce is a distributed data processing framework that parallels Google's.
• It processes enormous datasets by breaking them into smaller chunks for distributed processing by multiple worker nodes.
• It consists of a JobTracker that distributes tasks to WorkerNodes, which process their assigned fragments of the input data.

Hive

• Hive is a data warehousing tool that runs on Hadoop, supporting complex queries in SQL-like syntax over large datasets.
• It can perform large operations on massive data sets.

ML Systems

• ML Systems are platforms for implementing and running machine learning applications.
• They frequently have libraries for various machine-learning tasks.

Big Data Stack Diagram

• A comprehensive diagram depicts the components of a Big Data system, arranged in a hierarchical fashion to illustrate their interrelationships.

System Evolution

• Big Data systems tend to evolve by either specializing in specific functions or generalizing to handle multiple functions over time.
• Initial systems frequently have an application-centric approach but later evolve towards a broader functionality.

Where are we heading?

• The trend leans towards unified systems (like Porcella) designed to manage various analytical needs.

Next Part

• Upcoming topics focus on monitoring and measurement, a vital aspect for maintaining optimal performance of a system.

This quiz covers key concepts related to Big Data and the limitations of traditional relational databases. It explores applications, technologies, and challenges associated with modern database systems and analytics. Test your knowledge on these essential topics for understanding data management in today's computing environment.