Reverse index, Inverted index, Trie, Rsync, Merkle tree

6. Reverse index

A reverse key index is a type of index found in database management systems where the key value is reversed before entering it into the index. For example, the value 24538 becomes 83542 in the index . This technique is particularly useful when indexing data such as sequence numbers where each new key value is greater than the prior value. Reversing the key value reduces contention for index blocks and has become particularly important in high volume transaction processing systems. While this technique is not applicable to all types of data, it can significantly improve performance for certain use cases.

Reverse indexes can be used in distributed systems to improve performance by minimizing contention for index blocks. In a high-volume transaction processing system, there can be a large number of index blocks being written to or read from simultaneously, leading to contention for these resources. By reversing the key value before entering it into the index, the distribution of index blocks can be more evenly spread out, reducing contention and improving performance. Additionally, reverse indexes can be used for fast lookups in a wide range of data structures, including inverted indexes used for information retrieval systems, document stores, and full-text search engines.

A reverse index simply stores the values for the index in reverse order. A value such as 123 would be stored as 321 in a reverse index. Oracle shifts the order of the value transparently, so that a user in the previous example would input the value 123 and have the same value returned in a query. When a reverse index is used, the natural tendency of the index toward an unbalanced state is prevented, because consecutive values are distributed through the B*-tree index structure. The following SlideShow illustrates the problem and the solution delivered by reverse indexes:

7. Inverted index

An inverted index is a database index that stores a mapping from content, such as words or numbers, to its locations in a table, document, or set of documents. The purpose of an inverted index is to allow for fast full-text searches, but it comes at the cost of increased processing when a document is added to the database. This data structure is a central component of a typical search engine indexing algorithm and is used on a large scale in search engines, document retrieval systems, and full-text search engines. There are two main variants of inverted indexes: record-level and word-level, with the latter offering more functionality such as phrase searches, but requiring more processing power and space to be created. Additionally, compression techniques are often used to reduce the amount of storage required for an inverted index.

8. Trie algorithm

The trie algorithm, also known as a prefix tree, is a tree-based data structure used for storing and searching associative arrays or sets of strings . Unlike a binary search tree, nodes in a trie do not directly store the values associated with their keys. Rather, each node represents a character in a string key and has links to child nodes representing subsequent characters, with the entire string key being stored at the position in the tree defined by the path taken to reach its final character node. This allows for fast searching and retrieval of values based on their associated keys, as well as a variety of string-related operations such as prefix matching and auto-completion. In distributed systems, tries can be partitioned or replicated across multiple machines to improve performance and fault tolerance, with care taken to maintain data consistency and handle updates. Trie algorithms have numerous practical applications, including in information retrieval, spell checking, and networking protocols.

In distributed systems, tries can be partitioned or replicated across multiple machines to improve performance and fault tolerance. Partitioning involves dividing the trie into smaller sub-tries, with each sub-trie being stored on a separate machine. Queries can then be sent to these machines in parallel, with the results aggregated at the client-side. Replication, on the other hand, involves replicating the trie across multiple machines using distributed hash table (DHT) or peer-to-peer protocols. This ensures that each machine has a consistent copy of the data.

A hybrid approach can also be used where some parts of the trie are partitioned while others are replicated. This can help balance the load across the machines while also ensuring consistency and fault tolerance. However, maintaining data consistency and handling updates can be challenging in a distributed setting. Techniques such as versioning, conflict resolution, and distributed locking can be used to address these issues.

Trie algorithms have numerous practical applications in distributed systems, including in information retrieval, spell checking, and networking protocols. By partitioning or replicating the data structure across multiple machines, performance can be improved while maintaining data consistency and availability.

9. Rsync algorithm

The rsync algorithm is a type of delta encoding used for minimizing network usage when transferring and synchronizing files between computers . It works by comparing the modification times and sizes of files on the source and destination systems, and only transferring the differences (or “deltas”) between them. This reduces the amount of data that needs to be transmitted over the network, making the transfer faster and reducing network congestion. Additionally, zlib may be used to add data compression, and SSH or stunnel can be used for security. Rsync is typically used for synchronizing files and directories between two different systems, and is written in C as a single-threaded application.

10. Merkle tree

A Merkle tree is a tree data structure used in cryptography and computer science applications for efficient and secure verification of data integrity. In a Merkle tree, each leaf node is labeled with the cryptographic hash of a data block, and each non-leaf node is labeled with the cryptographic hash of the labels of its child nodes. This allows for verifying the contents of a large data structure using only a small subset of the hash values, making it efficient and highly secure. In distributed systems, Merkle trees are used for efficient and secure data verification across multiple nodes or peers. By creating and distributing Merkle trees among nodes, they can easily verify that the same data is available on all nodes and detect any changes, making it useful for applications such as blockchain, P2P networks, and file sharing. Merkle trees are a type of cryptographic commitment scheme, and the root of the tree serves as the commitment to the entire data structure.