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Approximate Membership Query Filter
Approximate Membership Query Filter (AMQ-Filter) is a group of space-efficient probabilistic data structures that supports approximate membership queries. An approximate membership query answers if an element is in a set or not with a false positive rate of \epsilon. Bloom filters are the most known AMQ-Filter, but there are other AMQ-Filters that support additional operations or have different space requirements. AMQ-Filters have numerous applications, mainly in distributed systems and databases. There, they are often used to avoid network request or I/O operations that result from requesting elements that do not exist. Approximate Membership Query Problem The approximate membership query problem is to store information about a set of elements S in a space-efficient way. The goal is to answer queries whether an element x is in the set S or not while allowing false positives with a maximal probability of \epsilon. All AMQ-Filter support this operation lookup. Dynamic AMQ-Filters ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bloom Filter
A Bloom filter is a space-efficient probabilistic data structure, conceived by Burton Howard Bloom in 1970, that is used to test whether an element is a member of a set. False positive matches are possible, but false negatives are not – in other words, a query returns either "possibly in set" or "definitely not in set". Elements can be added to the set, but not removed (though this can be addressed with the counting Bloom filter variant); the more items added, the larger the probability of false positives. Bloom proposed the technique for applications where the amount of source data would require an impractically large amount of memory if "conventional" error-free hashing techniques were applied. He gave the example of a hyphenation algorithm for a dictionary of 500,000 words, out of which 90% follow simple hyphenation rules, but the remaining 10% require expensive disk accesses to retrieve specific hyphenation patterns. With sufficient core memory, an error-free hash coul ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Quotient Filter
A quotient filter is a space-efficient probabilistic data structure used to test whether an element is a member of a set (an approximate membership query filter, AMQ). A query will elicit a reply specifying either that the element is definitely not in the set or that the element is probably in the set. The former result is definitive; ''i.e.'', the test does not generate false negatives. But with the latter result there is some probability, ε, of the test returning "element is in the set" when in fact the element is not present in the set (''i.e.'', a false positive). There is a tradeoff between ε, the false positive rate, and storage size; increasing the filter's storage size reduces ε. Other AMQ operations include "insert" and "optionally delete". The more elements are added to the set, the larger the probability of false positives. A typical application for quotient filters, and other AMQ filters, is to serve as a proxy for the keys in a database on disk. As keys are ad ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cuckoo Filter
A cuckoo filter is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set, like a Bloom filter does. False positive matches are possible, but false negatives are not – in other words, a query returns either "possibly in set" or "definitely not in set". A cuckoo filter can also delete existing items, which is not supported by Bloom filters. In addition, for applications that store many items and target moderately low false positive rates, cuckoo filters can achieve lower space overhead than space-optimized Bloom filters. Cuckoo filters were first described in 2014. Algorithm description A cuckoo filter uses a hash table based on cuckoo hashing to store the fingerprints of items. The data structure is broken into buckets of some size b. To insert the fingerprint of an item x, one first computes two potential buckets h_1(x) and h_2(x) where x could go. These buckets are calculated using the formula :h_1(x)=\text(x) :h_2(x)=h_1(x ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cuckoo Hashing
Cuckoo hashing is a scheme in computer programming for resolving hash collisions of values of hash functions in a table, with worst-case constant lookup time. The name derives from the behavior of some species of cuckoo, where the cuckoo chick pushes the other eggs or young out of the nest when it hatches in a variation of the behavior referred to as brood parasitism; analogously, inserting a new key into a cuckoo hashing table may push an older key to a different location in the table. History Cuckoo hashing was first described by Rasmus Pagh and Flemming Friche Rodler in a 2001 conference paper. The paper was awarded the European Symposium on Algorithms Test-of-Time award in 2020. Operations Cuckoo hashing is a form of open addressing in which each non-empty cell of a hash table contains a key or key–value pair. A hash function is used to determine the location for each key, and its presence in the table (or the value associated with it) can be found by examining that ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fingerprint (computing)
In computer science, a fingerprinting algorithm is a procedure that maps an arbitrarily large data item (such as a computer file) to a much shorter bit string, its fingerprint, that uniquely identifies the original data for all practical purposesA. Z. Broder. Some applications of Rabin's fingerprinting method. In Sequences II: Methods in Communications, Security, and Computer Science, pages 143--152. Springer-Verlag, 1993 just as human fingerprints uniquely identify people for practical purposes. This fingerprint may be used for data deduplication purposes. This is also referred to as file fingerprinting, data fingerprinting, or structured data fingerprinting. Fingerprints are typically used to avoid the comparison and transmission of bulky data. For instance, a web browser or proxy server can efficiently check whether a remote file has been modified, by fetching only its fingerprint and comparing it with that of the previously fetched copy.Detecting duplicate and near-duplicate ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Perfect Hash Function
In computer science, a perfect hash function for a set is a hash function that maps distinct elements in to a set of integers, with no collisions. In mathematical terms, it is an injective function. Perfect hash functions may be used to implement a lookup table with constant worst-case access time. A perfect hash function can, as any hash function, be used to implement hash tables, with the advantage that no collision resolution has to be implemented. In addition, if the keys are not the data and if it is known that queried keys will be valid, then the keys do not need to be stored in the lookup table, saving space. Disadvantages of perfect hash functions are that needs to be known for the construction of the perfect hash function. Non-dynamic perfect hash functions need to be re-constructed if changes. For frequently changing dynamic perfect hash functions may be used at the cost of additional space. The space requirement to store the perfect hash function is in . The ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Log-structured Merge-tree
In computer science, the log-structured merge-tree (also known as LSM tree, or LSMT) is a data structure with performance characteristics that make it attractive for providing indexed access to files with high insert volume, such as transactional log data. LSM trees, like other search trees, maintain key-value pairs. LSM trees maintain data in two or more separate structures, each of which is optimized for its respective underlying storage medium; data is synchronized between the two structures efficiently, in batches. One simple version of the LSM tree is a two-level LSM tree. As described by Patrick O'Neil, a two-level LSM tree comprises two tree-like structures, called C0 and C1. C0 is smaller and entirely resident in memory, whereas C1 is resident on disk. New records are inserted into the memory-resident C0 component. If the insertion causes the C0 component to exceed a certain size threshold, a contiguous segment of entries is removed from C0 and merged into C1 on disk. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bigtable
Bigtable is a fully managed wide-column and key-value NoSQL database service for large analytical and operational workloads as part of the Google Cloud portfolio. History Bigtable development began in 2004.. It is now used by a number of Google applications, such as Google Analytics, web indexing, MapReduce, which is often used for generating and modifying data stored in Bigtable, Google Maps,. Google Books search, "My Search History", Google Earth, Blogger.com, Google Code hosting, YouTube, and Gmail. Google's reasons for developing its own database include scalability and better control of performance characteristics. Google's Spanner RDBMS is layered on an implementation of Bigtable with a Paxos group for two-phase commits to each table. Google F1 was built using Spanner to replace an implementation based on MySQL. Apache HBase and Cassandra are some of the best known open source projects that were modeled after Bigtable. On May 6, 2015, a public version of Bigtable ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
HBase
HBase is an open-source non-relational distributed database modeled after Google's Bigtable and written in Java. It is developed as part of Apache Software Foundation's Apache Hadoop project and runs on top of HDFS (Hadoop Distributed File System) or Alluxio, providing Bigtable-like capabilities for Hadoop. That is, it provides a fault-tolerant way of storing large quantities of sparse data (small amounts of information caught within a large collection of empty or unimportant data, such as finding the 50 largest items in a group of 2 billion records, or finding the non-zero items representing less than 0.1% of a huge collection). HBase features compression, in-memory operation, and Bloom filters on a per-column basis as outlined in the original Bigtable paper. Tables in HBase can serve as the input and output for MapReduce jobs run in Hadoop, and may be accessed through the Java API but also through REST, Avro or Thrift gateway APIs. HBase is a wide-column store and has been ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
LevelDB
LevelDB is an open-source on-disk key-value store written by Google fellows Jeffrey Dean and Sanjay Ghemawat. Inspired by Bigtable, LevelDB is hosted on GitHub under the New BSD License and has been ported to a variety of Unix-based systems, macOS, Windows, and Android. Features LevelDB stores keys and values in arbitrary byte arrays, and data is sorted by key. It supports batching writes, forward and backward iteration, and compression of the data via Google's Snappy compression library. LevelDB is not an SQL database. Like other NoSQL and dbm stores, it does not have a relational data model and it does not support SQL queries. Also, it has no support for indexes. Applications use LevelDB as a library, as it does not provide a server or command-line interface. MariaDB 10.0 comes with a storage engine which allows users to query LevelDB tables from MariaDB. History LevelDB is based on concepts from Google's Bigtable database system. The table implementation for the Bigtable ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
SQLite4
SQLite (, ) is a database engine written in the C programming language. It is not a standalone app; rather, it is a library that software developers embed in their apps. As such, it belongs to the family of embedded databases. It is the most widely deployed database engine, as it is used by several of the top web browsers, operating systems, mobile phones, and other embedded systems. Many programming languages have bindings to the SQLite library. It generally follows PostgreSQL syntax, but does not enforce type checking by default. This means that one can, for example, insert a string into a column defined as an integer. History D. Richard Hipp designed SQLite in the spring of 2000 while working for General Dynamics on contract with the United States Navy. Hipp was designing software used for a damage-control system aboard guided-missile destroyers, which originally used HP-UX with an IBM Informix database back-end. SQLite began as a Tcl extension. In August 2000, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
