Tree

This executable provides an HTTP server that watches for file system changes to .go files within the working directory (and all nested go packages). Navigating to the configured host and port in a web browser will display the latest results of running `go test` in each go package.

Package btree implements in-memory B-Trees of arbitrary degree. btree implements an in-memory B-Tree for use as an ordered data structure. It is not meant for persistent storage solutions. It has a flatter structure than an equivalent red-black or other binary tree, which in some cases yields better memory usage and/or performance. See some discussion on the matter here: Note, though, that this project is in no way related to the C++ B-Tree implementation written about there. Within this tree, each node contains a slice of items and a (possibly nil) slice of children. For basic numeric values or raw structs, this can cause efficiency differences when compared to equivalent C++ template code that stores values in arrays within the node: These issues don't tend to matter, though, when working with strings or other heap-allocated structures, since C++-equivalent structures also must store pointers and also distribute their values across the heap. This implementation is designed to be a drop-in replacement to gollrb.LLRB trees, (http://github.com/petar/gollrb), an excellent and probably the most widely used ordered tree implementation in the Go ecosystem currently. Its functions, therefore, exactly mirror those of llrb.LLRB where possible. Unlike gollrb, though, we currently don't support storing multiple equivalent values. There are two implementations; those suffixed with 'G' are generics, usable for any type, and require a passed-in "less" function to define their ordering. Those without this prefix are specific to the 'Item' interface, and use its 'Less' function for ordering.

Package etree provides XML services through an Element Tree abstraction.

Copyright 2020 Joshua J Baker. All rights reserved. Use of this source code is governed by an MIT-style license that can be found in the LICENSE file. Copyright 2020 Joshua J Baker. All rights reserved. Use of this source code is governed by an MIT-style license that can be found in the LICENSE file. Copyright 2020 Joshua J Baker. All rights reserved. Use of this source code is governed by an MIT-style license that can be found in the LICENSE file.

Package awsauth implements AWS request signing using Signed Signature Version 2, Signed Signature Version 3, and Signed Signature Version 4. Supports S3 and STS.

Package manners provides a wrapper for a standard net/http server that ensures all active HTTP client have completed their current request before the server shuts down. It can be used a drop-in replacement for the standard http package, or can wrap a pre-configured Server. eg. or for a customized server: The server will shut down cleanly when the Close() method is called:

Package treeprint provides a simple ASCII tree composing tool.

Package merkletree implements a Merkle Tree capable of storing arbitrary content. A Merkle Tree is a hash tree that provides an efficient way to verify the contents of a set data are present and untampered with. At its core, a Merkle Tree is a list of items representing the data that should be verified. Each of these items is inserted into a leaf node and a tree of hashes is constructed bottom up using a hash of the nodes left and right children's hashes. This means that the root node will effictively be a hash of all other nodes (hashes) in the tree. This property allows the tree to be reproduced and thus verified by on the hash of the root node of the tree. The benefit of the tree structure is verifying any single content entry in the tree will require only nlog2(n) steps in the worst case. Creating a new merkletree requires that the type that the tree will be constructed from implements the Content interface. A slice of the Content items should be created and then passed to the NewTree method. t represents the Merkle Tree and can be verified and manipulated with the API methods described below.

This is inspired by Julien Schmidt's httprouter, in that it uses a patricia tree, but the implementation is rather different. Specifically, the routing rules are relaxed so that a single path segment may be a wildcard in one route and a static token in another. This gives a nice combination of high performance with a lot of convenience in designing the routing patterns.

This is inspired by Julien Schmidt's httprouter, in that it uses a patricia tree, but the implementation is rather different. Specifically, the routing rules are relaxed so that a single path segment may be a wildcard in one route and a static token in another. This gives a nice combination of high performance with a lot of convenience in designing the routing patterns.

Package rtreego is a library for efficiently storing and querying spatial data.

Package art implements an Adapative Radix Tree(ART) in pure Go. Note that this implementation is not thread-safe but it could be really easy to implement. The design of ART is based on "The Adaptive Radix Tree: ARTful Indexing for Main-Memory Databases" [1]. Usage Also the current implementation was inspired by [2] and [3] [1] http://db.in.tum.de/~leis/papers/ART.pdf (Specification) [2] https://github.com/armon/libart (C99 implementation) [3] https://github.com/kellydunn/go-art (other Go implementation)

Code generated by test_grammar_generate.sh; DO NOT EDIT.

Package gotree create and print tree.

Package assertions contains the implementations for all assertions which are referenced in goconvey's `convey` package (github.com/smartystreets/goconvey/convey) and gunit (github.com/smartystreets/gunit) for use with the So(...) method. They can also be used in traditional Go test functions and even in applications. https://smartystreets.com Many of the assertions lean heavily on work done by Aaron Jacobs in his excellent oglematchers library. (https://github.com/jacobsa/oglematchers) The ShouldResemble assertion leans heavily on work done by Daniel Jacques in his very helpful go-render library. (https://github.com/luci/go-render)

Package gunit provides "testing" package hooks and convenience functions for writing tests in an xUnit style. See the README file and the examples folder for examples.

Package rbtree implements operations on Red-Black tree.

Package merkletree is an implementation of a Merkle tree (https://en.wikipedia.org/wiki/Merkle_tree). It provides methods to create a tree and generate and verify proofs. The hashing algorithm for the tree is selectable between BLAKE2b and Keccak256, or you can supply your own. Creating a Merkle tree requires a list of values that are each byte arrays. Once a tree has been created proofs can be generated using the tree's GenerateProof() function. The package includes a function to verify a generated proof given only the data to prove, proof and the root hash of the relevant Merkle tree. This allows for efficient verification of proofs without requiring the entire Merkle tree to be stored or recreated. The tree pads its values to the next highest power of 2; values not supplied are treated as 0. This can be seen graphically by generating a DOT representation of the graph with DOT().

Package kdtree implements a k-d tree data structure.

Package merkletree provides tools for calculating the Merkle root of a dataset, for creating a proof that a piece of data is in a Merkle tree of a given root, and for verifying proofs that a piece of data is in a Merkle tree of a given root. The tree is implemented according to the specification for Merkle trees provided in RFC 6962. Package merkletree also supports building roots and proofs from cached subroots of the Merkle tree. For example, a large file could be cached by building the Merkle root for each 4MB sector and remembering the Merkle roots of each sector. Using a cached tree, the Merkle root of the whole file can be computed by passing the cached tree each of the roots of the 4MB sector. Building proofs using these cached roots is also supported. A proof must be built within the target sector using a normal Tree, requiring the whole sector to be hashed. The results of that proof can then be passed into the Prove() function of a cached tree, which will create the full proof without needing to hash the entire file. Caching also makes it inexpensive to update the Merkle root of the file after changing or deleting segments of the larger file. Examples can be found in the README for the package.

Package merkletree provides tools for calculating the Merkle root of a dataset, for creating a proof that a piece of data is in a Merkle tree of a given root, and for verifying proofs that a piece of data is in a Merkle tree of a given root. The tree is implemented according to the specification for Merkle trees provided in RFC 6962. Package merkletree also supports building roots and proofs from cached subroots of the Merkle tree. For example, a large file could be cached by building the Merkle root for each 4MB sector and remembering the Merkle roots of each sector. Using a cached tree, the Merkle root of the whole file can be computed by passing the cached tree each of the roots of the 4MB sector. Building proofs using these cached roots is also supported. A proof must be built within the target sector using a normal Tree, requiring the whole sector to be hashed. The results of that proof can then be passed into the Prove() function of a cached tree, which will create the full proof without needing to hash the entire file. Caching also makes it inexpensive to update the Merkle root of the file after changing or deleting segments of the larger file. Examples can be found in the README for the package.

Package bolt implements a low-level key/value store in pure Go. It supports fully serializable transactions, ACID semantics, and lock-free MVCC with multiple readers and a single writer. Bolt can be used for projects that want a simple data store without the need to add large dependencies such as Postgres or MySQL. Bolt is a single-level, zero-copy, B+tree data store. This means that Bolt is optimized for fast read access and does not require recovery in the event of a system crash. Transactions which have not finished committing will simply be rolled back in the event of a crash. The design of Bolt is based on Howard Chu's LMDB database project. Bolt currently works on Windows, Mac OS X, and Linux. There are only a few types in Bolt: DB, Bucket, Tx, and Cursor. The DB is a collection of buckets and is represented by a single file on disk. A bucket is a collection of unique keys that are associated with values. Transactions provide either read-only or read-write access to the database. Read-only transactions can retrieve key/value pairs and can use Cursors to iterate over the dataset sequentially. Read-write transactions can create and delete buckets and can insert and remove keys. Only one read-write transaction is allowed at a time. The database uses a read-only, memory-mapped data file to ensure that applications cannot corrupt the database, however, this means that keys and values returned from Bolt cannot be changed. Writing to a read-only byte slice will cause Go to panic. Keys and values retrieved from the database are only valid for the life of the transaction. When used outside the transaction, these byte slices can point to different data or can point to invalid memory which will cause a panic.

Package goquery implements features similar to jQuery, including the chainable syntax, to manipulate and query an HTML document. It brings a syntax and a set of features similar to jQuery to the Go language. It is based on Go's net/html package and the CSS Selector library cascadia. Since the net/html parser returns nodes, and not a full-featured DOM tree, jQuery's stateful manipulation functions (like height(), css(), detach()) have been left off. Also, because the net/html parser requires UTF-8 encoding, so does goquery: it is the caller's responsibility to ensure that the source document provides UTF-8 encoded HTML. See the repository's wiki for various options on how to do this. Syntax-wise, it is as close as possible to jQuery, with the same method names when possible, and that warm and fuzzy chainable interface. jQuery being the ultra-popular library that it is, writing a similar HTML-manipulating library was better to follow its API than to start anew (in the same spirit as Go's fmt package), even though some of its methods are less than intuitive (looking at you, index()...). It is hosted on GitHub, along with additional documentation in the README.md file: https://github.com/puerkitobio/goquery Please note that because of the net/html dependency, goquery requires Go1.1+. The various methods are split into files based on the category of behavior. The three dots (...) indicate that various "overloads" are available. * array.go : array-like positional manipulation of the selection. * expand.go : methods that expand or augment the selection's set. * filter.go : filtering methods, that reduce the selection's set. * iteration.go : methods to loop over the selection's nodes. * manipulation.go : methods for modifying the document * property.go : methods that inspect and get the node's properties values. * query.go : methods that query, or reflect, a node's identity. * traversal.go : methods to traverse the HTML document tree. * type.go : definition of the types exposed by goquery. * utilities.go : definition of helper functions (and not methods on a *Selection) that are not part of jQuery, but are useful to goquery. This example scrapes the reviews shown on the home page of metalsucks.net.

Package badger implements an embeddable, simple and fast key-value database, written in pure Go. It is designed to be highly performant for both reads and writes simultaneously. Badger uses Multi-Version Concurrency Control (MVCC), and supports transactions. It runs transactions concurrently, with serializable snapshot isolation guarantees. Badger uses an LSM tree along with a value log to separate keys from values, hence reducing both write amplification and the size of the LSM tree. This allows LSM tree to be served entirely from RAM, while the values are served from SSD. Badger has the following main types: DB, Txn, Item and Iterator. DB contains keys that are associated with values. It must be opened with the appropriate options before it can be accessed. All operations happen inside a Txn. Txn represents a transaction, which can be read-only or read-write. Read-only transactions can read values for a given key (which are returned inside an Item), or iterate over a set of key-value pairs using an Iterator (which are returned as Item type values as well). Read-write transactions can also update and delete keys from the DB. See the examples for more usage details.

Package badger implements an embeddable, simple and fast key-value database, written in pure Go. It is designed to be highly performant for both reads and writes simultaneously. Badger uses Multi-Version Concurrency Control (MVCC), and supports transactions. It runs transactions concurrently, with serializable snapshot isolation guarantees. Badger uses an LSM tree along with a value log to separate keys from values, hence reducing both write amplification and the size of the LSM tree. This allows LSM tree to be served entirely from RAM, while the values are served from SSD. Badger has the following main types: DB, Txn, Item and Iterator. DB contains keys that are associated with values. It must be opened with the appropriate options before it can be accessed. All operations happen inside a Txn. Txn represents a transaction, which can be read-only or read-write. Read-only transactions can read values for a given key (which are returned inside an Item), or iterate over a set of key-value pairs using an Iterator (which are returned as Item type values as well). Read-write transactions can also update and delete keys from the DB. See the examples for more usage details.

Package badger implements an embeddable, simple and fast key-value database, written in pure Go. It is designed to be highly performant for both reads and writes simultaneously. Badger uses Multi-Version Concurrency Control (MVCC), and supports transactions. It runs transactions concurrently, with serializable snapshot isolation guarantees. Badger uses an LSM tree along with a value log to separate keys from values, hence reducing both write amplification and the size of the LSM tree. This allows LSM tree to be served entirely from RAM, while the values are served from SSD. Badger has the following main types: DB, Txn, Item and Iterator. DB contains keys that are associated with values. It must be opened with the appropriate options before it can be accessed. All operations happen inside a Txn. Txn represents a transaction, which can be read-only or read-write. Read-only transactions can read values for a given key (which are returned inside an Item), or iterate over a set of key-value pairs using an Iterator (which are returned as Item type values as well). Read-write transactions can also update and delete keys from the DB. See the examples for more usage details.