Graph

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.

Ristretto is a fast, fixed size, in-memory cache with a dual focus on throughput and hit ratio performance. You can easily add Ristretto to an existing system and keep the most valuable data where you need it. This package includes multiple probabalistic data structures needed for admission/eviction metadata. Most are Counting Bloom Filter variations, but a caching-specific feature that is also required is a "freshness" mechanism, which basically serves as a "lifetime" process. This freshness mechanism was described in the original TinyLFU paper 1, but other mechanisms may be better suited for certain data distributions.

Package appdash provides a Go app performance tracing suite. Appdash allows you to trace the end-to-end performance of hierarchically structured applications. You can, for example, measure the time and see the detailed information of each HTTP request and SQL query made by an entire distributed web application. The cmd/appdash tool launches a web front-end which displays a web UI for viewing collected app traces. It is effectively a remote collector which your application can connect and send events to. Timing and application-specific metadata information can be viewed in a nice timeline view for each span (e.g. HTTP request) and it's children. The web front-end can also be embedded in your own Go HTTP server by utilizing the traceapp sub-package, which is effectively what cmd/appdash serves internally. Sub-packages for HTTP and SQL event tracing are provided for use with appdash, which allows it to function equivalently to Google's Dapper and Twitter's Zipkin performance tracing suites. The most high-level structure is a Trace, which represents the performance of an application from start to finish (in an HTTP application, for example, the loading of a web page). A Trace is a tree structure that is made up of several spans, which are just IDs (in an HTTP application, these ID's are passed through the stack via a few special headers). Each span ID has a set of Events that directly correspond to it inside a Collector. These events can be any combination of message, log, time-span, or time-stamped events (the cmd/appdash web UI displays these events as appropriate). Inside your application, a Recorder is used to send events to a Collector, which can be a remote HTTP(S) collector, a local in-memory or persistent collector, etc. Additionally, you can implement the Collector interface yourself and store events however you like.

Package graphql provides a GraphQL client implementation. For more information, see package github.com/shurcooL/githubv4, which is a specialized version targeting GitHub GraphQL API v4. That package is driving the feature development. For now, see README for more details.

Package graphql provides a low level GraphQL client. To specify your own http.Client, use the WithHTTPClient option:

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 graphql-go-tools is library to create GraphQL services using the go programming language. GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools. Source: https://graphql.org This library is intended to be a set of low level building blocks to write high performance and secure GraphQL applications. Use cases could range from writing layer seven GraphQL proxies, firewalls, caches etc.. You would usually not use this library to write a GraphQL server yourself but to build tools for the GraphQL ecosystem. To achieve this goal the library has zero dependencies at its core functionality. It has a full implementation of the GraphQL AST and supports lexing, parsing, validation, normalization, introspection, query planning as well as query execution etc. With the execution package it's possible to write a fully functional GraphQL server that is capable to mediate between various protocols and formats. In it's current state you can use the following DataSources to resolve fields: - Static data (embed static data into a schema to extend a field in a simple way) - HTTP JSON APIs (combine multiple Restful APIs into one single GraphQL Endpoint, nesting is possible) - GraphQL APIs (you can combine multiple GraphQL APIs into one single GraphQL Endpoint, nesting is possible) - Webassembly/WASM Lambdas (e.g. resolve a field using a Rust lambda) If you're looking for a ready to use solution that has all this functionality packaged as a Gateway have a look at: https://github.com/jensneuse/graphql-gateway Created by Jens Neuse

Package gographviz provides parsing for the DOT grammar into an abstract syntax tree representing a graph, analysis of the abstract syntax tree into a more usable structure, and writing back of this structure into the DOT format.

Package dataloader is an implimentation of facebook's dataloader in go. See https://github.com/facebook/dataloader for more information

Package dataloader is an implimentation of facebook's dataloader in go. See https://github.com/facebook/dataloader for more information

Package graph contains generic implementations of basic graph algorithms. The algorithms in this library can be applied to any graph data structure implementing the two Iterator methods: Order, which returns the number of vertices, and Visit, which iterates over the neighbors of a vertex. All algorithms operate on directed graphs with a fixed number of vertices, labeled from 0 to n-1, and edges with integer cost. An undirected edge {v, w} of cost c is represented by the two directed edges (v, w) and (w, v), both of cost c. A self-loop, an edge connecting a vertex to itself, is both directed and undirected. The type Mutable represents a directed graph with a fixed number of vertices and weighted edges that can be added or removed. The implementation uses hash maps to associate each vertex in the graph with its adjacent vertices. This gives constant time performance for all basic operations. The type Immutable is a compact representation of an immutable graph. The implementation uses lists to associate each vertex in the graph with its adjacent vertices. This makes for fast and predictable iteration: the Visit method produces its elements by reading from a fixed sorted precomputed list. This type supports multigraphs. The subpackage graph/build offers a tool for building virtual graphs. In a virtual graph no vertices or edges are stored in memory, they are instead computed as needed. New virtual graphs are constructed by composing and filtering a set of standard graphs, or by writing functions that describe the edges of a graph. The Basics example shows how to build a plain graph and how to efficiently use the Visit iterator, the key abstraction of this package. The DFS example contains a full implementation of depth-first search. Build a plain graph and visit all of its edges. Show how to use this package by implementing a complete depth-first search.

Package graphql provides a GraphQL client implementation. For more information, see package github.com/hasura/go-graphql-client For now, see README for more details.

Package jsonrpc2 provides a client and server implementation of [JSON-RPC 2.0](http://www.jsonrpc.org/specification).

Package graph is a library for creating generic graph data structures and modifying, analyzing, and visualizing them. A graph consists of vertices of type T, which are identified by a hash value of type K. The hash value for a given vertex is obtained using the hashing function passed to New. A hashing function takes a T and returns a K. For primitive types like integers, you may use a predefined hashing function such as IntHash – a function that takes an integer and uses that integer as the hash value at the same time: For storing custom data types, you need to provide your own hashing function. This example takes a City instance and returns its name as the hash value: Creating a graph using this hashing function will yield a graph of vertices of type City identified by hash values of type string. Adding vertices to a graph of integers is simple. graph.Graph.AddVertex takes a vertex and adds it to the graph. Most functions accept and return only hash values instead of entire instances of the vertex type T. For example, graph.Graph.AddEdge creates an edge between two vertices and accepts the hash values of those vertices. Because this graph uses the IntHash hashing function, the vertex values and hash values are the same. All operations that modify the graph itself are methods of Graph. All other operations are top-level functions of by this library. For detailed usage examples, take a look at the README.

Package dgo is used to interact with a Dgraph server. Queries, mutations, and most other types of admin tasks can be run from the client.

Package syntaxhighlight provides syntax highlighting for code. It currently uses a language-independent lexer and performs decently on JavaScript, Java, Ruby, Python, Go, and C.

Package dataloader is an implementation of facebook's dataloader in go. See https://github.com/facebook/dataloader for more information

Package dgo is used to interact with a Dgraph server. Queries, mutations, and most other types of admin tasks can be run from the client.

Package graphql-go-tools is library to create GraphQL services using the go programming language. GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools. Source: https://graphql.org This library is intended to be a set of low level building blocks to write high performance and secure GraphQL applications. Use cases could range from writing layer seven GraphQL proxies, firewalls, caches etc.. You would usually not use this library to write a GraphQL server yourself but to build tools for the GraphQL ecosystem. To achieve this goal the library has zero dependencies at its core functionality. It has a full implementation of the GraphQL AST and supports lexing, parsing, validation, normalization, introspection, query planning as well as query execution etc. With the execution package it's possible to write a fully functional GraphQL server that is capable to mediate between various protocols and formats. In it's current state you can use the following DataSources to resolve fields: - Static data (embed static data into a schema to extend a field in a simple way) - HTTP JSON APIs (combine multiple Restful APIs into one single GraphQL Endpoint, nesting is possible) - GraphQL APIs (you can combine multiple GraphQL APIs into one single GraphQL Endpoint, nesting is possible) - Webassembly/WASM Lambdas (e.g. resolve a field using a Rust lambda) If you're looking for a ready to use solution that has all this functionality packaged as a Gateway have a look at: https://wundergraph.com Created by Jens Neuse