Query Builders

Package gocqlx is an idiomatic extension to gocql that provides usability features. With gocqlx you can bind the query parameters from maps and structs, use named query parameters (:identifier) and scan the query results into structs and slices. It comes with a fluent and flexible CQL query builder and a database migrations module.

Package jet is a framework for writing type-safe SQL queries in Go, with ability to easily convert database query result into desired arbitrary object structure. Use the bellow command to install jet Install jet generator to GOPATH bin folder. This will allow generating jet files from the command line. *Make sure GOPATH bin folder is added to the PATH environment variable. Jet requires already defined database schema(with tables, enums etc), so that jet generator can generate SQL Builder and Model files. File generation is very fast, and can be added as every pre-build step. Sample command: Then next step is to import generated SQL Builder and Model files and write SQL queries in Go: To write SQL queries for PostgreSQL import: To write SQL queries for MySQL and MariaDB import: *Dot import is used so that Go code resemble as much as native SQL. Dot import is not mandatory. Write SQL: Store result into desired destination: Detail info about all features and use cases can be found at project wiki page - https://github.com/go-jet/jet/wiki.

Package sqldimel provides a SQL DML query builder. Package sqldimel provides a SQL DML query builder.

Provides SQL table metadata, enabling select field lists, easy getters, relations when using a query builder.

Package jet is a framework for writing type-safe SQL queries in Go, with ability to easily convert database query result into desired arbitrary object structure. Use the bellow command to install jet Install jet generator to GOPATH bin folder. This will allow generating jet files from the command line. *Make sure GOPATH bin folder is added to the PATH environment variable. Jet requires already defined database schema(with tables, enums etc), so that jet generator can generate SQL Builder and Model files. File generation is very fast, and can be added as every pre-build step. Sample command: Then next step is to import generated SQL Builder and Model files and write SQL queries in Go: To write SQL queries for PostgreSQL import: To write SQL queries for MySQL and MariaDB import: *Dot import is used so that Go code resemble as much as native SQL. Dot import is not mandatory. Write SQL: Store result into desired destination: Detail info about all features and use cases can be found at project wiki page - https://github.com/go-jet/jet/wiki.

Package jet is a framework for writing type-safe SQL queries in Go, with ability to easily convert database query result into desired arbitrary object structure. Use the bellow command to install jet Install jet generator to GOPATH bin folder. This will allow generating jet files from the command line. *Make sure GOPATH bin folder is added to the PATH environment variable. Jet requires already defined database schema(with tables, enums etc), so that jet generator can generate SQL Builder and Model files. File generation is very fast, and can be added as every pre-build step. Sample command: Then next step is to import generated SQL Builder and Model files and write SQL queries in Go: To write SQL queries for PostgreSQL import: To write SQL queries for MySQL and MariaDB import: *Dot import is used so that Go code resemble as much as native SQL. Dot import is not mandatory. Write SQL: Store result into desired destination: Detail info about all features and use cases can be found at project wiki page - https://github.com/go-jet/jet/wiki.

goqu an idiomatch SQL builder, and query package. Please see https://github.com/kovetskiy/goqu for an introduction to goqu.

goqu an idiomatch SQL builder, and query package. Please see https://github.com/GlebBeloded/goqu for an introduction to goqu.

Package dotsql provides a way to separate your code from SQL queries. It is not an ORM, it is not a query builder. Dotsql is a library that helps you keep sql files in one place and use it with ease. For more usage examples see https://github.com/gchaincl/dotsql

goqu an idiomatch SQL builder, and query package. Please see https://github.com/doug-martin/goqu for an introduction to goqu.

Package dbx provides a set of DB-agnostic and easy-to-use query building methods for relational databases. This example shows how to do CRUD operations. This example shows how to populate DB data in different ways. This example shows how to use query builder to build DB queries. This example shows how to use query builder in transactions.

Package dotsql provides a way to separate your code from SQL queries. It is not an ORM, it is not a query builder. Dotsql is a library that helps you keep sql files in one place and use it with ease. For more usage examples see https://github.com/gchaincl/dotsql

Provides SQL table metadata, enabling select field lists, easy getters, relations when using a query builder.

goqu an idiomatch SQL builder, and query package. Please see https://github.com/doug-martin/goqu for an introduction to goqu.

Package gocqlx is an idiomatic extension to gocql that provides usability features. With gocqlx you can bind the query parameters from maps and structs, use named query parameters (:identifier) and scan the query results into structs and slices. It comes with a fluent and flexible CQL query builder and a database migrations module.

Package dotsql provides a way to separate your code from SQL queries. It is not an ORM, it is not a query builder. Dotsql is a library that helps you keep sql files in one place and use it with ease. For more usage examples see https://github.com/gchaincl/dotsql

Package dbx provides a set of DB-agnostic and easy-to-use query building methods for relational databases. This example shows how to do CRUD operations. This example shows how to populate DB data in different ways. This example shows how to use query builder to build DB queries. This example shows how to use query builder in transactions.

Package dotsql provides a way to separate your code from SQL queries. It is not an ORM, it is not a query builder. Dotsql is a library that helps you keep sql files in one place and use it with ease. For more usage examples see https://github.com/gchaincl/dotsql

goqu an idiomatch SQL builder, and query package. Please see https://github.com/doug-martin/goqu for an introduction to goqu.

Package dotsql provides a way to separate your code from SQL queries. It is not an ORM, it is not a query builder. Dotsql is a library that helps you keep sql files in one place and use it with ease. For more usage examples see https://github.com/gchaincl/dotsql

Package jet is a framework for writing type-safe SQL queries in Go, with ability to easily convert database query result into desired arbitrary object structure. Use the bellow command to install jet Install jet generator to GOPATH bin folder. This will allow generating jet files from the command line. *Make sure GOPATH bin folder is added to the PATH environment variable. Jet requires already defined database schema(with tables, enums etc), so that jet generator can generate SQL Builder and Model files. File generation is very fast, and can be added as every pre-build step. Sample command: Then next step is to import generated SQL Builder and Model files and write SQL queries in Go: To write SQL queries for PostgreSQL import: To write SQL queries for MySQL and MariaDB import: *Dot import is used so that Go code resemble as much as native SQL. Dot import is not mandatory. Write SQL: Store result into desired destination: Detail info about all features and use cases can be found at project wiki page - https://github.com/go-jet/jet/wiki.

Package jet is a framework for writing type-safe SQL queries in Go, with ability to easily convert database query result into desired arbitrary object structure. Use the bellow command to install jet Install jet generator to GOPATH bin folder. This will allow generating jet files from the command line. *Make sure GOPATH bin folder is added to the PATH environment variable. Jet requires already defined database schema(with tables, enums etc), so that jet generator can generate SQL Builder and Model files. File generation is very fast, and can be added as every pre-build step. Sample command: Then next step is to import generated SQL Builder and Model files and write SQL queries in Go: To write SQL queries for PostgreSQL import: To write SQL queries for MySQL and MariaDB import: *Dot import is used so that Go code resemble as much as native SQL. Dot import is not mandatory. Write SQL: Store result into desired destination: Detail info about all features and use cases can be found at project wiki page - https://github.com/go-jet/jet/wiki.

Package sqlz (pronounced "sequelize") is an un-opinionated, un-obtrusive SQL query builder for Go projects, based on github.com/jmoiron/sqlx. As opposed to other query builders, sqlz does not mean to bridge the gap between different SQL servers and implementations by providing a unified interface. Instead, it aims to support an extended SQL syntax that may be implementation-specific. For example, if you wish to use PostgreSQL-specific features such as JSON operators and upsert statements, sqlz means to support these without caring if the underlying database backend really is PostgreSQL. In other words, sqlz builds whatever queries you want it to build. sqlz is easy to integrate into existing code, as it does not require you to create your database connections through the sqlz API; in fact, it doesn't supply one. You can either use your existing `*sql.DB` connection or an `*sqlx.DB` connection, so you can start writing new queries with sqlz without having to modify any existing code. sqlz leverages sqlx for easy loading of query results. Please make sure you are familiar with how sqlx works in order to understand how row scanning is performed. You may need to add `db` struct tags to your Go structures. sqlz provides a comfortable API for running queries in a transaction, and will automatically commit or rollback the transaction as necessary.

Package esquery provides a non-obtrusive, idiomatic and easy-to-use query and aggregation builder for the official Go client (https://github.com/elastic/go-elasticsearch) for the ElasticSearch database (https://www.elastic.co/products/elasticsearch). esquery alleviates the need to use extremely nested maps (map[string]interface{}) and serializing queries to JSON manually. It also helps eliminating common mistakes such as misspelling query types, as everything is statically typed. Using `esquery` can make your code much easier to write, read and maintain, and significantly reduce the amount of code you write. esquery provides a method chaining-style API for building and executing queries and aggregations. It does not wrap the official Go client nor does it require you to change your existing code in order to integrate the library. Queries can be directly built with `esquery`, and executed by passing an `*elasticsearch.Client` instance (with optional search parameters). Results are returned as-is from the official client (e.g. `*esapi.Response` objects). Getting started is extremely simple: esquery currently supports version 7 of the ElasticSearch Go client. The library cannot currently generate "short queries". For example, whereas ElasticSearch can accept this: { "query": { "term": { "user": "Kimchy" } } } The library will always generate this: This is also true for queries such as "bool", where fields like "must" can either receive one query object, or an array of query objects. `esquery` will generate an array even if there's only one query object.

Package godb is query builder and struct mapper. godb does not manage relationships like Active Record or Entity Framework, it's not a full-featured ORM. Its goal is to be more productive than manually doing mapping between Go structs and databases tables. godb needs adapters to use databases, some are packaged with godb for : Start with an adapter, and the Open method which returns a godb.DB pointer : There are three ways to executes SQL with godb : Using raw queries you can execute any SQL queries and get the results into a slice of structs (or single struct) using the automatic mapping. Structs tools looks more 'orm-ish' as they're take instances of objects or slices to run select, insert, update and delete. Statements tools stand between raw queries and structs tools. It's easier to use than raw queries, but are limited to simpler cases. The statements tools are based on types : Example : The SelectStatement type could also build a query using columns from a structs. It facilitates the build of queries returning values from multiple table (or views). See struct mapping explanations, in particular the `rel` part. Example : The structs tools are based on types : Examples : Raw queries are executed using the RawSQL type. The query could be a simple hand-written string, or something complex builded using SQLBuffer and Conditions. Example : Stucts contents are mapped to databases columns with tags, like in previous example with the Book struct. The tag is 'db' and its content is : For autoincrement identifier simple use both 'key' and 'auto'. Example : More than one field could have the 'key' keyword, but with most databases drivers none of them could have the 'auto' keyword, because executing an insert query only returns one value : the last inserted id : https://golang.org/pkg/database/sql/driver/#RowsAffected.LastInsertId . With PostgreSQL you cas have multiple fields with 'key' and 'auto' options. Structs could be nested. A nested struct is mapped only if has the 'db' tag. The tag value is a columns prefix applied to all fields columns of the struct. The prefix is not mandatory, a blank string is allowed (no prefix). A nested struct could also have an optionnal `rel` attribute of the form `rel=relationname`. It's useful to build a select query using multiples relations (table, view, ...). See the example using the BooksWithInventories type. Example Databases columns are : The mapping is managed by the 'dbreflect' subpackage. Normally its direct use is not necessary, except in one case : some structs are scannable and have to be considered like fields, and mapped to databases columns. Common case are time.Time, or sql.NullString, ... You can register a custom struct with the `RegisterScannableStruct` and a struct instance, for example the time.Time is registered like this : The structs statements use the struct name as table name. But you can override this simply by simplementing a TableName method : Statements and structs tools manage 'where' and 'group by' sql clauses. These conditional clauses are build either with raw sql code, or build with the Condition struct like this : WhereQ methods take a Condition instance build by godb.Q . Where mathods take raw SQL, but is just a syntactic sugar. These calls are equivalents : Multiple calls to Where or WhereQ are allowed, these calls are equivalents : Slices are managed in a particular way : a single placeholder is replaced with multiple ones. This allows code like : The SQLBuffer exists to ease the build of complex raw queries. It's also used internaly by godb. Its use and purpose are simple : concatenate sql parts (accompagned by their arguments) in an efficient way. Example : For all databases, structs updates and deletes manage optimistic locking when a dedicated integer row is present. Simply tags it with `oplock` : When an update or delete operation fails, Do() returns the `ErrOpLock` error. With PostgreSQL and SQL Server, godb manages optimistic locking with automatic fields. Just add a dedicated field in the struct and tag it with `auto,oplock`. With PostgreSQL you can use the `xmin` system column like this : For more informations about `xmin` see https://www.postgresql.org/docs/10/static/ddl-system-columns.html With SQL Server you can use a `rowversion` field with the `mssql.Rowversion` type like this : For more informations about the `rowversion` data type see https://docs.microsoft.com/en-us/sql/t-sql/data-types/rowversion-transact-sql godb keep track of time consumed while executing queries. You can reset it and get the time consumed since Open or the previous reset : You can log all executed queried and details of condumed time. Simply add a logger : godb takes advantage of PostgreSQL RETURNING clause, and SQL Server OUTPUT clause. With statements tools you have to add a RETURNING clause with the Suffix method and call DoWithReturning method instead of Do(). It's optionnal. With StructInsert it's transparent, the RETURNING or OUTPUT clause is added for all 'auto' columns and it's managed for you. One of the big advantage is with BulkInsert : for others databases the rows are inserted but the new keys are unkonwns. With PostgreSQL and SQL Server the slice is updated for all inserted rows. It also enables optimistic locking with *automatic* columns. godb has two prepared statements caches, one to use during transactions, and one to use outside of a transaction. Both use a LRU algorithm. The transaction cache is enabled by default, but not the other. A transaction (sql.Tx) isn't shared between goroutines, using prepared statement with it has a predictable behavious. But without transaction a prepared statement could have to be reprepared on a different connection if needed, leading to unpredictable performances in high concurrency scenario. Enabling the non transaction cache could improve performances with single goroutine batch. With multiple goroutines accessing the same database : it depends ! A benchmark would be wise. Using statements tools and structs tools you can execute select queries and get an iterator instead of filling a slice of struct instances. This could be useful if the request's result is big and you don't want to allocate too much memory. On the other side you will write almost as much code as with the `sql` package, but with an automatic struct mapping, and a request builder. Iterators are also available with raw queries. In this cas you cas executes any kind of sql code, not just select queries. To get an interator simply use the `DoWithIterator` method instead of `Do`. The iterator usage is similar to the standard `sql.Rows` type. Don't forget to check that there are no errors with the `Err` method, and don't forget to call `Close` when the iterator is no longer useful, especially if you don't scan all the resultset. To avoid performance cost godb.DB does not implement synchronization. So a given instance of godb.DB should not be used by multiple goroutines. But a godb.DB instance can be created and used as a blueprint and cloned for each goroutine. See Clone and Clear methods. A typical use case is a web server. When the application starts a godb.DB is created, and cloned in each http handler with Clone, and ressources are to be freed calling Clear (use defer statement).

Package rds provides the client and types for making API requests to Amazon Relational Database Service. Amazon Relational Database Service (Amazon RDS) is a web service that makes it easier to set up, operate, and scale a relational database in the cloud. It provides cost-efficient, resizable capacity for an industry-standard relational database and manages common database administration tasks, freeing up developers to focus on what makes their applications and businesses unique. Amazon RDS gives you access to the capabilities of a MySQL, MariaDB, PostgreSQL, Microsoft SQL Server, Oracle, or Amazon Aurora database server. These capabilities mean that the code, applications, and tools you already use today with your existing databases work with Amazon RDS without modification. Amazon RDS automatically backs up your database and maintains the database software that powers your DB instance. Amazon RDS is flexible: you can scale your database instance's compute resources and storage capacity to meet your application's demand. As with all Amazon Web Services, there are no up-front investments, and you pay only for the resources you use. This interface reference for Amazon RDS contains documentation for a programming or command line interface you can use to manage Amazon RDS. Note that Amazon RDS is asynchronous, which means that some interfaces might require techniques such as polling or callback functions to determine when a command has been applied. In this reference, the parameter descriptions indicate whether a command is applied immediately, on the next instance reboot, or during the maintenance window. The reference structure is as follows, and we list following some related topics from the user guide. Amazon RDS API Reference For the alphabetical list of API actions, see API Actions (http://docs.aws.amazon.com/AmazonRDS/latest/APIReference/API_Operations.html). For the alphabetical list of data types, see Data Types (http://docs.aws.amazon.com/AmazonRDS/latest/APIReference/API_Types.html). For a list of common query parameters, see Common Parameters (http://docs.aws.amazon.com/AmazonRDS/latest/APIReference/CommonParameters.html). For descriptions of the error codes, see Common Errors (http://docs.aws.amazon.com/AmazonRDS/latest/APIReference/CommonErrors.html). Amazon RDS User Guide For a summary of the Amazon RDS interfaces, see Available RDS Interfaces (http://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Welcome.html#Welcome.Interfaces). For more information about how to use the Query API, see Using the Query API (http://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Using_the_Query_API.html). See https://docs.aws.amazon.com/goto/WebAPI/rds-2014-10-31 for more information on this service. See rds package documentation for more information. https://docs.aws.amazon.com/sdk-for-go/api/service/rds/ To Amazon Relational Database Service with the SDK use the New function to create a new service client. With that client you can make API requests to the service. These clients are safe to use concurrently. See the SDK's documentation for more information on how to use the SDK. https://docs.aws.amazon.com/sdk-for-go/api/ See aws.Config documentation for more information on configuring SDK clients. https://docs.aws.amazon.com/sdk-for-go/api/aws/#Config See the Amazon Relational Database Service client RDS for more information on creating client for this service. https://docs.aws.amazon.com/sdk-for-go/api/service/rds/#New The rdsutil package's BuildAuthToken function provides a connection authentication token builder. Given an endpoint of the RDS database, AWS region, DB user, and AWS credentials the function will create an presigned URL to use as the authentication token for the database's connection. The following example shows how to use BuildAuthToken to create an authentication token for connecting to a MySQL database in RDS. See rdsutil package for more information. http://docs.aws.amazon.com/sdk-for-go/api/service/rds/rdsutils/

Package esquery provides a non-obtrusive, idiomatic and easy-to-use query and aggregation builder for the official Go client (https://github.com/elastic/go-elasticsearch) for the ElasticSearch database (https://www.elastic.co/products/elasticsearch). esquery alleviates the need to use extremely nested maps (map[string]interface{}) and serializing queries to JSON manually. It also helps eliminating common mistakes such as misspelling query types, as everything is statically typed. Using `esquery` can make your code much easier to write, read and maintain, and significantly reduce the amount of code you write. esquery provides a method chaining-style API for building and executing queries and aggregations. It does not wrap the official Go client nor does it require you to change your existing code in order to integrate the library. Queries can be directly built with `esquery`, and executed by passing an `*elasticsearch.Client` instance (with optional search parameters). Results are returned as-is from the official client (e.g. `*esapi.Response` objects). Getting started is extremely simple: esquery currently supports version 7 of the ElasticSearch Go client. The library cannot currently generate "short queries". For example, whereas ElasticSearch can accept this: { "query": { "term": { "user": "Kimchy" } } } The library will always generate this: This is also true for queries such as "bool", where fields like "must" can either receive one query object, or an array of query objects. `esquery` will generate an array even if there's only one query object.

Package dbx provides a set of DB-agnostic and easy-to-use query building methods for relational databases. This example shows how to do CRUD operations. This example shows how to populate DB data in different ways. This example shows how to use query builder to build DB queries. This example shows how to use query builder in transactions.

Package gocqlx is an idiomatic extension to gocql that provides usability features. With gocqlx you can bind the query parameters from maps and structs, use named query parameters (:identifier) and scan the query results into structs and slices. It comes with a fluent and flexible CQL query builder and a database migrations module.

goqu an idiomatch SQL builder, and query package. Please see https://github.com/doug-martin/goqu for an introduction to goqu.

Package dotsql provides a way to separate your code from SQL queries. It is not an ORM, it is not a query builder. Dotsql is a library that helps you keep sql files in one place and use it with ease. For more usage examples see https://github.com/gchaincl/dotsql

goqu an idiomatch SQL builder, and query package. Please see https://github.com/doug-martin/goqu for an introduction to goqu.

Provides SQL table metadata, enabling select field lists, easy getters, relations when using a query builder.

Package gocqlx is an idiomatic extension to gocql that provides usability features. With gocqlx you can bind the query parameters from maps and structs, use named query parameters (:identifier) and scan the query results into structs and slices. It comes with a fluent and flexible CQL query builder and a database migrations module.

Package gocqlx is an idiomatic extension to gocql that provides usability features. With gocqlx you can bind the query parameters from maps and structs, use named query parameters (:identifier) and scan the query results into structs and slices. It comes with a fluent and flexible CQL query builder and a database migrations module.

Package sqlz implements an SQL query builder based on github.com/jmoiron/sqlx.

Package pqcomp provides dead simple query builder that support null types from sql package, but also provide interface Appearer. To be comprehensive solution, query builder needs to be optimized. Some of the benchmark results:

goqu an idiomatch SQL builder, and query package. Please see https://github.com/doug-martin/goqu for an introduction to goqu.

Package dbx provides a set of DB-agnostic and easy-to-use query building methods for relational databases. This example shows how to do CRUD operations. This example shows how to populate DB data in different ways. This example shows how to use query builder to build DB queries. This example shows how to use query builder in transactions.

Package dotsql provides a way to separate your code from SQL queries. It is not an ORM, it is not a query builder. Dotsql is a library that helps you keep sql files in one place and use it with ease. For more usage examples see https://github.com/gchaincl/dotsql

Package builder provides a fluent SQL query builder with support for multiple SQL dialects. It offers a clean and intuitive API for constructing SQL queries programmatically while handling proper escaping and parameter binding based on the target database system. The package supports multiple SQL dialects including MySQL, PostgreSQL, and SQLite, with appropriate escaping and placeholder styles for each. It provides a type-safe way to build complex SQL queries without string concatenation or manual escaping. Example usage: For more examples, see the builder_test.go file. Package builder provides a fluent SQL query builder with support for multiple SQL dialects. Package builder provides a fluent SQL query builder with support for multiple SQL dialects. Package builder provides a fluent SQL query builder with support for multiple SQL dialects. Package builder provides a fluent SQL query builder with support for multiple SQL dialects. Package builder provides a fluent SQL query builder with support for multiple SQL dialects. Package builder provides a fluent SQL query builder with support for multiple SQL dialects.

Package dbx provides a set of DB-agnostic and easy-to-use query building methods for relational databases. This example shows how to do CRUD operations. This example shows how to populate DB data in different ways. This example shows how to use query builder to build DB queries. This example shows how to use query builder in transactions.