Graph Structure

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package jsonapi provides a serializer and deserializer for jsonapi.org spec payloads. You can keep your model structs as is and use struct field tags to indicate to jsonapi how you want your response built or your request deserialized. What about my relationships? jsonapi supports relationships out of the box and will even side load them in your response into an "included" array--that contains associated objects. jsonapi uses StructField tags to annotate the structs fields that you already have and use in your app and then reads and writes jsonapi.org output based on the instructions you give the library in your jsonapi tags. Example structs using a Blog > Post > Comment structure, jsonapi Tag Reference Value, primary: "primary,<type field output>" This indicates that this is the primary key field for this struct type. Tag value arguments are comma separated. The first argument must be, "primary", and the second must be the name that should appear in the "type" field for all data objects that represent this type of model. Value, attr: "attr,<key name in attributes hash>[,<extra arguments>]" These fields' values should end up in the "attribute" hash for a record. The first argument must be, "attr', and the second should be the name for the key to display in the "attributes" hash for that record. The following extra arguments are also supported: "omitempty": excludes the fields value from the "attribute" hash. "iso8601": uses the ISO8601 timestamp format when serialising or deserialising the time.Time value. Value, relation: "relation,<key name in relationships hash>" Relations are struct fields that represent a one-to-one or one-to-many to other structs. jsonapi will traverse the graph of relationships and marshal or unmarshal records. The first argument must be, "relation", and the second should be the name of the relationship, used as the key in the "relationships" hash for the record. Use the methods below to Marshal and Unmarshal jsonapi.org json payloads. Visit the readme at https://github.com/google/jsonapi

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package jsonapi provides a serializer and deserializer for jsonapi.org spec payloads. You can keep your model structs as is and use struct field tags to indicate to jsonapi how you want your response built or your request deserialzied. What about my relationships? jsonapi supports relationships out of the box and will even side load them in your response into an "included" array--that contains associated objects. jsonapi uses StructField tags to annotate the structs fields that you already have and use in your app and then reads and writes jsonapi.org output based on the instructions you give the library in your jsonapi tags. Example structs using a Blog > Post > Comment structure, jsonapi Tag Reference Value, primary: "primary,<type field output>" This indicates that this is the primary key field for this struct type. Tag value arguments are comma separated. The first argument must be, "primary", and the second must be the name that should appear in the "type" field for all data objects that represent this type of model. Value, attr: "attr,<key name in attributes hash>[,<extra arguments>]" These fields' values should end up in the "attribute" hash for a record. The first argument must be, "attr', and the second should be the name for the key to display in the the "attributes" hash for that record. The following extra arguments are also supported: "omitempty": excludes the fields value from the "attribute" hash. "iso8601": uses the ISO8601 timestamp format when serialising or deserialising the time.Time value. Value, relation: "relation,<key name in relationships hash>" Relations are struct fields that represent a one-to-one or one-to-many to other structs. jsonapi will traverse the graph of relationships and marshal or unmarshal records. The first argument must be, "relation", and the second should be the name of the relationship, used as the key in the "relationships" hash for the record. Use the methods below to Marshal and Unmarshal jsonapi.org json payloads. Visit the readme at https://github.com/google/jsonapi

Package jsonapi provides a serializer and deserializer for jsonapi.org spec payloads. You can keep your model structs as is and use struct field tags to indicate to jsonapi how you want your response built or your request deserialzied. What about my relationships? jsonapi supports relationships out of the box and will even side load them in your response into an "included" array--that contains associated objects. jsonapi uses StructField tags to annotate the structs fields that you already have and use in your app and then reads and writes jsonapi.org output based on the instructions you give the library in your jsonapi tags. Example structs using a Blog > Post > Comment structure, jsonapi Tag Reference Value, primary: "primary,<type field output>" This indicates that this is the primary key field for this struct type. Tag value arguments are comma separated. The first argument must be, "primary", and the second must be the name that should appear in the "type" field for all data objects that represent this type of model. Value, attr: "attr,<key name in attributes hash>[,<extra arguments>]" These fields' values should end up in the "attribute" hash for a record. The first argument must be, "attr', and the second should be the name for the key to display in the "attributes" hash for that record. The following extra arguments are also supported: "omitempty": excludes the fields value from the "attribute" hash. "iso8601": uses the ISO8601 timestamp format when serialising or deserialising the time.Time value. Value, relation: "relation,<key name in relationships hash>" Relations are struct fields that represent a one-to-one or one-to-many to other structs. jsonapi will traverse the graph of relationships and marshal or unmarshal records. The first argument must be, "relation", and the second should be the name of the relationship, used as the key in the "relationships" hash for the record. Use the methods below to Marshal and Unmarshal jsonapi.org json payloads. Visit the readme at https://github.com/google/jsonapi

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 pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package jsonapi provides a serializer and deserializer for jsonapi.org spec payloads. You can keep your model structs as is and use struct field tags to indicate to jsonapi how you want your response built or your request deserialzied. What about my relationships? jsonapi supports relationships out of the box and will even side load them in your response into an "included" array--that contains associated objects. jsonapi uses StructField tags to annotate the structs fields that you already have and use in your app and then reads and writes jsonapi.org output based on the instructions you give the library in your jsonapi tags. Example structs using a Blog > Post > Comment structure, jsonapi Tag Reference Value, primary: "primary,<type field output>" This indicates that this is the primary key field for this struct type. Tag value arguments are comma separated. The first argument must be, "primary", and the second must be the name that should appear in the "type" field for all data objects that represent this type of model. Value, attr: "attr,<key name in attributes hash>[,<extra arguments>]" These fields' values should end up in the "attribute" hash for a record. The first argument must be, "attr', and the second should be the name for the key to display in the the "attributes" hash for that record. The following extra arguments are also supported: "omitempty": excludes the fields value from the "attribute" hash. "iso8601": uses the ISO8601 timestamp format when serialising or deserialising the time.Time value. Value, relation: "relation,<key name in relationships hash>" Relations are struct fields that represent a one-to-one or one-to-many to other structs. jsonapi will traverse the graph of relationships and marshal or unmarshal records. The first argument must be, "relation", and the second should be the name of the relationship, used as the key in the "relationships" hash for the record. Use the methods below to Marshal and Unmarshal jsonapi.org json payloads. Visit the readme at https://github.com/google/jsonapi

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package cadence and its subdirectories contain the Cadence client side framework. The Cadence service is a task orchestrator for your application’s tasks. Applications using Cadence can execute a logical flow of tasks, especially long-running business logic, asynchronously or synchronously. They can also scale at runtime on distributed systems. A quick example illustrates its use case. Consider Uber Eats where Cadence manages the entire business flow from placing an order, accepting it, handling shopping cart processes (adding, updating, and calculating cart items), entering the order in a pipeline (for preparing food and coordinating delivery), to scheduling delivery as well as handling payments. Cadence consists of a programming framework (or client library) and a managed service (or backend). The framework enables developers to author and coordinate tasks in Go code. The root cadence package contains common data structures. The subpackages are: The Cadence hosted service brokers and persists events generated during workflow execution. Worker nodes owned and operated by customers execute the coordination and task logic. To facilitate the implementation of worker nodes Cadence provides a client-side library for the Go language. In Cadence, you can code the logical flow of events separately as a workflow and code business logic as activities. The workflow identifies the activities and sequences them, while an activity executes the logic. Dynamic workflow execution graphs - Determine the workflow execution graphs at runtime based on the data you are processing. Cadence does not pre-compute the execution graphs at compile time or at workflow start time. Therefore, you have the ability to write workflows that can dynamically adjust to the amount of data they are processing. If you need to trigger 10 instances of an activity to efficiently process all the data in one run, but only 3 for a subsequent run, you can do that. Child Workflows - Orchestrate the execution of a workflow from within another workflow. Cadence will return the results of the child workflow execution to the parent workflow upon completion of the child workflow. No polling is required in the parent workflow to monitor status of the child workflow, making the process efficient and fault tolerant. Durable Timers - Implement delayed execution of tasks in your workflows that are robust to worker failures. Cadence provides two easy to use APIs, **workflow.Sleep** and **workflow.Timer**, for implementing time based events in your workflows. Cadence ensures that the timer settings are persisted and the events are generated even if workers executing the workflow crash. Signals - Modify/influence the execution path of a running workflow by pushing additional data directly to the workflow using a signal. Via the Signal facility, Cadence provides a mechanism to consume external events directly in workflow code. Task routing - Efficiently process large amounts of data using a Cadence workflow, by caching the data locally on a worker and executing all activities meant to process that data on that same worker. Cadence enables you to choose the worker you want to execute a certain activity by scheduling that activity execution in the worker's specific task-list. Unique workflow ID enforcement - Use business entity IDs for your workflows and let Cadence ensure that only one workflow is running for a particular entity at a time. Cadence implements an atomic "uniqueness check" and ensures that no race conditions are possible that would result in multiple workflow executions for the same workflow ID. Therefore, you can implement your code to attempt to start a workflow without checking if the ID is already in use, even in the cases where only one active execution per workflow ID is desired. Perpetual/ContinueAsNew workflows - Run periodic tasks as a single perpetually running workflow. With the "ContinueAsNew" facility, Cadence allows you to leverage the "unique workflow ID enforcement" feature for periodic workflows. Cadence will complete the current execution and start the new execution atomically, ensuring you get to keep your workflow ID. By starting a new execution Cadence also ensures that workflow execution history does not grow indefinitely for perpetual workflows. At-most once activity execution - Execute non-idempotent activities as part of your workflows. Cadence will not automatically retry activities on failure. For every activity execution Cadence will return a success result, a failure result, or a timeout to the workflow code and let the workflow code determine how each one of those result types should be handled. Asynch Activity Completion - Incorporate human input or thrid-party service asynchronous callbacks into your workflows. Cadence allows a workflow to pause execution on an activity and wait for an external actor to resume it with a callback. During this pause the activity does not have any actively executing code, such as a polling loop, and is merely an entry in the Cadence datastore. Therefore, the workflow is unaffected by any worker failures happening over the duration of the pause. Activity Heartbeating - Detect unexpected failures/crashes and track progress in long running activities early. By configuring your activity to report progress periodically to the Cadence server, you can detect a crash that occurs 10 minutes into an hour-long activity execution much sooner, instead of waiting for the 60-minute execution timeout. The recorded progress before the crash gives you sufficient information to determine whether to restart the activity from the beginning or resume it from the point of failure. Timeouts for activities and workflow executions - Protect against stuck and unresponsive activities and workflows with appropriate timeout values. Cadence requires that timeout values are provided for every activity or workflow invocation. There is no upper bound on the timeout values, so you can set timeouts that span days, weeks, or even months. Visibility - Get a list of all your active and/or completed workflow. Explore the execution history of a particular workflow execution. Cadence provides a set of visibility APIs that allow you, the workflow owner, to monitor past and current workflow executions. Debuggability - Replay any workflow execution history locally under a debugger. The Cadence client library provides an API to allow you to capture a stack trace from any failed workflow execution history.

Package cadence and its subdirectories contain the Cadence client side framework. The Cadence service is a task orchestrator for your application’s tasks. Applications using Cadence can execute a logical flow of tasks, especially long-running business logic, asynchronously or synchronously. They can also scale at runtime on distributed systems. A quick example illustrates its use case. Consider Uber Eats where Cadence manages the entire business flow from placing an order, accepting it, handling shopping cart processes (adding, updating, and calculating cart items), entering the order in a pipeline (for preparing food and coordinating delivery), to scheduling delivery as well as handling payments. Cadence consists of a programming framework (or client library) and a managed service (or backend). The framework enables developers to author and coordinate tasks in Go code. The root cadence package contains common data structures. The subpackages are: The Cadence hosted service brokers and persists events generated during workflow execution. Worker nodes owned and operated by customers execute the coordination and task logic. To facilitate the implementation of worker nodes Cadence provides a client-side library for the Go language. In Cadence, you can code the logical flow of events separately as a workflow and code business logic as activities. The workflow identifies the activities and sequences them, while an activity executes the logic. Dynamic workflow execution graphs - Determine the workflow execution graphs at runtime based on the data you are processing. Cadence does not pre-compute the execution graphs at compile time or at workflow start time. Therefore, you have the ability to write workflows that can dynamically adjust to the amount of data they are processing. If you need to trigger 10 instances of an activity to efficiently process all the data in one run, but only 3 for a subsequent run, you can do that. Child Workflows - Orchestrate the execution of a workflow from within another workflow. Cadence will return the results of the child workflow execution to the parent workflow upon completion of the child workflow. No polling is required in the parent workflow to monitor status of the child workflow, making the process efficient and fault tolerant. Durable Timers - Implement delayed execution of tasks in your workflows that are robust to worker failures. Cadence provides two easy to use APIs, **workflow.Sleep** and **workflow.Timer**, for implementing time based events in your workflows. Cadence ensures that the timer settings are persisted and the events are generated even if workers executing the workflow crash. Signals - Modify/influence the execution path of a running workflow by pushing additional data directly to the workflow using a signal. Via the Signal facility, Cadence provides a mechanism to consume external events directly in workflow code. Task routing - Efficiently process large amounts of data using a Cadence workflow, by caching the data locally on a worker and executing all activities meant to process that data on that same worker. Cadence enables you to choose the worker you want to execute a certain activity by scheduling that activity execution in the worker's specific task-list. Unique workflow ID enforcement - Use business entity IDs for your workflows and let Cadence ensure that only one workflow is running for a particular entity at a time. Cadence implements an atomic "uniqueness check" and ensures that no race conditions are possible that would result in multiple workflow executions for the same workflow ID. Therefore, you can implement your code to attempt to start a workflow without checking if the ID is already in use, even in the cases where only one active execution per workflow ID is desired. Perpetual/ContinueAsNew workflows - Run periodic tasks as a single perpetually running workflow. With the "ContinueAsNew" facility, Cadence allows you to leverage the "unique workflow ID enforcement" feature for periodic workflows. Cadence will complete the current execution and start the new execution atomically, ensuring you get to keep your workflow ID. By starting a new execution Cadence also ensures that workflow execution history does not grow indefinitely for perpetual workflows. At-most once activity execution - Execute non-idempotent activities as part of your workflows. Cadence will not automatically retry activities on failure. For every activity execution Cadence will return a success result, a failure result, or a timeout to the workflow code and let the workflow code determine how each one of those result types should be handled. Asynch Activity Completion - Incorporate human input or thrid-party service asynchronous callbacks into your workflows. Cadence allows a workflow to pause execution on an activity and wait for an external actor to resume it with a callback. During this pause the activity does not have any actively executing code, such as a polling loop, and is merely an entry in the Cadence datastore. Therefore, the workflow is unaffected by any worker failures happening over the duration of the pause. Activity Heartbeating - Detect unexpected failures/crashes and track progress in long running activities early. By configuring your activity to report progress periodically to the Cadence server, you can detect a crash that occurs 10 minutes into an hour-long activity execution much sooner, instead of waiting for the 60-minute execution timeout. The recorded progress before the crash gives you sufficient information to determine whether to restart the activity from the beginning or resume it from the point of failure. Timeouts for activities and workflow executions - Protect against stuck and unresponsive activities and workflows with appropriate timeout values. Cadence requires that timeout values are provided for every activity or workflow invocation. There is no upper bound on the timeout values, so you can set timeouts that span days, weeks, or even months. Visibility - Get a list of all your active and/or completed workflow. Explore the execution history of a particular workflow execution. Cadence provides a set of visibility APIs that allow you, the workflow owner, to monitor past and current workflow executions. Debuggability - Replay any workflow execution history locally under a debugger. The Cadence client library provides an API to allow you to capture a stack trace from any failed workflow execution history.

Package graphs implements some graphs data structures and algorithms

Package textrank is an implementation of Text Rank algorithm in Go with extendable features (automatic summarization, phrase extraction). It supports multithreading by goroutines. The package is under The MIT Licence. If there was a program what could rank book size text's words, phrases and sentences continuously on multiple threads and it would be opened to modifing by objects, written in a simple, secure, static language and if it would be very well documented... Now, here it is. - Find the most important phrases. - Find the most important words. - Find the most important N sentences. - Importance by phrase weights. - Importance by word occurrence. - Find the first N sentences, start from Xth sentence. - Find sentences by phrase chains ordered by position in text. - Access to the whole ranked data. - Support more languages. - Algorithm for weighting can be modified by interface implementation. - Parser can be modified by interface implementation. - Multi thread support. Find the most important phrases: This is the most basic and simplest usage of textrank. All possible pre-defined finder queries: After ranking, the graph contains a lot of valuable data. There are functions in textrank package what contains logic to retrieve those data from the graph. After ranking, the graph contains a lot of valuable data. The GetRank function allows access to the graph and every data can be retrieved from this structure. Adding text continuously: It is possibe to add more text after another texts already have been added. The Ranking function can merge these multiple texts and it can recalculate the weights and all related data. Using different algorithm to ranking text: There are two algorithm has implemented, it is possible to write custom algorithm by Algorithm interface and use it instead of defaults. Using multiple graphs: Graph ID exists because it is possible run multiple independent text ranking processes. Using different non-English languages: Engish is used by default but it is possible to add any language. To use other languages a stop word list is required what you can find here: https://github.com/stopwords-iso Asynchronous usage by goroutines: It is thread safe. Independent graphs can receive texts in the same time and can be extended by more text also in the same time.

Package cadence and its subdirectories contain the Cadence client side framework. The Cadence service is a task orchestrator for your application’s tasks. Applications using Cadence can execute a logical flow of tasks, especially long-running business logic, asynchronously or synchronously. They can also scale at runtime on distributed systems. A quick example illustrates its use case. Consider Uber Eats where Cadence manages the entire business flow from placing an order, accepting it, handling shopping cart processes (adding, updating, and calculating cart items), entering the order in a pipeline (for preparing food and coordinating delivery), to scheduling delivery as well as handling payments. Cadence consists of a programming framework (or client library) and a managed service (or backend). The framework enables developers to author and coordinate tasks in Go code. The root cadence package contains common data structures. The subpackages are: The Cadence hosted service brokers and persists events generated during workflow execution. Worker nodes owned and operated by customers execute the coordination and task logic. To facilitate the implementation of worker nodes Cadence provides a client-side library for the Go language. In Cadence, you can code the logical flow of events separately as a workflow and code business logic as activities. The workflow identifies the activities and sequences them, while an activity executes the logic. Dynamic workflow execution graphs - Determine the workflow execution graphs at runtime based on the data you are processing. Cadence does not pre-compute the execution graphs at compile time or at workflow start time. Therefore, you have the ability to write workflows that can dynamically adjust to the amount of data they are processing. If you need to trigger 10 instances of an activity to efficiently process all the data in one run, but only 3 for a subsequent run, you can do that. Child Workflows - Orchestrate the execution of a workflow from within another workflow. Cadence will return the results of the child workflow execution to the parent workflow upon completion of the child workflow. No polling is required in the parent workflow to monitor status of the child workflow, making the process efficient and fault tolerant. Durable Timers - Implement delayed execution of tasks in your workflows that are robust to worker failures. Cadence provides two easy to use APIs, **workflow.Sleep** and **workflow.Timer**, for implementing time based events in your workflows. Cadence ensures that the timer settings are persisted and the events are generated even if workers executing the workflow crash. Signals - Modify/influence the execution path of a running workflow by pushing additional data directly to the workflow using a signal. Via the Signal facility, Cadence provides a mechanism to consume external events directly in workflow code. Task routing - Efficiently process large amounts of data using a Cadence workflow, by caching the data locally on a worker and executing all activities meant to process that data on that same worker. Cadence enables you to choose the worker you want to execute a certain activity by scheduling that activity execution in the worker's specific task-list. Unique workflow ID enforcement - Use business entity IDs for your workflows and let Cadence ensure that only one workflow is running for a particular entity at a time. Cadence implements an atomic "uniqueness check" and ensures that no race conditions are possible that would result in multiple workflow executions for the same workflow ID. Therefore, you can implement your code to attempt to start a workflow without checking if the ID is already in use, even in the cases where only one active execution per workflow ID is desired. Perpetual/ContinueAsNew workflows - Run periodic tasks as a single perpetually running workflow. With the "ContinueAsNew" facility, Cadence allows you to leverage the "unique workflow ID enforcement" feature for periodic workflows. Cadence will complete the current execution and start the new execution atomically, ensuring you get to keep your workflow ID. By starting a new execution Cadence also ensures that workflow execution history does not grow indefinitely for perpetual workflows. At-most once activity execution - Execute non-idempotent activities as part of your workflows. Cadence will not automatically retry activities on failure. For every activity execution Cadence will return a success result, a failure result, or a timeout to the workflow code and let the workflow code determine how each one of those result types should be handled. Asynch Activity Completion - Incorporate human input or thrid-party service asynchronous callbacks into your workflows. Cadence allows a workflow to pause execution on an activity and wait for an external actor to resume it with a callback. During this pause the activity does not have any actively executing code, such as a polling loop, and is merely an entry in the Cadence datastore. Therefore, the workflow is unaffected by any worker failures happening over the duration of the pause. Activity Heartbeating - Detect unexpected failures/crashes and track progress in long running activities early. By configuring your activity to report progress periodically to the Cadence server, you can detect a crash that occurs 10 minutes into an hour-long activity execution much sooner, instead of waiting for the 60-minute execution timeout. The recorded progress before the crash gives you sufficient information to determine whether to restart the activity from the beginning or resume it from the point of failure. Timeouts for activities and workflow executions - Protect against stuck and unresponsive activities and workflows with appropriate timeout values. Cadence requires that timeout values are provided for every activity or workflow invocation. There is no upper bound on the timeout values, so you can set timeouts that span days, weeks, or even months. Visibility - Get a list of all your active and/or completed workflow. Explore the execution history of a particular workflow execution. Cadence provides a set of visibility APIs that allow you, the workflow owner, to monitor past and current workflow executions. Debuggability - Replay any workflow execution history locally under a debugger. The Cadence client library provides an API to allow you to capture a stack trace from any failed workflow execution history.

Package jsonapi provides a serializer and deserializer for jsonapi.org spec payloads. You can keep your model structs as is and use struct field tags to indicate to jsonapi how you want your response built or your request deserialzied. What about my relationships? jsonapi supports relationships out of the box and will even side load them in your response into an "included" array--that contains associated objects. jsonapi uses StructField tags to annotate the structs fields that you already have and use in your app and then reads and writes jsonapi.org output based on the instructions you give the library in your jsonapi tags. Example structs using a Blog > Post > Comment structure, jsonapi Tag Reference Value, primary: "primary,<type field output>" This indicates that this is the primary key field for this struct type. Tag value arguments are comma separated. The first argument must be, "primary", and the second must be the name that should appear in the "type" field for all data objects that represent this type of model. Value, attr: "attr,<key name in attributes hash>[,<extra arguments>]" These fields' values should end up in the "attribute" hash for a record. The first argument must be, "attr', and the second should be the name for the key to display in the "attributes" hash for that record. The following extra arguments are also supported: "omitempty": excludes the fields value from the "attribute" hash. "iso8601": uses the ISO8601 timestamp format when serialising or deserialising the time.Time value. Value, relation: "relation,<key name in relationships hash>" Relations are struct fields that represent a one-to-one or one-to-many to other structs. jsonapi will traverse the graph of relationships and marshal or unmarshal records. The first argument must be, "relation", and the second should be the name of the relationship, used as the key in the "relationships" hash for the record. Use the methods below to Marshal and Unmarshal jsonapi.org json payloads. Visit the readme at https://github.com/google/jsonapi

Package jsonapi provides a serializer and deserializer for jsonapi.org spec payloads. You can keep your model structs as is and use struct field tags to indicate to jsonapi how you want your response built or your request deserialized. What about my relationships? jsonapi supports relationships out of the box and will even side load them in your response into an "included" array--that contains associated objects. jsonapi uses StructField tags to annotate the structs fields that you already have and use in your app and then reads and writes jsonapi.org output based on the instructions you give the library in your jsonapi tags. Example structs using a Blog > Post > Comment structure, jsonapi Tag Reference Value, primary: "primary,<type field output>" This indicates that this is the primary key field for this struct type. Tag value arguments are comma separated. The first argument must be, "primary", and the second must be the name that should appear in the "type" field for all data objects that represent this type of model. Value, attr: "attr,<key name in attributes hash>[,<extra arguments>]" These fields' values should end up in the "attribute" hash for a record. The first argument must be, "attr', and the second should be the name for the key to display in the "attributes" hash for that record. The following extra arguments are also supported: "omitempty": excludes the fields value from the "attribute" hash. "iso8601": uses the ISO8601 timestamp format when serialising or deserialising the time.Time value. Value, relation: "relation,<key name in relationships hash>" Relations are struct fields that represent a one-to-one or one-to-many to other structs. jsonapi will traverse the graph of relationships and marshal or unmarshal records. The first argument must be, "relation", and the second should be the name of the relationship, used as the key in the "relationships" hash for the record. Use the methods below to Marshal and Unmarshal jsonapi.org json payloads. Visit the readme at https://github.com/google/jsonapi

Package jsonapi provides a serializer and deserializer for jsonapi.org spec payloads. You can keep your model structs as is and use struct field tags to indicate to jsonapi how you want your response built or your request deserialzied. What about my relationships? jsonapi supports relationships out of the box and will even side load them in your response into an "included" array--that contains associated objects. jsonapi uses StructField tags to annotate the structs fields that you already have and use in your app and then reads and writes jsonapi.org output based on the instructions you give the library in your jsonapi tags. Example structs using a Blog > Post > Comment structure, jsonapi Tag Reference Value, primary: "primary,<type field output>" This indicates that this is the primary key field for this struct type. Tag value arguments are comma separated. The first argument must be, "primary", and the second must be the name that should appear in the "type" field for all data objects that represent this type of model. Value, attr: "attr,<key name in attributes hash>[,<extra arguments>]" These fields' values should end up in the "attribute" hash for a record. The first argument must be, "attr', and the second should be the name for the key to display in the "attributes" hash for that record. The following extra arguments are also supported: "omitempty": excludes the fields value from the "attribute" hash. "iso8601": uses the ISO8601 timestamp format when serialising or deserialising the time.Time value. Value, relation: "relation,<key name in relationships hash>" Relations are struct fields that represent a one-to-one or one-to-many to other structs. jsonapi will traverse the graph of relationships and marshal or unmarshal records. The first argument must be, "relation", and the second should be the name of the relationship, used as the key in the "relationships" hash for the record. Use the methods below to Marshal and Unmarshal jsonapi.org json payloads. Visit the readme at https://github.com/google/jsonapi

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value Int64, Float64, Bool, Name, and so on return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package jsonapi provides a serializer and deserializer for jsonapi.org spec payloads. You can keep your model structs as is and use struct field tags to indicate to jsonapi how you want your response built or your request deserialzied. What about my relationships? jsonapi supports relationships out of the box and will even side load them in your response into an "included" array--that contains associated objects. jsonapi uses StructField tags to annotate the structs fields that you already have and use in your app and then reads and writes jsonapi.org output based on the instructions you give the library in your jsonapi tags. Example structs using a Blog > Post > Comment structure, jsonapi Tag Reference Value, primary: "primary,<type field output>" This indicates that this is the primary key field for this struct type. Tag value arguments are comma separated. The first argument must be, "primary", and the second must be the name that should appear in the "type" field for all data objects that represent this type of model. Value, attr: "attr,<key name in attributes hash>[,<extra arguments>]" These fields' values should end up in the "attribute" hash for a record. The first argument must be, "attr', and the second should be the name for the key to display in the "attributes" hash for that record. The following extra arguments are also supported: "omitempty": excludes the fields value from the "attribute" hash. "iso8601": uses the ISO8601 timestamp format when serialising or deserialising the time.Time value. Value, relation: "relation,<key name in relationships hash>" Relations are struct fields that represent a one-to-one or one-to-many to other structs. jsonapi will traverse the graph of relationships and marshal or unmarshal records. The first argument must be, "relation", and the second should be the name of the relationship, used as the key in the "relationships" hash for the record. Use the methods below to Marshal and Unmarshal jsonapi.org json payloads. Visit the readme at https://github.com/Long-Wan/jsonapi

Package dags visualizes a DAG graph into a structured, layered planer map.

Package cadence and its subdirectories contain the Cadence client side framework. The Cadence service is a task orchestrator for your application’s tasks. Applications using Cadence can execute a logical flow of tasks, especially long-running business logic, asynchronously or synchronously. They can also scale at runtime on distributed systems. A quick example illustrates its use case. Consider Uber Eats where Cadence manages the entire business flow from placing an order, accepting it, handling shopping cart processes (adding, updating, and calculating cart items), entering the order in a pipeline (for preparing food and coordinating delivery), to scheduling delivery as well as handling payments. Cadence consists of a programming framework (or client library) and a managed service (or backend). The framework enables developers to author and coordinate tasks in Go code. The root cadence package contains common data structures. The subpackages are: The Cadence hosted service brokers and persists events generated during workflow execution. Worker nodes owned and operated by customers execute the coordination and task logic. To facilitate the implementation of worker nodes Cadence provides a client-side library for the Go language. In Cadence, you can code the logical flow of events separately as a workflow and code business logic as activities. The workflow identifies the activities and sequences them, while an activity executes the logic. Dynamic workflow execution graphs - Determine the workflow execution graphs at runtime based on the data you are processing. Cadence does not pre-compute the execution graphs at compile time or at workflow start time. Therefore, you have the ability to write workflows that can dynamically adjust to the amount of data they are processing. If you need to trigger 10 instances of an activity to efficiently process all the data in one run, but only 3 for a subsequent run, you can do that. Child Workflows - Orchestrate the execution of a workflow from within another workflow. Cadence will return the results of the child workflow execution to the parent workflow upon completion of the child workflow. No polling is required in the parent workflow to monitor status of the child workflow, making the process efficient and fault tolerant. Durable Timers - Implement delayed execution of tasks in your workflows that are robust to worker failures. Cadence provides two easy to use APIs, **workflow.Sleep** and **workflow.Timer**, for implementing time based events in your workflows. Cadence ensures that the timer settings are persisted and the events are generated even if workers executing the workflow crash. Signals - Modify/influence the execution path of a running workflow by pushing additional data directly to the workflow using a signal. Via the Signal facility, Cadence provides a mechanism to consume external events directly in workflow code. Task routing - Efficiently process large amounts of data using a Cadence workflow, by caching the data locally on a worker and executing all activities meant to process that data on that same worker. Cadence enables you to choose the worker you want to execute a certain activity by scheduling that activity execution in the worker's specific task-list. Unique workflow ID enforcement - Use business entity IDs for your workflows and let Cadence ensure that only one workflow is running for a particular entity at a time. Cadence implements an atomic "uniqueness check" and ensures that no race conditions are possible that would result in multiple workflow executions for the same workflow ID. Therefore, you can implement your code to attempt to start a workflow without checking if the ID is already in use, even in the cases where only one active execution per workflow ID is desired. Perpetual/ContinueAsNew workflows - Run periodic tasks as a single perpetually running workflow. With the "ContinueAsNew" facility, Cadence allows you to leverage the "unique workflow ID enforcement" feature for periodic workflows. Cadence will complete the current execution and start the new execution atomically, ensuring you get to keep your workflow ID. By starting a new execution Cadence also ensures that workflow execution history does not grow indefinitely for perpetual workflows. At-most once activity execution - Execute non-idempotent activities as part of your workflows. Cadence will not automatically retry activities on failure. For every activity execution Cadence will return a success result, a failure result, or a timeout to the workflow code and let the workflow code determine how each one of those result types should be handled. Asynch Activity Completion - Incorporate human input or thrid-party service asynchronous callbacks into your workflows. Cadence allows a workflow to pause execution on an activity and wait for an external actor to resume it with a callback. During this pause the activity does not have any actively executing code, such as a polling loop, and is merely an entry in the Cadence datastore. Therefore, the workflow is unaffected by any worker failures happening over the duration of the pause. Activity Heartbeating - Detect unexpected failures/crashes and track progress in long running activities early. By configuring your activity to report progress periodically to the Cadence server, you can detect a crash that occurs 10 minutes into an hour-long activity execution much sooner, instead of waiting for the 60-minute execution timeout. The recorded progress before the crash gives you sufficient information to determine whether to restart the activity from the beginning or resume it from the point of failure. Timeouts for activities and workflow executions - Protect against stuck and unresponsive activities and workflows with appropriate timeout values. Cadence requires that timeout values are provided for every activity or workflow invocation. There is no upper bound on the timeout values, so you can set timeouts that span days, weeks, or even months. Visibility - Get a list of all your active and/or completed workflow. Explore the execution history of a particular workflow execution. Cadence provides a set of visibility APIs that allow you, the workflow owner, to monitor past and current workflow executions. Debuggability - Replay any workflow execution history locally under a debugger. The Cadence client library provides an API to allow you to capture a stack trace from any failed workflow execution history.

Package jsonapi provides a serializer and deserializer for jsonapi.org spec payloads. You can keep your model structs as is and use struct field tags to indicate to jsonapi how you want your response built or your request deserialzied. What about my relationships? jsonapi supports relationships out of the box and will even side load them in your response into an "included" array--that contains associated objects. jsonapi uses StructField tags to annotate the structs fields that you already have and use in your app and then reads and writes jsonapi.org output based on the instructions you give the library in your jsonapi tags. Example structs using a Blog > Post > Comment structure, jsonapi Tag Reference Value, primary: "primary,<type field output>" This indicates that this is the primary key field for this struct type. Tag value arguments are comma separated. The first argument must be, "primary", and the second must be the name that should appear in the "type" field for all data objects that represent this type of model. Value, attr: "attr,<key name in attributes hash>[,<extra arguments>]" These fields' values should end up in the "attribute" hash for a record. The first argument must be, "attr', and the second should be the name for the key to display in the "attributes" hash for that record. The following extra arguments are also supported: "omitempty": excludes the fields value from the "attribute" hash. "iso8601": uses the ISO8601 timestamp format when serialising or deserialising the time.Time value. Value, relation: "relation,<key name in relationships hash>" Relations are struct fields that represent a one-to-one or one-to-many to other structs. jsonapi will traverse the graph of relationships and marshal or unmarshal records. The first argument must be, "relation", and the second should be the name of the relationship, used as the key in the "relationships" hash for the record. Use the methods below to Marshal and Unmarshal jsonapi.org json payloads. Visit the readme at https://github.com/moltin/jsonapi

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package jsonapi provides a serializer and deserializer for jsonapi.org_rest spec payloads. You can keep your model structs as is and use struct field tags to indicate to jsonapi how you want your response built or your request deserialized. What about my relationships? jsonapi supports relationships out of the box and will even side load them in your response into an "included" array--that contains associated objects. jsonapi uses StructField tags to annotate the structs fields that you already have and use in your app and then reads and writes jsonapi.org_rest output based on the instructions you give the library in your jsonapi tags. Example structs using a Blog > Post > Comment structure, jsonapi Tag Reference Value, primary: "primary,<type field output>" This indicates that this is the primary key field for this struct type. Tag value arguments are comma separated. The first argument must be, "primary", and the second must be the name that should appear in the "type" field for all data objects that represent this type of model. Value, attr: "attr,<key name in attributes hash>[,<extra arguments>]" These fields' values should end up in the "attribute" hash for a record. The first argument must be, "attr', and the second should be the name for the key to display in the "attributes" hash for that record. The following extra arguments are also supported: "omitempty": excludes the fields value from the "attribute" hash. "iso8601": uses the ISO8601 timestamp format when serialising or deserialising the time.Time value. Value, relation: "relation,<key name in relationships hash>" Relations are struct fields that represent a one-to-one or one-to-many to other structs. jsonapi will traverse the graph of relationships and marshal or unmarshal records. The first argument must be, "relation", and the second should be the name of the relationship, used as the key in the "relationships" hash for the record. Use the methods below to Marshal and Unmarshal jsonapi.org_rest json payloads. Visit the readme at https://github.com/google/jsonapi

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package cadence and its subdirectories contain the Cadence client side framework. The Cadence service is a task orchestrator for your application’s tasks. Applications using Cadence can execute a logical flow of tasks, especially long-running business logic, asynchronously or synchronously. They can also scale at runtime on distributed systems. A quick example illustrates its use case. Consider Uber Eats where Cadence manages the entire business flow from placing an order, accepting it, handling shopping cart processes (adding, updating, and calculating cart items), entering the order in a pipeline (for preparing food and coordinating delivery), to scheduling delivery as well as handling payments. Cadence consists of a programming framework (or client library) and a managed service (or backend). The framework enables developers to author and coordinate tasks in Go code. The root cadence package contains common data structures. The subpackages are: The Cadence hosted service brokers and persists events generated during workflow execution. Worker nodes owned and operated by customers execute the coordination and task logic. To facilitate the implementation of worker nodes Cadence provides a client-side library for the Go language. In Cadence, you can code the logical flow of events separately as a workflow and code business logic as activities. The workflow identifies the activities and sequences them, while an activity executes the logic. Dynamic workflow execution graphs - Determine the workflow execution graphs at runtime based on the data you are processing. Cadence does not pre-compute the execution graphs at compile time or at workflow start time. Therefore, you have the ability to write workflows that can dynamically adjust to the amount of data they are processing. If you need to trigger 10 instances of an activity to efficiently process all the data in one run, but only 3 for a subsequent run, you can do that. Child Workflows - Orchestrate the execution of a workflow from within another workflow. Cadence will return the results of the child workflow execution to the parent workflow upon completion of the child workflow. No polling is required in the parent workflow to monitor status of the child workflow, making the process efficient and fault tolerant. Durable Timers - Implement delayed execution of tasks in your workflows that are robust to worker failures. Cadence provides two easy to use APIs, **workflow.Sleep** and **workflow.Timer**, for implementing time based events in your workflows. Cadence ensures that the timer settings are persisted and the events are generated even if workers executing the workflow crash. Signals - Modify/influence the execution path of a running workflow by pushing additional data directly to the workflow using a signal. Via the Signal facility, Cadence provides a mechanism to consume external events directly in workflow code. Task routing - Efficiently process large amounts of data using a Cadence workflow, by caching the data locally on a worker and executing all activities meant to process that data on that same worker. Cadence enables you to choose the worker you want to execute a certain activity by scheduling that activity execution in the worker's specific task-list. Unique workflow ID enforcement - Use business entity IDs for your workflows and let Cadence ensure that only one workflow is running for a particular entity at a time. Cadence implements an atomic "uniqueness check" and ensures that no race conditions are possible that would result in multiple workflow executions for the same workflow ID. Therefore, you can implement your code to attempt to start a workflow without checking if the ID is already in use, even in the cases where only one active execution per workflow ID is desired. Perpetual/ContinueAsNew workflows - Run periodic tasks as a single perpetually running workflow. With the "ContinueAsNew" facility, Cadence allows you to leverage the "unique workflow ID enforcement" feature for periodic workflows. Cadence will complete the current execution and start the new execution atomically, ensuring you get to keep your workflow ID. By starting a new execution Cadence also ensures that workflow execution history does not grow indefinitely for perpetual workflows. At-most once activity execution - Execute non-idempotent activities as part of your workflows. Cadence will not automatically retry activities on failure. For every activity execution Cadence will return a success result, a failure result, or a timeout to the workflow code and let the workflow code determine how each one of those result types should be handled. Asynch Activity Completion - Incorporate human input or thrid-party service asynchronous callbacks into your workflows. Cadence allows a workflow to pause execution on an activity and wait for an external actor to resume it with a callback. During this pause the activity does not have any actively executing code, such as a polling loop, and is merely an entry in the Cadence datastore. Therefore, the workflow is unaffected by any worker failures happening over the duration of the pause. Activity Heartbeating - Detect unexpected failures/crashes and track progress in long running activities early. By configuring your activity to report progress periodically to the Cadence server, you can detect a crash that occurs 10 minutes into an hour-long activity execution much sooner, instead of waiting for the 60-minute execution timeout. The recorded progress before the crash gives you sufficient information to determine whether to restart the activity from the beginning or resume it from the point of failure. Timeouts for activities and workflow executions - Protect against stuck and unresponsive activities and workflows with appropriate timeout values. Cadence requires that timeout values are provided for every activity or workflow invocation. There is no upper bound on the timeout values, so you can set timeouts that span days, weeks, or even months. Visibility - Get a list of all your active and/or completed workflow. Explore the execution history of a particular workflow execution. Cadence provides a set of visibility APIs that allow you, the workflow owner, to monitor past and current workflow executions. Debuggability - Replay any workflow execution history locally under a debugger. The Cadence client library provides an API to allow you to capture a stack trace from any failed workflow execution history.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pq implements a priority queue data structure on top of container/heap. As an addition to regular operations, it allows an update of an items priority, allowing the queue to be used in graph search algorithms like Dijkstra's algorithm. Computational complexities of operations are mainly determined by container/heap. In addition, a map of items is maintained, allowing O(1) lookup needed for priority updates, which themselves are O(log n).

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package jsonapi provides a serializer and deserializer for jsonapi.org spec payloads. You can keep your model structs as is and use struct field tags to indicate to jsonapi how you want your response built or your request deserialzied. What about my relationships? jsonapi supports relationships out of the box and will even side load them in your response into an "included" array--that contains associated objects. jsonapi uses StructField tags to annotate the structs fields that you already have and use in your app and then reads and writes jsonapi.org output based on the instructions you give the library in your jsonapi tags. Example structs using a Blog > Post > Comment structure, jsonapi Tag Reference Value, primary: "primary,<type field output>" This indicates that this is the primary key field for this struct type. Tag value arguments are comma separated. The first argument must be, "primary", and the second must be the name that should appear in the "type" field for all data objects that represent this type of model. Value, attr: "attr,<key name in attributes hash>[,<extra arguments>]" These fields' values should end up in the "attribute" hash for a record. The first argument must be, "attr', and the second should be the name for the key to display in the the "attributes" hash for that record. The following extra arguments are also supported: "omitempty": excludes the fields value from the "attribute" hash. "iso8601": uses the ISO8601 timestamp format when serialising or deserialising the time.Time value. Value, relation: "relation,<key name in relationships hash>" Relations are struct fields that represent a one-to-one or one-to-many to other structs. jsonapi will traverse the graph of relationships and marshal or unmarshal records. The first argument must be, "relation", and the second should be the name of the relationship, used as the key in the "relationships" hash for the record. Use the methods below to Marshal and Unmarshal jsonapi.org json payloads. Visit the readme at https://github.com/google/jsonapi

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package jsonapi provides a serializer and deserializer for jsonapi.org spec payloads. You can keep your model structs as is and use struct field tags to indicate to jsonapi how you want your response built or your request deserialzied. What about my relationships? jsonapi supports relationships out of the box and will even side load them in your response into an "included" array--that contains associated objects. jsonapi uses StructField tags to annotate the structs fields that you already have and use in your app and then reads and writes jsonapi.org output based on the instructions you give the library in your jsonapi tags. Example structs using a Blog > Post > Comment structure, jsonapi Tag Reference Value, primary: "primary,<type field output>" This indicates that this is the primary key field for this struct type. Tag value arguments are comma separated. The first argument must be, "primary", and the second must be the name that should appear in the "type" field for all data objects that represent this type of model. Value, attr: "attr,<key name in attributes hash>[,<extra arguments>]" These fields' values should end up in the "attribute" hash for a record. The first argument must be, "attr', and the second should be the name for the key to display in the the "attributes" hash for that record. The following extra arguments are also supported: "omitempty": excludes the fields value from the "attribute" hash. "iso8601": uses the ISO8601 timestamp format when serialising or deserialising the time.Time value. Value, relation: "relation,<key name in relationships hash>" Relations are struct fields that represent a one-to-one or one-to-many to other structs. jsonapi will traverse the graph of relationships and marshal or unmarshal records. The first argument must be, "relation", and the second should be the name of the relationship, used as the key in the "relationships" hash for the record. Use the methods below to Marshal and Unmarshal jsonapi.org json payloads. Visit the readme at https://github.com/google/jsonapi

Package jsonapi provides a serializer and deserializer for jsonapi.org spec payloads. You can keep your model structs as is and use struct field tags to indicate to jsonapi how you want your response built or your request deserialzied. What about my relationships? jsonapi supports relationships out of the box and will even side load them in your response into an "included" array--that contains associated objects. jsonapi uses StructField tags to annotate the structs fields that you already have and use in your app and then reads and writes jsonapi.org output based on the instructions you give the library in your jsonapi tags. Example structs using a Blog > Post > Comment structure, jsonapi Tag Reference Value, primary: "primary,<type field output>" This indicates that this is the primary key field for this struct type. Tag value arguments are comma separated. The first argument must be, "primary", and the second must be the name that should appear in the "type" field for all data objects that represent this type of model. Value, attr: "attr,<key name in attributes hash>[,<extra arguments>]" These fields' values should end up in the "attribute" hash for a record. The first argument must be, "attr', and the second should be the name for the key to display in the the "attributes" hash for that record. The following extra arguments are also supported: "omitempty": excludes the fields value from the "attribute" hash. "iso8601": uses the ISO8601 timestamp format when serialising or deserialising the time.Time value. Value, relation: "relation,<key name in relationships hash>" Relations are struct fields that represent a one-to-one or one-to-many to other structs. jsonapi will traverse the graph of relationships and marshal or unmarshal records. The first argument must be, "relation", and the second should be the name of the relationship, used as the key in the "relationships" hash for the record. Use the methods below to Marshal and Unmarshal jsonapi.org json payloads. Visit the readme at https://github.com/google/jsonapi

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package jsonapi provides a serializer and deserializer for jsonapi.org spec payloads. You can keep your model structs as is and use struct field tags to indicate to jsonapi how you want your response built or your request deserialzied. What about my relationships? jsonapi supports relationships out of the box and will even side load them in your response into an "included" array--that contains associated objects. jsonapi uses StructField tags to annotate the structs fields that you already have and use in your app and then reads and writes jsonapi.org output based on the instructions you give the library in your jsonapi tags. Example structs using a Blog > Post > Comment structure, jsonapi Tag Reference Value, primary: "primary,<type field output>" This indicates that this is the primary key field for this struct type. Tag value arguments are comma separated. The first argument must be, "primary", and the second must be the name that should appear in the "type" field for all data objects that represent this type of model. Value, attr: "attr,<key name in attributes hash>[,<extra arguments>]" These fields' values should end up in the "attribute" hash for a record. The first argument must be, "attr', and the second should be the name for the key to display in the "attributes" hash for that record. The following extra arguments are also supported: "omitempty": excludes the fields value from the "attribute" hash. "iso8601": uses the ISO8601 timestamp format when serialising or deserialising the time.Time value. Value, relation: "relation,<key name in relationships hash>" Relations are struct fields that represent a one-to-one or one-to-many to other structs. jsonapi will traverse the graph of relationships and marshal or unmarshal records. The first argument must be, "relation", and the second should be the name of the relationship, used as the key in the "relationships" hash for the record. Use the methods below to Marshal and Unmarshal jsonapi.org json payloads. Visit the readme at https://github.com/google/jsonapi

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.