Shopping APIs

Package monkit is a flexible code instrumenting and data collection library. I'm going to try and sell you as fast as I can on this library. Example usage We've got tools that capture distribution information (including quantiles) about int64, float64, and bool types. We have tools that capture data about events (we've got meters for deltas, rates, etc). We have rich tools for capturing information about tasks and functions, and literally anything that can generate a name and a number. Almost just as importantly, the amount of boilerplate and code you have to write to get these features is very minimal. Data that's hard to measure probably won't get measured. This data can be collected and sent to Graphite (http://graphite.wikidot.com/) or any other time-series database. Here's a selection of live stats from one of our storage nodes: This library generates call graphs of your live process for you. These call graphs aren't created through sampling. They're full pictures of all of the interesting functions you've annotated, along with quantile information about their successes, failures, how often they panic, return an error (if so instrumented), how many are currently running, etc. The data can be returned in dot format, in json, in text, and can be about just the functions that are currently executing, or all the functions the monitoring system has ever seen. Here's another example of one of our production nodes: https://raw.githubusercontent.com/spacemonkeygo/monkit/master/images/callgraph2.png This library generates trace graphs of your live process for you directly, without requiring standing up some tracing system such as Zipkin (though you can do that too). Inspired by Google's Dapper (http://research.google.com/pubs/pub36356.html) and Twitter's Zipkin (http://zipkin.io), we have process-internal trace graphs, triggerable by a number of different methods. You get this trace information for free whenever you use Go contexts (https://blog.golang.org/context) and function monitoring. The output formats are svg and json. Additionally, the library supports trace observation plugins, and we've written a plugin that sends this data to Zipkin (http://github.com/spacemonkeygo/monkit-zipkin). https://raw.githubusercontent.com/spacemonkeygo/monkit/master/images/trace.png Before our crazy Go rewrite of everything (https://www.spacemonkey.com/blog/posts/go-space-monkey) (and before we had even seen Google's Dapper paper), we were a Python shop, and all of our "interesting" functions were decorated with a helper that collected timing information and sent it to Graphite. When we transliterated to Go, we wanted to preserve that functionality, so the first version of our monitoring package was born. Over time it started to get janky, especially as we found Zipkin and started adding tracing functionality to it. We rewrote all of our Go code to use Google contexts, and then realized we could get call graph information. We decided a refactor and then an all-out rethinking of our monitoring package was best, and so now we have this library. Sometimes you really want callstack contextual information without having to pass arguments through everything on the call stack. In other languages, many people implement this with thread-local storage. Example: let's say you have written a big system that responds to user requests. All of your libraries log using your log library. During initial development everything is easy to debug, since there's low user load, but now you've scaled and there's OVER TEN USERS and it's kind of hard to tell what log lines were caused by what. Wouldn't it be nice to add request ids to all of the log lines kicked off by that request? Then you could grep for all log lines caused by a specific request id. Geez, it would suck to have to pass all contextual debugging information through all of your callsites. Google solved this problem by always passing a context.Context interface through from call to call. A Context is basically just a mapping of arbitrary keys to arbitrary values that users can add new values for. This way if you decide to add a request context, you can add it to your Context and then all callsites that decend from that place will have the new data in their contexts. It is admittedly very verbose to add contexts to every function call. Painfully so. I hope to write more about it in the future, but Google also wrote up their thoughts about it (https://blog.golang.org/context), which you can go read. For now, just swallow your disgust and let's keep moving. Let's make a super simple Varnish (https://www.varnish-cache.org/) clone. Open up gedit! (Okay just kidding, open whatever text editor you want.) For this motivating program, we won't even add the caching, though there's comments for where to add it if you'd like. For now, let's just make a barebones system that will proxy HTTP requests. We'll call it VLite, but maybe we should call it VReallyLite. Run and build this and open localhost:8080 in your browser. If you use the default proxy target, it should inform you that the world hasn't been destroyed yet. The first thing you'll want to do is add the small amount of boilerplate to make the instrumentation we're going to add to your process observable later. Import the basic monkit packages: and then register environmental statistics and kick off a goroutine in your main method to serve debug requests: Rebuild, and then check out localhost:9000/stats (or localhost:9000/stats/json, if you prefer) in your browser! Remember what I said about Google's contexts (https://blog.golang.org/context)? It might seem a bit overkill for such a small project, but it's time to add them. To help out here, I've created a library that constructs contexts for you for incoming HTTP requests. Nothing that's about to happen requires my webhelp library (https://godoc.org/github.com/jtolds/webhelp), but here is the code now refactored to receive and pass contexts through our two per-request calls. You can create a new context for a request however you want. One reason to use something like webhelp is that the cancelation feature of Contexts is hooked up to the HTTP request getting canceled. Let's start to get statistics about how many requests we receive! First, this package (main) will need to get a monitoring Scope. Add this global definition right after all your imports, much like you'd create a logger with many logging libraries: Now, make the error return value of HandleHTTP named (so, (err error)), and add this defer line as the very first instruction of HandleHTTP: Let's also add the same line (albeit modified for the lack of error) to Proxy, replacing &err with nil: You should now have something like: We'll unpack what's going on here, but for now: For this new funcs dataset, if you want a graph, you can download a dot graph at localhost:9000/funcs/dot and json information from localhost:9000/funcs/json. You should see something like: with a similar report for the Proxy method, or a graph like: https://raw.githubusercontent.com/spacemonkeygo/monkit/master/images/handlehttp.png This data reports the overall callgraph of execution for known traces, along with how many of each function are currently running, the most running concurrently (the highwater), how many were successful along with quantile timing information, how many errors there were (with quantile timing information if applicable), and how many panics there were. Since the Proxy method isn't capturing a returned err value, and since HandleHTTP always returns nil, this example won't ever have failures. If you're wondering about the success count being higher than you expected, keep in mind your browser probably requested a favicon.ico. Cool, eh? How it works is an interesting line of code - there's three function calls. If you look at the Go spec, all of the function calls will run at the time the function starts except for the very last one. The first function call, mon.Task(), creates or looks up a wrapper around a Func. You could get this yourself by requesting mon.Func() inside of the appropriate function or mon.FuncNamed(). Both mon.Task() and mon.Func() are inspecting runtime.Caller to determine the name of the function. Because this is a heavy operation, you can actually store the result of mon.Task() and reuse it somehow else if you prefer, so instead of you could instead use which is more performant every time after the first time. runtime.Caller only gets called once. Careful! Don't use the same myFuncMon in different functions unless you want to screw up your statistics! The second function call starts all the various stop watches and bookkeeping to keep track of the function. It also mutates the context pointer it's given to extend the context with information about what current span (in Zipkin parlance) is active. Notably, you *can* pass nil for the context if you really don't want a context. You just lose callgraph information. The last function call stops all the stop watches ad makes a note of any observed errors or panics (it repanics after observing them). Turns out, we don't even need to change our program anymore to get rich tracing information! Open your browser and go to localhost:9000/trace/svg?regex=HandleHTTP. It won't load, and in fact, it's waiting for you to open another tab and refresh localhost:8080 again. Once you retrigger the actual application behavior, the trace regex will capture a trace starting on the first function that matches the supplied regex, and return an svg. Go back to your first tab, and you should see a relatively uninteresting but super promising svg. Let's make the trace more interesting. Add a to your HandleHTTP method, rebuild, and restart. Load localhost:8080, then start a new request to your trace URL, then reload localhost:8080 again. Flip back to your trace, and you should see that the Proxy method only takes a portion of the time of HandleHTTP! https://cdn.rawgit.com/spacemonkeygo/monkit/master/images/trace.svg There's multiple ways to select a trace. You can select by regex using the preselect method (default), which first evaluates the regex on all known functions for sanity checking. Sometimes, however, the function you want to trace may not yet be known to monkit, in which case you'll want to turn preselection off. You may have a bad regex, or you may be in this case if you get the error "Bad Request: regex preselect matches 0 functions." Another way to select a trace is by providing a trace id, which we'll get to next! Make sure to check out what the addition of the time.Sleep call did to the other reports. It's easy to write plugins for monkit! Check out our first one that exports data to Zipkin (http://zipkin.io/)'s Scribe API: https://github.com/spacemonkeygo/monkit-zipkin We plan to have more (for HTrace, OpenTracing, etc, etc), soon!

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 temporal and its subdirectories contain the Temporal client side framework. The Temporal service is a task orchestrator for your application’s tasks. Applications using Temporal 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 Temporal 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. Temporal 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 temporal package contains common data structures. The subpackages are: The Temporal 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 Temporal provides a client-side library for the Go language. In Temporal, 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. Temporal 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. Temporal 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. Temporal provides two easy to use APIs, **workflow.Sleep** and **workflow.Timer**, for implementing time based events in your workflows. Temporal 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, Temporal provides a mechanism to consume external events directly in workflow code. Task routing - Efficiently process large amounts of data using a Temporal workflow, by caching the data locally on a worker and executing all activities meant to process that data on that same worker. Temporal enables you to choose the worker you want to execute a certain activity by scheduling that activity execution in the worker's specific task queue. Unique workflow ID enforcement - Use business entity IDs for your workflows and let Temporal ensure that only one workflow is running for a particular entity at a time. Temporal 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, Temporal allows you to leverage the "unique workflow ID enforcement" feature for periodic workflows. Temporal will complete the current execution and start the new execution atomically, ensuring you get to keep your workflow ID. By starting a new execution Temporal 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. Temporal will not automatically retry activities on failure. For every activity execution Temporal 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. Temporal 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 Temporal 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 Temporal 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. Temporal 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. Temporal 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 Temporal client library provides an API to allow you to capture a stack trace from any failed workflow execution history.

Package monkit is a flexible code instrumenting and data collection library. I'm going to try and sell you as fast as I can on this library. Example usage We've got tools that capture distribution information (including quantiles) about int64, float64, and bool types. We have tools that capture data about events (we've got meters for deltas, rates, etc). We have rich tools for capturing information about tasks and functions, and literally anything that can generate a name and a number. Almost just as importantly, the amount of boilerplate and code you have to write to get these features is very minimal. Data that's hard to measure probably won't get measured. This data can be collected and sent to Graphite (http://graphite.wikidot.com/) or any other time-series database. Here's a selection of live stats from one of our storage nodes: This library generates call graphs of your live process for you. These call graphs aren't created through sampling. They're full pictures of all of the interesting functions you've annotated, along with quantile information about their successes, failures, how often they panic, return an error (if so instrumented), how many are currently running, etc. The data can be returned in dot format, in json, in text, and can be about just the functions that are currently executing, or all the functions the monitoring system has ever seen. Here's another example of one of our production nodes: https://raw.githubusercontent.com/spacemonkeygo/monkit/master/images/callgraph2.png This library generates trace graphs of your live process for you directly, without requiring standing up some tracing system such as Zipkin (though you can do that too). Inspired by Google's Dapper (http://research.google.com/pubs/pub36356.html) and Twitter's Zipkin (http://zipkin.io), we have process-internal trace graphs, triggerable by a number of different methods. You get this trace information for free whenever you use Go contexts (https://blog.golang.org/context) and function monitoring. The output formats are svg and json. Additionally, the library supports trace observation plugins, and we've written a plugin that sends this data to Zipkin (http://github.com/spacemonkeygo/monkit-zipkin). https://raw.githubusercontent.com/spacemonkeygo/monkit/master/images/trace.png Before our crazy Go rewrite of everything (https://www.spacemonkey.com/blog/posts/go-space-monkey) (and before we had even seen Google's Dapper paper), we were a Python shop, and all of our "interesting" functions were decorated with a helper that collected timing information and sent it to Graphite. When we transliterated to Go, we wanted to preserve that functionality, so the first version of our monitoring package was born. Over time it started to get janky, especially as we found Zipkin and started adding tracing functionality to it. We rewrote all of our Go code to use Google contexts, and then realized we could get call graph information. We decided a refactor and then an all-out rethinking of our monitoring package was best, and so now we have this library. Sometimes you really want callstack contextual information without having to pass arguments through everything on the call stack. In other languages, many people implement this with thread-local storage. Example: let's say you have written a big system that responds to user requests. All of your libraries log using your log library. During initial development everything is easy to debug, since there's low user load, but now you've scaled and there's OVER TEN USERS and it's kind of hard to tell what log lines were caused by what. Wouldn't it be nice to add request ids to all of the log lines kicked off by that request? Then you could grep for all log lines caused by a specific request id. Geez, it would suck to have to pass all contextual debugging information through all of your callsites. Google solved this problem by always passing a context.Context interface through from call to call. A Context is basically just a mapping of arbitrary keys to arbitrary values that users can add new values for. This way if you decide to add a request context, you can add it to your Context and then all callsites that decend from that place will have the new data in their contexts. It is admittedly very verbose to add contexts to every function call. Painfully so. I hope to write more about it in the future, but Google also wrote up their thoughts about it (https://blog.golang.org/context), which you can go read. For now, just swallow your disgust and let's keep moving. Let's make a super simple Varnish (https://www.varnish-cache.org/) clone. Open up gedit! (Okay just kidding, open whatever text editor you want.) For this motivating program, we won't even add the caching, though there's comments for where to add it if you'd like. For now, let's just make a barebones system that will proxy HTTP requests. We'll call it VLite, but maybe we should call it VReallyLite. Run and build this and open localhost:8080 in your browser. If you use the default proxy target, it should inform you that the world hasn't been destroyed yet. The first thing you'll want to do is add the small amount of boilerplate to make the instrumentation we're going to add to your process observable later. Import the basic monkit packages: and then register environmental statistics and kick off a goroutine in your main method to serve debug requests: Rebuild, and then check out localhost:9000/stats (or localhost:9000/stats/json, if you prefer) in your browser! Remember what I said about Google's contexts (https://blog.golang.org/context)? It might seem a bit overkill for such a small project, but it's time to add them. To help out here, I've created a library that constructs contexts for you for incoming HTTP requests. Nothing that's about to happen requires my webhelp library (https://godoc.org/github.com/jtolds/webhelp), but here is the code now refactored to receive and pass contexts through our two per-request calls. You can create a new context for a request however you want. One reason to use something like webhelp is that the cancelation feature of Contexts is hooked up to the HTTP request getting canceled. Let's start to get statistics about how many requests we receive! First, this package (main) will need to get a monitoring Scope. Add this global definition right after all your imports, much like you'd create a logger with many logging libraries: Now, make the error return value of HandleHTTP named (so, (err error)), and add this defer line as the very first instruction of HandleHTTP: Let's also add the same line (albeit modified for the lack of error) to Proxy, replacing &err with nil: You should now have something like: We'll unpack what's going on here, but for now: For this new funcs dataset, if you want a graph, you can download a dot graph at localhost:9000/funcs/dot and json information from localhost:9000/funcs/json. You should see something like: with a similar report for the Proxy method, or a graph like: https://raw.githubusercontent.com/spacemonkeygo/monkit/master/images/handlehttp.png This data reports the overall callgraph of execution for known traces, along with how many of each function are currently running, the most running concurrently (the highwater), how many were successful along with quantile timing information, how many errors there were (with quantile timing information if applicable), and how many panics there were. Since the Proxy method isn't capturing a returned err value, and since HandleHTTP always returns nil, this example won't ever have failures. If you're wondering about the success count being higher than you expected, keep in mind your browser probably requested a favicon.ico. Cool, eh? How it works is an interesting line of code - there's three function calls. If you look at the Go spec, all of the function calls will run at the time the function starts except for the very last one. The first function call, mon.Task(), creates or looks up a wrapper around a Func. You could get this yourself by requesting mon.Func() inside of the appropriate function or mon.FuncNamed(). Both mon.Task() and mon.Func() are inspecting runtime.Caller to determine the name of the function. Because this is a heavy operation, you can actually store the result of mon.Task() and reuse it somehow else if you prefer, so instead of you could instead use which is more performant every time after the first time. runtime.Caller only gets called once. Careful! Don't use the same myFuncMon in different functions unless you want to screw up your statistics! The second function call starts all the various stop watches and bookkeeping to keep track of the function. It also mutates the context pointer it's given to extend the context with information about what current span (in Zipkin parlance) is active. Notably, you *can* pass nil for the context if you really don't want a context. You just lose callgraph information. The last function call stops all the stop watches ad makes a note of any observed errors or panics (it repanics after observing them). Turns out, we don't even need to change our program anymore to get rich tracing information! Open your browser and go to localhost:9000/trace/svg?regex=HandleHTTP. It won't load, and in fact, it's waiting for you to open another tab and refresh localhost:8080 again. Once you retrigger the actual application behavior, the trace regex will capture a trace starting on the first function that matches the supplied regex, and return an svg. Go back to your first tab, and you should see a relatively uninteresting but super promising svg. Let's make the trace more interesting. Add a to your HandleHTTP method, rebuild, and restart. Load localhost:8080, then start a new request to your trace URL, then reload localhost:8080 again. Flip back to your trace, and you should see that the Proxy method only takes a portion of the time of HandleHTTP! https://cdn.rawgit.com/spacemonkeygo/monkit/master/images/trace.svg There's multiple ways to select a trace. You can select by regex using the preselect method (default), which first evaluates the regex on all known functions for sanity checking. Sometimes, however, the function you want to trace may not yet be known to monkit, in which case you'll want to turn preselection off. You may have a bad regex, or you may be in this case if you get the error "Bad Request: regex preselect matches 0 functions." Another way to select a trace is by providing a trace id, which we'll get to next! Make sure to check out what the addition of the time.Sleep call did to the other reports. It's easy to write plugins for monkit! Check out our first one that exports data to Zipkin (http://zipkin.io/)'s Scribe API: https://github.com/spacemonkeygo/monkit-zipkin We plan to have more (for HTrace, OpenTracing, etc, etc), soon!

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 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 shop is an eCommerce API used for publishing, listing, filtering and checkout of articles. This API is accessible through the gRPC protocol. The documentation is based on protobuf generated code. The protobuf file can be found at: https://github.com/moapis/shop/blob/master/shop.proto. More information on gRPC: https://grpc.io/docs/. Protocol buffers: https://developers.google.com/protocol-buffers/docs/overview.