You can specify exactly which fields you want returned by specifying a fields parameter. This helps limit responses to just the data we actually want. It can also help reduce costs, as Google bills based on the returned fields. The key idea here is that the client should be able to specify the fields that they want to read or update through your service. Our service manages a bunch of items, each of which has a nabh audit questions for nurses, stock keeping unit SKU 1quantity, and price.
To keep things simple, our prices will always be integral amounts in your unit of choice. Right now, it returns every field in our item. Similarly to our fetch endpoint, you must fully specify the item to be updated.
Even if you want to only change the quantity, you must still provide the name and price as well. To simplify our example, we just require specifying all fields in our request. Other ways of dealing with this issue are to use proto2 or to define a flag on each field to indicate whether or not the field was updated. Each of these approaches has its own problems. Field masks are similar to any other kind of mask.
You might already be familiar with bitmasks for bitwise operations or layer masks for image editing. A field mask lets us indicate fields in a protobuf message.
The mask itself is just a message, defined as a list of string paths:. The actual path values are just the field names defined in your message. If you have nested objects, you can specify the nested fields with a. We could use this mask:. To actually write the code to do this, we want to use FieldMaskUtil. The merge method will apply a field mask to a message for us.
It sets fields in the destination builder, according to the field mask and source. To update our fetch endpoint, we first need to let our callers specify a field mask for the fields they want returned. Our endpoint would then construct its response as before, but apply the given field mask before returning. First, if this endpoint is already in use, this will actually be a breaking change.
Existing requests will be sending the default, empty field mask. This would then result in nothing being returned. Second, this change does not substantially affect anything server-side. Our improvement is just the reduced amount of data that we have to send back to the client. We want to create an updated Itemusing the fields from our request. We use our existing item to create our destination builder. Again, as with our changes to our fetch endpoint, the changes to our update endpoint will break existing behavior.
Because field masks rely on using strings to specify fields, they break the backward and forward compatibility that protobufs provide. A SKU is just a unique identifier.In simple words, it enables the server and client applications to communicate transparently and build connected systems. GRPC is developed and open sourced by Google.
The remote Procedure call is quite an interesting concept. Remote Procedure Call is a high-level model for client-server communication. Assume there are two computers, computer A on local and computer B on some network.
One service calls either one service or two services or maybe more services and further those services may be calling other services.
But all these issues are overcome by GRPC. But GRPC can be used only in those use cases where there is internal communication between services. GRPC is only available for internal services because there is no APIs available for external use, but the Google team is working on GRPC-web also so that users can also use this for external services communications.
GRPC used in those use cases where there is a requirement of communication between services internally. Many of the companies like Netflix, Cisco, Wisconsin, Cockroach labs and many more started using GRPC for connecting multiple services in the environment. WebSocket is a protocol used for creating a two-way channel between a server and web browser.«Как использование Protobuf может упростить жизнь разработчикам», Владислав Кожушко, jlh.tarugos2308.pw
Websockets overcomes all the issues with HTTP such as —. To create the WebSocket connection, the client has to send the WebSocket handshake request, and then the server sends the WebSocket handshake response. In simple words, NO. But on the other hand, in WebSocket, developers are allowed to use API which is responsible for consuming and pushing the messages with a full duplex connection.
Another reason is that if the connection closed in between the content pushing process, and further the connection is opened again, then this connection cannot continue from where it left.This article will guide you through how to use AElf Boilerplate to implement a smart contract. It takes example on the Greeter contract that's already included in Boilerplate.
Based on the concepts this article presents you'll be able to create your own basic contract. The previous article showed you how to build, run and test a contract with the simple Hello World contract that is included in Boilerplate. This article is similar but more complete and will explain exactly how to add the elements of your contract and where to place them.
The following content will walk you through the basics of writing a smart contract; this process contains essentially four steps:. The Greeter contract is a very simple contract that exposes a Greet method that simply logs to the console and returns a "Hello World" message and a more sophisticated GreetTo method that records every greeting it receives and returns the greet message as well as the time of the greeting.
This tutorial shows you how to develop a smart contract with the C contract SDK, you can find you more here. Boilerplate will automatically add the reference to the SDK. As stated above the first step when writing a smart contract on AElf Boilerplate is to define the methods and types of your contract. The definition contains no logic, at build time the proto file is used to generate C classes that will be used to implement the contracts logic and state.
The "protobuf" folder already contains a certain amount of contract definitions, including tutorial examples, system contracts. You'll also notice it contains AElf Contract Standard definitions that are also defined the same way as contracts. Lastly it also contains options. Best practices:. Now let's take a look a the Greeter contract's definition:. Above is the full definition of the contract, it is mainly composed of three parts:.
Let's have a deeper look at the three different parts. The first line specifies the syntax that this protobuf file uses, we recommend you always use proto3 for your contracts.
Building scalable microservices with gRPC
Next you'll notice that this contract specifies some imports, let's analyze them briefly:. They are useful for defining things like an empty return value, time and wrappers around some common types such as string. The last line specifies an option that determines the target namespace of the generated code.
Here the generated code will be in the AElf. Greeter namespace. The first line here uses the aelf. This means that the state of the contract should be defined in the GreeterContractState class under the AElf.
Next, two action methods are defined: Greet and GreetTo. A contract method is defined by three things: the method namethe input argument s type s and the output type. For example Greet requires that the input type is google.
Empty that is used to specify that this method takes no arguments and the output type will be a google. StringValue which is a traditional string. As you can see with the GreetTo method, you can use custom types as input and output of contract methods. The service also defines a view method, that is, a method used only to query the contracts state and that has no side effect on the state.
For example, the definition of GetGreetedList uses the aelf. Best practice:. The protobuf file also includes the definition of two custom types.
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You'll notice the repeated keyword the the GreetedList message. This is protobuf syntax to represent a collection. Previously we defined the contract in a protobuf file, now let's take a look at the implementation of the contract methods defined above.Comment 0. A few months ago a colleague and long-time friend of mine published an intriguing blog on a few of the less discussed costs associated with implementing microservices.
The blog post made several important points on performance when designing and consuming microservices. There is an overhead to using a remote service beyond the obvious network latency due to routing and distance.
The blog describes how there is a cost attributed to serialization of JSON and therefore a microservice should do meaningful work to overcome the costs of serialization. While this is a generally accepted guideline for microservices, it is often overlooked.
A concrete reminder helps illustrate the point. One potential pitfall of having a more substantive endpoint is that the payload of a response can degrade performance and quickly consume thread pools and overload the network. I decided to create a sufficiently complex data model that utilized nested objects, lists, and primitives while trying to keep the model simple to understand. I ended up with a Recipe domain model that I would probably not use in a serious cooking application but that serves the purpose of this experiment.
The first challenge I encountered was how to work effectively with Protobuf messages. After spending some time reading through sparse documentation that focused on an elementary demonstration of Protobuf messages, I finally decided on a method for converting messages in and out of my domain model.
The preceding statements about using Protobufs is opinionated and someone who uses them often may disagree, but my experience was not smooth and I found messages to be rigid and more difficult than I expected. I spent some time learning JMH and designed my plan on how to test both methods. Using JMH, I designed a series of tests that allowed me to populate my POJO model, then construct a method that converted into and out of each of the technologies.
I isolated the conversion of the objects in order to capture just the costs associated with conversion. My results were not surprising as I expected Protobuf to be more efficient. I measured the average time to marshal an object into JSON at Converting a JSON string into the domain object took on average Run the samples yourself using the GitHub project created for this project.
I then captured the traffic using Wireshark and calculated the total amount of bytes sent for these requests. The JSON was minified but not compressed. Using compression can be detrimental to the overall performance of the solution based on the payload size. If the payload is too small, the cost of compressing and decompressing will overcome the benefits of a smaller payload. This is a very similar problem to the costs associated with marshaling JSON with small payloads as found by Jeremy's blog.
After completing a project to help determine the overall benefits of using Protobuf over JSON I have come to a conclusion that unless performance is absolutely critical and the developing team's maturity level is high enough to understand the high costs of using Protobufs, then it is a legitimate option to increase the performance associated with message passing.
That being said, the costs of working with Protobufs is very high. Developers lose access to human-readable messages often useful during debugging. Additionally, Protobufs are messages, not objects and therefore come with more structure and rigor, which I found to be complicated due to the inflexibility using only primitives and enums.
Lastly, there is limited documentation on Protocol Buffers beyond the basic "hello world" applications. See the original article here.
Performance Zone. Over a million developers have joined DZone.You can become a serverless blackbelt. Enrol to my 4-week online workshop Production-Ready Serverless and gain hands-on experience building something from scratch using serverless technologies. At the end of the workshop, you should have a broader view of the challenges you will face as your serverless architecture matures and expands.
You should also have a firm grasp on when serverless is a good fit for your system as well as common pitfalls you need to avoid. When working with the BinaryFormatter class frequently, one of the things you notice is that it is really damn inefficient… both in terms of speed as well as the payload the size of the serialized byte array.
Marc Gravell of StackOverflow fame! One curious observation about the payload size is that, when I used a BinaryWriter to simply write every property into the output stream without any metadata, what I got back should be the minimum payload size without compression, and yet the protobuf-net serializer still manages to beat that! BinaryFormatter with ISerializable. I also tested the BinaryFormatter with a class that implements the ISerializable interface see below because others had suggested in the past that you are likely to get a noticeable performance boost if you implement the ISerializable interface yourself.
The belief is that it will perform much better as it removes the reliance on reflection which can be detrimental to the performance of your code when used excessively. However, based on the tests I have done, this does not seem to be the case, the slightly better serialization speed is far from conclusive and is offset by a slightly slower deserialization speed.
As I mentioned in the post, protobuf-net managed to produce a smaller payload than what is required to hold all the property values of the test object without any meta. I posted this question on SO, and as Marc said in his answer the smaller payload is achieved through the use of varint and zigzag encoding, read more about them here. I specialise in rapidly transitioning teams to serverless and building production-ready services on AWS.
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Check out my new podcast Real-World Serverless where I talk with engineers who are building amazing things with serverless technologies and discuss the real-world use cases and challenges they face. Check out my new course, Learn you some Lambda best practice for great good! In this course, you will learn best practices for working with AWS Lambda in terms of performance, cost, security, scalability, resilience and observability.
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Skip to content You can become a serverless blackbelt.This page describes some commonly-used design patterns for dealing with Protocol Buffers.
You can also send design and usage questions to the Protocol Buffers discussion group. Streaming Multiple Messages If you want to write multiple messages to a single file or stream, it is up to you to keep track of where one message ends and the next begins. The Protocol Buffer wire format is not self-delimiting, so protocol buffer parsers cannot determine where a message ends on their own.
The easiest way to solve this problem is to write the size of each message before you write the message itself. When you read the messages back in, you read the size, then read the bytes into a separate buffer, then parse from that buffer. Large Data Sets Protocol Buffers are not designed to handle large messages.
As a general rule of thumb, if you are dealing in messages larger than a megabyte each, it may be time to consider an alternate strategy. That said, Protocol Buffers are great for handling individual messages within a large data set. Usually, large data sets are really just a collection of small pieces, where each small piece may be a structured piece of data. Even though Protocol Buffers cannot handle the entire set at once, using Protocol Buffers to encode each piece greatly simplifies your problem: now all you need is to handle a set of byte strings rather than a set of structures.
Protocol Buffers do not include any built-in support for large data sets because different situations call for different solutions. Sometimes a simple list of records will do while other times you may want something more like a database. Each solution should be developed as a separate library, so that only those who need it need to pay the costs. Self-describing Messages Protocol Buffers do not contain descriptions of their own types.
Thus, given only a raw message without the corresponding.
However, note that the contents of a. All that said, the reason that this functionality is not included in the Protocol Buffer library is because we have never had a use for it inside Google. This technique requires support for dynamic messages using descriptors. Please check that your platforms support this feature before using self-describing messages.
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Already on GitHub? Sign in to your account. We have a large application that has 9 different gRPC services and we are currently trying to transfer them into a micro services architecture.
We are running into issues where our foo service returns a message that bundles a message from the bar service, but the foo service knows nothing about any of the bar service's messages.
I have run into issue This makes me think that I'm using the wrong approach. I wanted to get a reality check on our structure, and if there is a better way to accomplish what we are attempting to do. Or is it bad practice to include the bar service messages inside of a foo service message? There is nothing wrong with splitting up the proto definitions of the services.
That would be how I would do it as well.
Performance Test – BinaryFormatter vs Protobuf-Net
Do define them in separate proto packages as well. Do also define them in one or more separate proto packages and the services protos can import and reference them. Do make sure that when you generate the Go code for these to use equivalent Go packages i. But that will also depend on your build environment if these are automatically generated or not.
Thanks for the information! I spent some time trying to get the separate proto definitions per service working, but in the end we just went with the one proto package approach. Our plan is to make the proto package its own repo and pull it into each service and compile on build.
This means that each service would have access to every other service's methods and messages. Plus, if a service needs to talk with another service, it already has access to the other services requests and replies to make the call.
One downside is that we need to be careful about naming because all requests and replies are in the same namespace, so we can't have a GetRequest for each services, we need a GetUserRequest and a GetRoleRequest, etc. It's less sightly, but in the end very explicit.
Another complication is how we will navigate updating proto definitions and having them available for each service, but we'll figure that out when we get there. I found this question solved at here. Duplicate of Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
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