Tag Archives: java

Running ActiveMQ in a Hybrid Cloud Environment with Amazon MQ

Post Syndicated from Tara Van Unen original https://aws.amazon.com/blogs/compute/running-activemq-in-a-hybrid-cloud-environment-with-amazon-mq/

This post courtesy of Greg Share, AWS Solutions Architect

Many organizations, particularly enterprises, rely on message brokers to connect and coordinate different systems. Message brokers enable distributed applications to communicate with one another, serving as the technological backbone for their IT environment, and ultimately their business services. Applications depend on messaging to work.

In many cases, those organizations have started to build new or “lift and shift” applications to AWS. In some cases, there are applications, such as mainframe systems, too costly to migrate. In these scenarios, those on-premises applications still need to interact with cloud-based components.

Amazon MQ is a managed message broker service for ActiveMQ that enables organizations to send messages between applications in the cloud and on-premises to enable hybrid environments and application modernization. For example, you can invoke AWS Lambda from queues and topics managed by Amazon MQ brokers to integrate legacy systems with serverless architectures. ActiveMQ is an open-source message broker written in Java that is packaged with clients in multiple languages, Java Message Server (JMS) client being one example.

This post shows you can use Amazon MQ to integrate on-premises and cloud environments using the network of brokers feature of ActiveMQ. It provides configuration parameters for a one-way duplex connection for the flow of messages from an on-premises ActiveMQ message broker to Amazon MQ.

ActiveMQ and the network of brokers

First, look at queues within ActiveMQ and then at the network of brokers as a mechanism to distribute messages.

The network of brokers behaves differently from models such as physical networks. The key consideration is that the production (sending) of a message is disconnected from the consumption of that message. Think of the delivery of a parcel: The parcel is sent by the supplier (producer) to the end customer (consumer). The path it took to get there is of little concern to the customer, as long as it receives the package.

The same logic can be applied to the network of brokers. Here’s how you build the flow from a simple message to a queue and build toward a network of brokers. Before you look at setting up a hybrid connection, I discuss how a broker processes messages in a simple scenario.

When a message is sent from a producer to a queue on a broker, the following steps occur:

  1. A message is sent to a queue from the producer.
  2. The broker persists this in its store or journal.
  3. At this point, an acknowledgement (ACK) is sent to the producer from the broker.

When a consumer looks to consume the message from that same queue, the following steps occur:

  1. The message listener (consumer) calls the broker, which creates a subscription to the queue.
  2. Messages are fetched from the message store and sent to the consumer.
  3. The consumer acknowledges that the message has been received before processing it.
  4. Upon receiving the ACK, the broker sets the message as having been consumed. By default, this deletes it from the queue.
    • You can set the consumer to ACK after processing by setting up transaction management or handle it manually using Session.CLIENT_ACKNOWLEDGE.

Static propagation

I now introduce the concept of static propagation with the network of brokers as the mechanism for message transfer from on-premises brokers to Amazon MQ.  Static propagation refers to message propagation that occurs in the absence of subscription information. In this case, the objective is to transfer messages arriving at your selected on-premises broker to the Amazon MQ broker for consumption within the cloud environment.

After you configure static propagation with a network of brokers, the following occurs:

  1. The on-premises broker receives a message from a producer for a specific queue.
  2. The on-premises broker sends (statically propagates) the message to the Amazon MQ broker.
  3. The Amazon MQ broker sends an acknowledgement to the on-premises broker, which marks the message as having been consumed.
  4. Amazon MQ holds the message in its queue ready for consumption.
  5. A consumer connects to Amazon MQ broker, subscribes to the queue in which the message resides, and receives the message.
  6. Amazon MQ broker marks the message as having been consumed.

Getting started

The first step is creating an Amazon MQ broker.

  1. Sign in to the Amazon MQ console and launch a new Amazon MQ broker.
  2. Name your broker and choose Next step.
  3. For Broker instance type, choose your instance size:
  4. For Deployment mode, enter one of the following:
    Single-instance broker for development and test implementations (recommended)
    Active/standby broker for high availability in production environments
  5. Scroll down and enter your user name and password.
  6. Expand Advanced Settings.
  7. For VPC, Subnet, and Security Group, pick the values for the resources in which your broker will reside.
  8. For Public Accessibility, choose Yes, as connectivity is internet-based. Another option would be to use private connectivity between your on-premises network and the VPC, an example being an AWS Direct Connect or VPN connection. In that case, you could set Public Accessibility to No.
  9. For Maintenance, leave the default value, No preference.
  10. Choose Create Broker. Wait several minutes for the broker to be created.

After creation is complete, you see your broker listed.

For connectivity to work, you must configure the security group where Amazon MQ resides. For this post, I focus on the OpenWire protocol.

For Openwire connectivity, allow port 61617 access for Amazon MQ from your on-premises ActiveMQ broker source IP address. For alternate protocols, see the Amazon MQ broker configuration information for the ports required:

OpenWire – ssl://xxxxxxx.xxx.com:61617
AMQP – amqp+ssl:// xxxxxxx.xxx.com:5671
STOMP – stomp+ssl:// xxxxxxx.xxx.com:61614
MQTT – mqtt+ssl:// xxxxxxx.xxx.com:8883
WSS – wss:// xxxxxxx.xxx.com:61619

Configuring the network of brokers

Configuring the network of brokers with static propagation occurs on the on-premises broker by applying changes to the following file:
<activemq install directory>/conf activemq.xml

Network connector

This is the first configuration item required to enable a network of brokers. It is only required on the on-premises broker, which initiates and creates the connection with Amazon MQ. This connection, after it’s established, enables the flow of messages in either direction between the on-premises broker and Amazon MQ. The focus of this post is the uni-directional flow of messages from the on-premises broker to Amazon MQ.

The default activemq.xml file does not include the network connector configuration. Add this with the networkConnector element. In this scenario, edit the on-premises broker activemq.xml file to include the following information between <systemUsage> and <transportConnectors>:

                name="Q:source broker name->target broker name"
                uri="static:(ssl:// aws mq endpoint:61617)" 
                    <queue physicalName="queuename"/>
                      <queue physicalName=">" />

The highlighted components are the most important elements when configuring your on-premises broker.

  • name – Name of the network bridge. In this case, it specifies two things:
    • That this connection relates to an ActiveMQ queue (Q) as opposed to a topic (T), for reference purposes.
    • The source broker and target broker.
  • duplex –Setting this to false ensures that messages traverse uni-directionally from the on-premises broker to Amazon MQ.
  • uri –Specifies the remote endpoint to which to connect for message transfer. In this case, it is an Openwire endpoint on your Amazon MQ broker. This information could be obtained from the Amazon MQ console or via the API.
  • username and password – The same username and password configured when creating the Amazon MQ broker, and used to access the Amazon MQ ActiveMQ console.
  • networkTTL – Number of brokers in the network through which messages and subscriptions can pass. Leave this setting at the current value, if it is already included in your broker connection.
  • staticallyIncludedDestinations > queue physicalName – The destination ActiveMQ queue for which messages are destined. This is the queue that is propagated from the on-premises broker to the Amazon MQ broker for message consumption.

After the network connector is configured, you must restart the ActiveMQ service on the on-premises broker for the changes to be applied.

Verify the configuration

There are a number of places within the ActiveMQ console of your on-premises and Amazon MQ brokers to browse to verify that the configuration is correct and the connection has been established.

On-premises broker

Launch the ActiveMQ console of your on-premises broker and navigate to Network. You should see an active network bridge similar to the following:

This identifies that the connection between your on-premises broker and your Amazon MQ broker is up and running.

Now navigate to Connections and scroll to the bottom of the page. Under the Network Connectors subsection, you should see a connector labeled with the name: value that you provided within the ActiveMQ.xml configuration file. You should see an entry similar to:

Amazon MQ broker

Launch the ActiveMQ console of your Amazon MQ broker and navigate to Connections. Scroll to the Connections openwire subsection and you should see a connection specified that references the name: value that you provided within the ActiveMQ.xml configuration file. You should see an entry similar to:

If you configured the uri: for AMQP, STOMP, MQTT, or WSS as opposed to Openwire, you would see this connection under the corresponding section of the Connections page.

Testing your message flow

The setup described outlines a way for messages produced on premises to be propagated to the cloud for consumption in the cloud. This section provides steps on verifying the message flow.

Verify that the queue has been created

After you specify this queue name as staticallyIncludedDestinations > queue physicalName: and your ActiveMQ service starts, you see the following on your on-premises ActiveMQ console Queues page.

As you can see, no messages have been sent but you have one consumer listed. If you then choose Active Consumers under the Views column, you see Active Consumers for TestingQ.

This is telling you that your Amazon MQ broker is a consumer of your on-premises broker for the testing queue.

Produce and send a message to the on-premises broker

Now, produce a message on an on-premises producer and send it to your on-premises broker to a queue named TestingQ. If you navigate back to the queues page of your on-premises ActiveMQ console, you see that the messages enqueued and messages dequeued column count for your TestingQ queue have changed:

What this means is that the message originating from the on-premises producer has traversed the on-premises broker and propagated immediately to the Amazon MQ broker. At this point, the message is no longer available for consumption from the on-premises broker.

If you access the ActiveMQ console of your Amazon MQ broker and navigate to the Queues page, you see the following for the TestingQ queue:

This means that the message originally sent to your on-premises broker has traversed the network of brokers unidirectional network bridge, and is ready to be consumed from your Amazon MQ broker. The indicator is the Number of Pending Messages column.

Consume the message from an Amazon MQ broker

Connect to the Amazon MQ TestingQ queue from a consumer within the AWS Cloud environment for message consumption. Log on to the ActiveMQ console of your Amazon MQ broker and navigate to the Queue page:

As you can see, the Number of Pending Messages column figure has changed to 0 as that message has been consumed.

This diagram outlines the message lifecycle from the on-premises producer to the on-premises broker, traversing the hybrid connection between the on-premises broker and Amazon MQ, and finally consumption within the AWS Cloud.


This post focused on an ActiveMQ-specific scenario for transferring messages within an ActiveMQ queue from an on-premises broker to Amazon MQ.

For other on-premises brokers, such as IBM MQ, another approach would be to run ActiveMQ on-premises broker and use JMS bridging to IBM MQ, while using the approach in this post to forward to Amazon MQ. Yet another approach would be to use Apache Camel for more sophisticated routing.

I hope that you have found this example of hybrid messaging between an on-premises environment in the AWS Cloud to be useful. Many customers are already using on-premises ActiveMQ brokers, and this is a great use case to enable hybrid cloud scenarios.

To learn more, see the Amazon MQ website and Developer Guide. You can try Amazon MQ for free with the AWS Free Tier, which includes up to 750 hours of a single-instance mq.t2.micro broker and up to 1 GB of storage per month for one year.


HiveMQ 3.3.3 released

Post Syndicated from The HiveMQ Team original https://www.hivemq.com/blog/hivemq-3-3-3-released/

The HiveMQ team is pleased to announce the availability of HiveMQ 3.3.3. This is a maintenance release for the 3.3 series and brings the following improvements:

  • Adds global option to rate-limit plugin service calls
  • Improved Logging for configured TLS Cipher Suites
  • Improved Retained Message Metrics
  • Improved support for Java 9
  • Fixed an issue where the metric half-full-queue.count could show an incorrect value
  • Fixed an issue that could cause cluster nodes to wait for operational nodes on startup indefinitely
  • Improved payload reference counting for single node deployments
  • Fixed an issue with rolling upgrades in an edge case where a node with a newer version is joining during network-split
  • Improved Shutdown behaviour for OnPublishReceivedCallbacks and plugin system services
  • Fixed an issue where assignments in the ClientGroupingService got cleaned up prematurely
  • Improved example configuration file for in-memory persistence

You can download the new HiveMQ version here.

We recommend to upgrade if you are an HiveMQ 3.3.x user.

Have a great day,
The HiveMQ Team

HiveMQ 3.2.9 released

Post Syndicated from The HiveMQ Team original https://www.hivemq.com/blog/hivemq-3-2-9-released/

The HiveMQ team is pleased to announce the availability of HiveMQ 3.2.9. This is a maintenance release for the 3.2 series and brings the following improvements:

  • Improved Logging for configured TLS Cipher Suites
  • Improved Retained Message Metrics
  • Improved support for Java 9
  • Fixed an issue where the metric half-full-queue.count could show an incorrect value
  • Fixed an issue that could cause cluster nodes to wait for operational nodes on startup indefinitely
  • Improved payload reference counting for single node deployments
  • Fixed an issue with rolling upgrades in an edge case where a node with a newer version is joining during network-split
  • Improved Shutdown behaviour for OnPublishReceivedCallbacks and plugin system services

You can download the new HiveMQ version here.

We recommend to upgrade if you are an HiveMQ 3.2.x user.

Have a great day,
The HiveMQ Team

Integration With Zapier

Post Syndicated from Bozho original https://techblog.bozho.net/integration-with-zapier/

Integration is boring. And also inevitable. But I won’t be writing about enterprise integration patterns. Instead, I’ll explain how to create an app for integration with Zapier.

What is Zapier? It is a service that allows you tо connect two (or more) otherwise unconnected services via their APIs (or protocols). You can do stuff like “Create a Trello task from an Evernote note”, “publish new RSS items to Facebook”, “append new emails to a spreadsheet”, “post approaching calendar meeting to Slack”, “Save big email attachments to Dropbox”, “tweet all instagrams above a certain likes threshold”, and so on. In fact, it looks to cover mostly the same usecases as another famous service that I really like – IFTTT (if this then that), with my favourite use-case “Get a notification when the international space station passes over your house”. And all of those interactions can be configured via a UI.

Now that’s good for end users but what does it have to do with software development and integration? Zapier (unlike IFTTT, unfortunately), allows custom 3rd party services to be included. So if you have a service of your own, you can create an “app” and allow users to integrate your service with all the other 3rd party services. IFTTT offers a way to invoke web endpoints (including RESTful services), but it doesn’t allow setting headers, so that makes it quite limited for actual APIs.

In this post I’ll briefly explain how to write a custom Zapier app and then will discuss where services like Zapier stand from an architecture perspective.

The thing that I needed it for – to be able to integrate LogSentinel with any of the third parties available through Zapier, i.e. to store audit logs for events that happen in all those 3rd party systems. So how do I do that? There’s a tutorial that makes it look simple. And it is, with a few catches.

First, there are two tutorials – one in GitHub and one on Zapier’s website. And they differ slightly, which becomes tricky in some cases.

I initially followed the GitHub tutorial and had my build fail. It claimed the zapier platform dependency is missing. After I compared it with the example apps, I found out there’s a caret in front of the zapier platform dependency. Removing it just yielded another error – that my node version should be exactly 6.10.2. Why?

The Zapier CLI requires you have exactly version 6.10.2 installed. You’ll see errors and will be unable to proceed otherwise.

It appears that they are using AWS Lambda which is stuck on Node 6.10.2 (actually – it’s 6.10.3 when you check). The current major release is 8, so minus points for choosing … javascript for a command-line tool and for building sandboxed apps. Maybe other decisions had their downsides as well, I won’t be speculating. Maybe it’s just my dislike for dynamic languages.

So, after you make sure you have the correct old version on node, you call zapier init and make sure there are no carets, npm install and then zapier test. So far so good, you have a dummy app. Now how do you make a RESTful call to your service?

Zapier splits the programmable entities in two – “triggers” and “creates”. A trigger is the event that triggers the whole app, an a “create” is what happens as a result. In my case, my app doesn’t publish any triggers, it only accepts input, so I won’t be mentioning triggers (though they seem easy). You configure all of the elements in index.js (e.g. this one):

const log = require('./creates/log');
creates: {
    [log.key]: log,

The log.js file itself is the interesting bit – there you specify all the parameters that should be passed to your API call, as well as making the API call itself:

const log = (z, bundle) => {
  const responsePromise = z.request({
    method: 'POST',
    url: `https://api.logsentinel.com/api/log/${bundle.inputData.actorId}/${bundle.inputData.action}`,
    body: bundle.inputData.details,
	headers: {
		'Accept': 'application/json'
  return responsePromise
    .then(response => JSON.parse(response.content));

module.exports = {
  key: 'log-entry',
  noun: 'Log entry',

  display: {
    label: 'Log',
    description: 'Log an audit trail entry'

  operation: {
    inputFields: [
      {key: 'actorId', label:'ActorID', required: true},
      {key: 'action', label:'Action', required: true},
      {key: 'details', label:'Details', required: false}
    perform: log

You can pass the input parameters to your API call, and it’s as simple as that. The user can then specify which parameters from the source (“trigger”) should be mapped to each of your parameters. In an example zap, I used an email trigger and passed the sender as actorId, the sibject as “action” and the body of the email as details.

There’s one more thing – authentication. Authentication can be done in many ways. Some services offer OAuth, others – HTTP Basic or other custom forms of authentication. There is a section in the documentation about all the options. In my case it was (almost) an HTTP Basic auth. My initial thought was to just supply the credentials as parameters (which you just hardcode rather than map to trigger parameters). That may work, but it’s not the canonical way. You should configure “authentication”, as it triggers a friendly UI for the user.

You include authentication.js (which has the fields your authentication requires) and then pre-process requests by adding a header (in index.js):

const authentication = require('./authentication');

const includeAuthHeaders = (request, z, bundle) => {
  if (bundle.authData.organizationId) {
	request.headers = request.headers || {};
	request.headers['Application-Id'] = bundle.authData.applicationId
	const basicHash = Buffer(`${bundle.authData.organizationId}:${bundle.authData.apiSecret}`).toString('base64');
	request.headers['Authorization'] = `Basic ${basicHash}`;
  return request;

const App = {
  // This is just shorthand to reference the installed dependencies you have. Zapier will
  // need to know these before we can upload
  version: require('./package.json').version,
  platformVersion: require('zapier-platform-core').version,
  authentication: authentication,
  // beforeRequest & afterResponse are optional hooks into the provided HTTP client
  beforeRequest: [

And then you zapier push your app and you can test it. It doesn’t automatically go live, as you have to invite people to try it and use it first, but in many cases that’s sufficient (i.e. using Zapier when doing integration with a particular client)

Can Zapier can be used for any integration problem? Unlikely – it’s pretty limited and simple, but that’s also a strength. You can, in half a day, make your service integrate with thousands of others for the most typical use-cases. And not that although it’s meant for integrating public services rather than for enterprise integration (where you make multiple internal systems talk to each other), as an increasing number of systems rely on 3rd party services, it could find home in an enterprise system, replacing some functions of an ESB.

Effectively, such services (Zapier, IFTTT) are “Simple ESB-as-a-service”. You go to a UI, fill a bunch of fields, and you get systems talking to each other without touching the systems themselves. I’m not a big fan of ESBs, mostly because they become harder to support with time. But minimalist, external ones might be applicable in certain situations. And while such services are primarily aimed at end users, they could be a useful bit in an enterprise architecture that relies on 3rd party services.

Whether it could process the required load, whether an organization is willing to let its data flow through a 3rd party provider (which may store the intermediate parameters), is a question that should be answered in a case by cases basis. I wouldn’t recommend it as a general solution, but it’s certainly an option to consider.

The post Integration With Zapier appeared first on Bozho's tech blog.

Reactive Microservices Architecture on AWS

Post Syndicated from Sascha Moellering original https://aws.amazon.com/blogs/architecture/reactive-microservices-architecture-on-aws/

Microservice-application requirements have changed dramatically in recent years. These days, applications operate with petabytes of data, need almost 100% uptime, and end users expect sub-second response times. Typical N-tier applications can’t deliver on these requirements.

Reactive Manifesto, published in 2014, describes the essential characteristics of reactive systems including: responsiveness, resiliency, elasticity, and being message driven.

Being message driven is perhaps the most important characteristic of reactive systems. Asynchronous messaging helps in the design of loosely coupled systems, which is a key factor for scalability. In order to build a highly decoupled system, it is important to isolate services from each other. As already described, isolation is an important aspect of the microservices pattern. Indeed, reactive systems and microservices are a natural fit.

Implemented Use Case
This reference architecture illustrates a typical ad-tracking implementation.

Many ad-tracking companies collect massive amounts of data in near-real-time. In many cases, these workloads are very spiky and heavily depend on the success of the ad-tech companies’ customers. Typically, an ad-tracking-data use case can be separated into a real-time part and a non-real-time part. In the real-time part, it is important to collect data as fast as possible and ask several questions including:,  “Is this a valid combination of parameters?,””Does this program exist?,” “Is this program still valid?”

Because response time has a huge impact on conversion rate in advertising, it is important for advertisers to respond as fast as possible. This information should be kept in memory to reduce communication overhead with the caching infrastructure. The tracking application itself should be as lightweight and scalable as possible. For example, the application shouldn’t have any shared mutable state and it should use reactive paradigms. In our implementation, one main application is responsible for this real-time part. It collects and validates data, responds to the client as fast as possible, and asynchronously sends events to backend systems.

The non-real-time part of the application consumes the generated events and persists them in a NoSQL database. In a typical tracking implementation, clicks, cookie information, and transactions are matched asynchronously and persisted in a data store. The matching part is not implemented in this reference architecture. Many ad-tech architectures use frameworks like Hadoop for the matching implementation.

The system can be logically divided into the data collection partand the core data updatepart. The data collection part is responsible for collecting, validating, and persisting the data. In the core data update part, the data that is used for validation gets updated and all subscribers are notified of new data.

Components and Services

Main Application
The main application is implemented using Java 8 and uses Vert.x as the main framework. Vert.x is an event-driven, reactive, non-blocking, polyglot framework to implement microservices. It runs on the Java virtual machine (JVM) by using the low-level IO library Netty. You can write applications in Java, JavaScript, Groovy, Ruby, Kotlin, Scala, and Ceylon. The framework offers a simple and scalable actor-like concurrency model. Vert.x calls handlers by using a thread known as an event loop. To use this model, you have to write code known as “verticles.” Verticles share certain similarities with actors in the actor model. To use them, you have to implement the verticle interface. Verticles communicate with each other by generating messages in  a single event bus. Those messages are sent on the event bus to a specific address, and verticles can register to this address by using handlers.

With only a few exceptions, none of the APIs in Vert.x block the calling thread. Similar to Node.js, Vert.x uses the reactor pattern. However, in contrast to Node.js, Vert.x uses several event loops. Unfortunately, not all APIs in the Java ecosystem are written asynchronously, for example, the JDBC API. Vert.x offers a possibility to run this, blocking APIs without blocking the event loop. These special verticles are called worker verticles. You don’t execute worker verticles by using the standard Vert.x event loops, but by using a dedicated thread from a worker pool. This way, the worker verticles don’t block the event loop.

Our application consists of five different verticles covering different aspects of the business logic. The main entry point for our application is the HttpVerticle, which exposes an HTTP-endpoint to consume HTTP-requests and for proper health checking. Data from HTTP requests such as parameters and user-agent information are collected and transformed into a JSON message. In order to validate the input data (to ensure that the program exists and is still valid), the message is sent to the CacheVerticle.

This verticle implements an LRU-cache with a TTL of 10 minutes and a capacity of 100,000 entries. Instead of adding additional functionality to a standard JDK map implementation, we use Google Guava, which has all the features we need. If the data is not in the L1 cache, the message is sent to the RedisVerticle. This verticle is responsible for data residing in Amazon ElastiCache and uses the Vert.x-redis-client to read data from Redis. In our example, Redis is the central data store. However, in a typical production implementation, Redis would just be the L2 cache with a central data store like Amazon DynamoDB. One of the most important paradigms of a reactive system is to switch from a pull- to a push-based model. To achieve this and reduce network overhead, we’ll use Redis pub/sub to push core data changes to our main application.

Vert.x also supports direct Redis pub/sub-integration, the following code shows our subscriber-implementation:

vertx.eventBus().<JsonObject>consumer(REDIS_PUBSUB_CHANNEL_VERTX, received -> {

JsonObject value = received.body().getJsonObject("value");

String message = value.getString("message");

JsonObject jsonObject = new JsonObject(message);



redis.subscribe(Constants.REDIS_PUBSUB_CHANNEL, res -> {

if (res.succeeded()) {

LOGGER.info("Subscribed to " + Constants.REDIS_PUBSUB_CHANNEL);

} else {




The verticle subscribes to the appropriate Redis pub/sub-channel. If a message is sent over this channel, the payload is extracted and forwarded to the cache-verticle that stores the data in the L1-cache. After storing and enriching data, a response is sent back to the HttpVerticle, which responds to the HTTP request that initially hit this verticle. In addition, the message is converted to ByteBuffer, wrapped in protocol buffers, and send to an Amazon Kinesis Data Stream.

The following example shows a stripped-down version of the KinesisVerticle:

public class KinesisVerticle extends AbstractVerticle {

private static final Logger LOGGER = LoggerFactory.getLogger(KinesisVerticle.class);

private AmazonKinesisAsync kinesisAsyncClient;

private String eventStream = "EventStream";


public void start() throws Exception {

EventBus eb = vertx.eventBus();

kinesisAsyncClient = createClient();

eventStream = System.getenv(STREAM_NAME) == null ? "EventStream" : System.getenv(STREAM_NAME);

eb.consumer(Constants.KINESIS_EVENTBUS_ADDRESS, message -> {

try {

TrackingMessage trackingMessage = Json.decodeValue((String)message.body(), TrackingMessage.class);

String partitionKey = trackingMessage.getMessageId();

byte [] byteMessage = createMessage(trackingMessage);

ByteBuffer buf = ByteBuffer.wrap(byteMessage);

sendMessageToKinesis(buf, partitionKey);



catch (KinesisException exc) {





Kinesis Consumer
This AWS Lambda function consumes data from an Amazon Kinesis Data Stream and persists the data in an Amazon DynamoDB table. In order to improve testability, the invocation code is separated from the business logic. The invocation code is implemented in the class KinesisConsumerHandler and iterates over the Kinesis events pulled from the Kinesis stream by AWS Lambda. Each Kinesis event is unwrapped and transformed from ByteBuffer to protocol buffers and converted into a Java object. Those Java objects are passed to the business logic, which persists the data in a DynamoDB table. In order to improve duration of successive Lambda calls, the DynamoDB-client is instantiated lazily and reused if possible.

Redis Updater
From time to time, it is necessary to update core data in Redis. A very efficient implementation for this requirement is using AWS Lambda and Amazon Kinesis. New core data is sent over the AWS Kinesis stream using JSON as data format and consumed by a Lambda function. This function iterates over the Kinesis events pulled from the Kinesis stream by AWS Lambda. Each Kinesis event is unwrapped and transformed from ByteBuffer to String and converted into a Java object. The Java object is passed to the business logic and stored in Redis. In addition, the new core data is also sent to the main application using Redis pub/sub in order to reduce network overhead and converting from a pull- to a push-based model.

The following example shows the source code to store data in Redis and notify all subscribers:

public void updateRedisData(final TrackingMessage trackingMessage, final Jedis jedis, final LambdaLogger logger) {

try {

ObjectMapper mapper = new ObjectMapper();

String jsonString = mapper.writeValueAsString(trackingMessage);

Map<String, String> map = marshal(jsonString);

String statusCode = jedis.hmset(trackingMessage.getProgramId(), map);


catch (Exception exc) {

if (null == logger)






public void notifySubscribers(final TrackingMessage trackingMessage, final Jedis jedis, final LambdaLogger logger) {

try {

ObjectMapper mapper = new ObjectMapper();

String jsonString = mapper.writeValueAsString(trackingMessage);

jedis.publish(Constants.REDIS_PUBSUB_CHANNEL, jsonString);


catch (final IOException e) {

log(e.getMessage(), logger);



Similarly to our Kinesis Consumer, the Redis-client is instantiated somewhat lazily.

Infrastructure as Code
As already outlined, latency and response time are a very critical part of any ad-tracking solution because response time has a huge impact on conversion rate. In order to reduce latency for customers world-wide, it is common practice to roll out the infrastructure in different AWS Regions in the world to be as close to the end customer as possible. AWS CloudFormation can help you model and set up your AWS resources so that you can spend less time managing those resources and more time focusing on your applications that run in AWS.

You create a template that describes all the AWS resources that you want (for example, Amazon EC2 instances or Amazon RDS DB instances), and AWS CloudFormation takes care of provisioning and configuring those resources for you. Our reference architecture can be rolled out in different Regions using an AWS CloudFormation template, which sets up the complete infrastructure (for example, Amazon Virtual Private Cloud (Amazon VPC), Amazon Elastic Container Service (Amazon ECS) cluster, Lambda functions, DynamoDB table, Amazon ElastiCache cluster, etc.).

In this blog post we described reactive principles and an example architecture with a common use case. We leveraged the capabilities of different frameworks in combination with several AWS services in order to implement reactive principles—not only at the application-level but also at the system-level. I hope I’ve given you ideas for creating your own reactive applications and systems on AWS.

About the Author

Sascha Moellering is a Senior Solution Architect. Sascha is primarily interested in automation, infrastructure as code, distributed computing, containers and JVM. He can be reached at [email protected]



Security updates for Friday

Post Syndicated from jake original https://lwn.net/Articles/746326/rss

Security updates have been issued by CentOS (systemd and thunderbird), Debian (squid and squid3), Fedora (firefox), Mageia (java-1.8.0-openjdk and sox), openSUSE (ecryptfs-utils and libXfont), Oracle (systemd and thunderbird), Scientific Linux (thunderbird), and Ubuntu (dovecot and w3m).

Security updates for Wednesday

Post Syndicated from jake original https://lwn.net/Articles/745912/rss

Security updates have been issued by Arch Linux (dnsmasq, libmupdf, mupdf, mupdf-gl, mupdf-tools, and zathura-pdf-mupdf), CentOS (kernel), Debian (smarty3, thunderbird, and unbound), Fedora (bind, bind-dyndb-ldap, coreutils, curl, dnsmasq, dnsperf, gcab, java-1.8.0-openjdk, libxml2, mongodb, poco, rubygem-rack-protection, transmission, unbound, and wireshark), Red Hat (collectd, erlang, and openstack-nova), SUSE (bind), and Ubuntu (clamav and webkit2gtk).

Security updates for Tuesday

Post Syndicated from corbet original https://lwn.net/Articles/745775/rss

Security updates have been issued by Arch Linux (curl, lib32-curl, lib32-libcurl-compat, lib32-libcurl-gnutls, libcurl-compat, libcurl-gnutls, and rsync), Debian (curl), Fedora (clamav and java-1.8.0-openjdk), openSUSE (apache2), Oracle (kernel), and Ubuntu (linux-kvm and thunderbird).

2018-01-28 чукове

Post Syndicated from Vasil Kolev original https://vasil.ludost.net/blog/?p=3377

“Не го насилвай, вземи по-голям чук”

Каня се от много време да направя debugging workshop, и около мисленето как точно да стане днес стигнах до интересен извод за инструментите, дето ползвам и си правя за дебъгващи цели и като цяло за разни мои начини на работа.

Чукът е хубаво нещо. Какъвто и проблем да имаш, след удара с чука резултатът има същия вид (сплескан) и донякъде ми се вижда като хубава метафора за начина, по който оправям някакви проблеми. Той може да се опише като “най-краткия и прост начин за достигане на нужното крайно състояние, без да има особено значение какво е началното.

Като за пример, тия дни ми се налагаше да подменя едно парче софтуер в 50-тина клъстера, като всеки от тях имаше м/у 3 и 50 машини. Понеже инструментите, които имам са pssh и pscp, се оказа най-лесно на един пас да копирам нужните файлове по всички сървъри, и на втори пас да се логне pssh и ако трябва, да копира където трябва, иначе просто да изтрие това, което бях копирал. Някакъв по-подреден начин би било да извадя списък на всички машини, на които има нужда да се направи действието и да го направя само там, но щях да го напиша и направя по-бавно, отколкото по грубия и бърз начин.

По подобен начин за друг инструмент си бях написал скрипт, който го налива в цял клъстер и отделен, който го update-ва. В един момент осъзнах, че това е тъпо и направих инсталатора така, че да не му пука, ако има вече нещо инсталирано и просто спокойно да може да слага отгоре (както и ако го прекъсна и го пусна пак, да свърши пак нужната работа). Крайният резултат беше, че общото количество код намаля.

Принципът изглежда да може да се приложи към любимите ми начини за дебъгване – това, което ползвай най-често е strace, което спокойно може да се опише като един от най-тежките чукове за дебъгване. Почти без значение какво дебъгвам – компилиран C код, php, python, perl, java – успявам да видя симптомите и да се ориентирам какво става, въпреки че като цяло за всеки от тия езици има специализиран и вероятно доста по-нежен вариант да се гледа какво става.
(искам да отбележа, че има и други тежки случаи – имам колега, който за да смята някакви математически изрази от време на време вместо да си пусне някакъв калкулатор като bc, пуска gdb и прави в него нещо като “p 1024*1024*231/1.1”)

Замислил се бях дали това всъщност не е погрешно и че трябва да се избягва, и стигнах до извода, че не виждам друг работещ начин. Много често ни се налага да дебъгваме чужд код (който сме link-нали/който е под нас някъде/от който зависим, или просто това са ни изсипали) и вариантът да го прочетем и разберем не е опция, понеже в наши дни почти няма проекти, които да могат да бъдат изчетени и опознати за под седмица-две (рекордно малкият код, който в една от фирмите, в които съм работил и търкаляше основните услуги беше около 20000 реда, което е горе-долу в човешките възможности, и пак ще отнеме доста време да се разгледа, а фирмата в това отношение беше сериозно изключение). Това води до нуждата за всякакви помощни средства, за да можем да се справим, понеже човешката глава има сериозни ограничения по темата, и тук на помощ ни идват чуковете, с които всеки проблем може да бъде сведен до пирон (или хлебарка, която трябва да се прасне достатъчно силно).

(да не говорим, че хората искат да пишат умно, и колкото по-умно пишат, толкова по-трудно се дебъгва това, което са сътворили)

Security updates for Friday

Post Syndicated from jake original https://lwn.net/Articles/745493/rss

Security updates have been issued by CentOS (389-ds-base, dhcp, kernel, and nautilus), Debian (curl, openssh, and wireshark), Fedora (clamav, firefox, java-9-openjdk, and poco), Gentoo (clamav), openSUSE (curl, libevent, mupdf, mysql-community-server, newsbeuter, php5, redis, and tre), Oracle (389-ds-base, dhcp, kernel, and nautilus), Slackware (mozilla), and Ubuntu (kernel and linux-hwe, linux-azure, linux-gcp, linux-oem).

SUPER game night 3: GAMES MADE QUICK??? 2.0

Post Syndicated from Eevee original https://eev.ee/blog/2018/01/23/super-game-night-3-games-made-quick-2-0/

Game night continues with a smorgasbord of games from my recent game jam, GAMES MADE QUICK??? 2.0!

The idea was to make a game in only a week while watching AGDQ, as an alternative to doing absolutely nothing for a week while watching AGDQ. (I didn’t submit a game myself; I was chugging along on my Anise game, which isn’t finished yet.)

I can’t very well run a game jam and not play any of the games, so here’s some of them in no particular order! Enjoy!

These are impressions, not reviews. I try to avoid major/ending spoilers, but big plot points do tend to leave impressions.

Weather Quest, by timlmul

short · rpg · jan 2017 · (lin)/mac/win · free on itch · jam entry

Weather Quest is its author’s first shipped game, written completely from scratch (the only vendored code is a micro OO base). It’s very short, but as someone who has also written LÖVE games completely from scratch, I can attest that producing something this game-like in a week is a fucking miracle. Bravo!

For reference, a week into my first foray, I think I was probably still writing my own Tiled importer like an idiot.

Only Mac and Windows builds are on itch, but it’s a LÖVE game, so Linux folks can just grab a zip from GitHub and throw that at love.


Pancake Numbers Simulator, by AnorakThePrimordial

short · sim · jan 2017 · lin/mac/win · free on itch · jam entry

Given a stack of N pancakes (of all different sizes and in no particular order), the Nth pancake number is the most flips you could possibly need to sort the pancakes in order with the smallest on top. A “flip” is sticking a spatula under one of the pancakes and flipping the whole sub-stack over. There’s, ah, a video embedded on the game page with some visuals.

Anyway, this game lets you simulate sorting a stack via pancake flipping, which is surprisingly satisfying! I enjoy cleaning up little simulated messes, such as… incorrectly-sorted pancakes, I guess?

This probably doesn’t work too well as a simulator for solving the general problem — you’d have to find an optimal solution for every permutation of N pancakes to be sure you were right. But it’s a nice interactive illustration of the problem, and if you know the pancake number for your stack size of choice (which I wish the game told you — for seven pancakes, it’s 8), then trying to restore a stack in that many moves makes for a nice quick puzzle.

FINAL SCORE: \(\frac{18}{11}\)

Framed Animals, by chridd

short · metroidvania · jan 2017 · web/win · free on itch · jam entry

The concept here was to kill the frames, save the animals, which is a delightfully literal riff on a long-running AGDQ/SGDQ donation incentive — people vote with their dollars to decide whether Super Metroid speedrunners go out of their way to free the critters who show you how to walljump and shinespark. Super Metroid didn’t have a showing at this year’s AGDQ, and so we have this game instead.

It’s rough, but clever, and I got really into it pretty quickly — each animal you save gives you a new ability (in true Metroid style), and you get to test that ability out by playing as the animal, with only that ability and no others, to get yourself back to the most recent save point.

I did, tragically, manage to get myself stuck near what I think was about to be the end of the game, so some of the animals will remain framed forever. What an unsatisfying conclusion.

Gravity feels a little high given the size of the screen, and like most tile-less platformers, there’s not really any way to gauge how high or long your jump is before you leap. But I’m only even nitpicking because I think this is a great idea and I hope the author really does keep working on it.

FINAL SCORE: $136,596.69

Battle 4 Glory, by Storyteller Games

short · fighter · jan 2017 · win · free on itch · jam entry

This is a Smash Bros-style brawler, complete with the four players, the 2D play area in a 3D world, and the random stage obstacles showing up. I do like the Smash style, despite not otherwise being a fan of fighting games, so it’s nice to see another game chase that aesthetic.

Alas, that’s about as far as it got — which is pretty far for a week of work! I don’t know what more to say, though. The environments are neat, but unless I’m missing something, the only actions at your disposal are jumping and very weak melee attacks. I did have a good few minutes of fun fruitlessly mashing myself against the bumbling bots, as you can see.


Icnaluferu Guild, Year Sixteen, by CHz

short · adventure · jan 2017 · web · free on itch · jam entry

Here we have the first of several games made with bitsy, a micro game making tool that basically only supports walking around, talking to people, and picking up items.

I tell you this because I think half of my appreciation for this game is in the ways it wriggled against those limits to emulate a Zelda-like dungeon crawler. Everything in here is totally fake, and you can’t really understand just how fake unless you’ve tried to make something complicated with bitsy.

It’s pretty good. The dialogue is entertaining (the rest of your party develops distinct personalities solely through oneliners, somehow), the riffs on standard dungeon fare are charming, and the Link’s Awakening-esque perspective walls around the edges of each room are fucking glorious.


The Lonely Tapes, by JTHomeslice

short · rpg · jan 2017 · web · free on itch · jam entry

Another bitsy entry, this one sees you play as a Wal— sorry, a JogDawg, which has lost its cassette tapes and needs to go recover them!

(A cassette tape is like a VHS, but for music.)

(A VHS is—)

I have the sneaking suspicion that I missed out on some musical in-jokes, due to being uncultured swine. I still enjoyed the game — it’s always clear when someone is passionate about the thing they’re writing about, and I could tell I was awash in that aura even if some of it went over my head. You know you’ve done good if someone from way outside your sphere shows up and still has a good time.

FINAL SCORE: Nine… Inch Nails? They’re a band, right? God I don’t know write your own damn joke

Pirate Kitty-Quest, by TheKoolestKid

short · adventure · jan 2017 · win · free on itch · jam entry

I completely forgot I’d even given “my birthday” and “my cat” as mostly-joking jam themes until I stumbled upon this incredible gem. I don’t think — let me just check here and — yeah no this person doesn’t even follow me on Twitter. I have no idea who they are?




This game wins the jam, hands down. 🏆

FINAL SCORE: Yarr, eight pieces o’ eight

CHIPS Mario, by NovaSquirrel

short · platformer · jan 2017 · (lin/mac)/win · free on itch · jam entry

You see this? This is fucking witchcraft.

This game is made with MegaZeux. MegaZeux games look like THIS. Text-mode, bound to a grid, with two colors per cell. That’s all you get.

Until now, apparently?? The game is a tech demo of “unbound” sprites, which can be drawn on top of the character grid without being aligned to it. And apparently have looser color restrictions.

The collision is a little glitchy, which isn’t surprising for a MegaZeux platformer; I had some fun interactions with platforms a couple times. But hey, goddamn, it’s free-moving Mario, in MegaZeux, what the hell.

(I’m looking at the most recently added games on DigitalMZX now, and I notice that not only is this game in the first slot, but NovaSquirrel’s MegaZeux entry for Strawberry Jam last February is still in the seventh slot. RIP, MegaZeux. I’m surprised a major feature like this was even added if the community has largely evaporated?)

FINAL SCORE: n/a, disqualified for being probably summoned from the depths of Hell

d!¢< pic, by 573 Games

short · story · jan 2017 · web · free on itch · jam entry

This is a short story about not sending dick pics. It’s very short, so I can’t say much without spoiling it, but: you are generally prompted to either text something reasonable, or send a dick pic. You should not send a dick pic.

It’s a fascinating artifact, not because of the work itself, but because it’s so terse that I genuinely can’t tell what the author was even going for. And this is the kind of subject where the author was, surely, going for something. Right? But was it genuinely intended to be educational, or was it tongue-in-cheek about how some dudes still don’t get it? Or is it side-eying the player who clicks the obviously wrong option just for kicks, which is the same reason people do it for real? Or is it commentary on how “send a dick pic” is a literal option for every response in a real conversation, too, and it’s not that hard to just not do it — unless you are one of the kinds of people who just feels a compulsion to try everything, anything, just because you can? Or is it just a quick Twine and I am way too deep in this? God, just play the thing, it’s shorter than this paragraph.

I’m also left wondering when it is appropriate to send a dick pic. Presumably there is a correct time? Hopefully the author will enter Strawberry Jam 2 to expound upon this.


Marble maze, by Shtille

short · arcade · jan 2017 · win · free on itch · jam entry

Ah, hm. So this is a maze navigated by rolling a marble around. You use WASD to move the marble, and you can also turn the camera with the arrow keys.

The trouble is… the marble’s movement is always relative to the world, not the camera. That means if you turn the camera 30° and then try to move the marble, it’ll move at a 30° angle from your point of view.

That makes navigating a maze, er, difficult.

Camera-relative movement is the kind of thing I take so much for granted that I wouldn’t even think to do otherwise, and I think it’s valuable to look at surprising choices that violate fundamental conventions, so I’m trying to take this as a nudge out of my comfort zone. What could you design in an interesting way that used world-relative movement? Probably not the player, but maybe something else in the world, as long as you had strong landmarks? Hmm.


Refactor: flight, by fluffy

short · arcade · jan 2017 · lin/mac/win · free on itch · jam entry

Refactor is a game album, which is rather a lot what it sounds like, and Flight is one of the tracks. Which makes this a single, I suppose.

It’s one of those games where you move down an oddly-shaped tunnel trying not to hit the walls, but with some cute twists. Coins and gems hop up from the bottom of the screen in time with the music, and collecting them gives you points. Hitting a wall costs you some points and kills your momentum, but I don’t think outright losing is possible, which is great for me!

Also, the monk cycles through several animal faces. I don’t know why, and it’s very good. One of those odd but memorable details that sits squarely on the intersection of abstract, mysterious, and a bit weird, and refuses to budge from that spot.

The music is great too? Really chill all around.


The Adventures of Klyde

short · adventure · jan 2017 · web · free on itch · jam entry

Another bitsy game, this one starring a pig (humorously symbolized by a giant pig nose with ears) who must collect fruit and solve some puzzles.

This is charmingly nostalgic for me — it reminds me of some standard fare in engines like MegaZeux, where the obvious things to do when presented with tiles and pickups were to make mazes. I don’t mean that in a bad way; the maze is the fundamental environmental obstacle.

A couple places in here felt like invisible teleport mazes I had to brute-force, but I might have been missing a hint somewhere. I did make it through with only a little trouble, but alas — I stepped in a bad warp somewhere and got sent to the upper left corner of the starting screen, which is surrounded by walls. So Klyde’s new life is being trapped eternally in a nowhere space.

FINAL SCORE: 19/20 apples

And more

That was only a third of the games, and I don’t think even half of the ones I’ve played. I’ll have to do a second post covering the rest of them? Maybe a third?

Or maybe this is a ludicrous format for commenting on several dozen games and I should try to narrow it down to the ones that resonated the most for Strawberry Jam 2? Maybe??

Firefox 58 is out

Post Syndicated from ris original https://lwn.net/Articles/745180/rss

Firefox 58 has been released. “With this release, we’re building on the great foundation provided by our all-new Firefox Quantum browser. We’re optimizing the performance gains we released in 57 by improving the way we render graphics and cache JavaScript. We also made functional and privacy improvements to Firefox Screenshots. On Firefox for Android, we’ve added support for Progressive Web Apps (PWAs) so you can add websites to your home screen and use them like native apps.

"Skyfall attack" was attention seeking

Post Syndicated from Robert Graham original http://blog.erratasec.com/2018/01/skyfall-attack-was-attention-seeking.html

After the Meltdown/Spectre attacks, somebody created a website promising related “Skyfall/Solace” attacks. They revealed today that it was a “hoax”.

It was a bad hoax. It wasn’t a clever troll, parody, or commentary. It was childish behavior seeking attention.
For all you hate naming of security vulnerabilities, Meltdown/Spectre was important enough to deserve a name. Sure, from an infosec perspective, it was minor, we just patch and move on. But from an operating-system and CPU design perspective, these things where huge.
Page table isolation to fix Meltdown is a fundamental redesign of the operating system. What you learned in college about how Solaris, Windows, Linux, and BSD were designed is now out-of-date. It’s on the same scale of change as address space randomization.
The same is true of Spectre. It changes what capabilities are given to JavaScript (buffers and high resolution timers). It dramatically increases the paranoia we have of running untrusted code from the Internet. We’ve been cleansing JavaScript of things like buffer-overflows and type confusion errors, now we have to cleanse it of branch prediction issues.

Moreover, not only do we need to change software, we need to change the CPU. No, we won’t get rid of branch-prediction and out-of-order execution, but there things that can easily be done to mitigate these attacks. We won’t be recalling the billions of CPUs already shipped, and it will take a year before fixed CPUs appear on the market, but it’s still an important change. That we fix security through such a massive hardware change is by itself worthy of “names”.

Yes, the “naming” of vulnerabilities is annoying. A bunch of vulns named by their creators have disappeared, and we’ve stopped talking about them. On the other hand, we still talk about Heartbleed and Shellshock, because they were damn important. A decade from now, we’ll still be talking about Meltdown/Spectre. Even if they hadn’t been named by their creators, we still would’ve come up with nicknames to talk about them, because CVE numbers are so inconvenient.
Thus, the hoax’s mocking of the naming is invalid. It was largely incoherent rambling from somebody who really doesn’t understand the importance of these vulns, who uses the hoax to promote themselves.

When You Have A Blockchain, Everything Looks Like a Nail

Post Syndicated from Bozho original https://techblog.bozho.net/blockchain-everything-looks-like-nail/

Blockchain, AI, big data, NoSQL, microservices, single page applications, cloud, SOA. What do these have in common? They have been or are hyped. At some point they were “the big thing” du jour. Everyone was investigating the possibility of using them, everyone was talking about them, there were meetups, conferences, articles on Hacker news and reddit. There are more examples, of course (which is the javascript framework this month?) but I’ll focus my examples on those above.

Another thing they have in common is that they are useful. All of them have some pretty good applications that are definitely worth the time and investment.

Yet another thing they have in common is that they are far from universally applicable. I’ve argued that monoliths are often still the better approach and that microservices introduce too much complexity for the average project. Big Data is something very few organizations actually have; AI/machine learning can help a wide variety of problems, but it is just a tool in a toolbox, not the solution to all problems. Single page applications are great for, yeah, applications, but most websites are still websites, not feature-rich frontends – you don’t need an SPA for every type of website. NoSQL has solved niche issues, and issues of scale that few companies have had, but nothing beats a good old relational database for the typical project out there. “The cloud” is not always where you want your software to be; and SOA just means everything (ESBs, direct integrations, even microservices, according to some). And the blockchain – it seems to be having limited success beyond cryptocurrencies.

And finally, another trait many of them share is that the hype has settled down. Only yesterday I read an article about the “death of the microservices madness”. I don’t see nearly as many new NoSQL databases as a few years ago, some of the projects that have been popular have faded. SOA and “the cloud” are already “boring”, and we’ve realized we don’t actually have big data if it fits in an Excel spreadsheet. SPAs and AI are still high in popularity, but we are getting a good understanding as a community why and when they are useful.

But it seems that nuanced reality has never stopped us from hyping a particular technology or approach. And maybe that’s okay in order to get a promising, though niche, technology, the spotlight and let it shine in the particular usecases where it fits.

But countless projects have and will suffer from our collective inability to filter through these hypes. I’d bet millions of developer hours have been wasted in trying to use the above technologies where they just didn’t fit. It’s like that scene from Idiocracy where a guy tries to fit a rectangular figure into a circular hole.

And the new one is not “the blockchain”. I won’t repeat my rant, but in summary – it doesn’t solve many of the problems companies are trying to solve with it right now just because it’s cool. Or at least it doesn’t solve them better than existing solutions. Many pilots will be carried out, many hours will be wasted in figuring out why that thing doesn’t work. A few of those projects will be a good fit and will actually bring value.

Do you need to reach multi-party consensus for the data you store? Can all stakeholder support the infrastructure to run their node(s)? Do they have the staff to administer the node(s)? Do you need to execute distributed application code on the data? Won’t it be easier to just deploy RESTful APIs and integrate the parties through that? Do you need to store all the data, or just parts of it, to guarantee data integrity?

“If you have is a hammer, everything looks like a nail” as the famous saying goes. In the software industry we repeatedly find new and cool hammers and then try to hit as many nails as we can. But only few of them are actual nails. The rest remain ugly, hard to support, “who was the idiot that wrote this” and “I wasn’t here when the decisions were made” types of projects.

I don’t have the illusion that we will calm down and skip the next hypes. Especially if adding the hyped word to your company raises your stock price. But if there’s one thing I’d like people to ask themselves when choosing a technology stack, it is “do we really need that to solve our problems?”.

If the answer is really “yes”, then great, go ahead and deploy the multi-organization permissioned blockchain, or fork Ethereum, or whatever. If not, you can still do a project a home that you can safely abandon. And if you need some pilot project to figure out whether the new piece of technology would be beneficial – go ahead and try it. But have a baseline – the fact that it somehow worked doesn’t mean it’s better than old, tested models of doing the same thing.

The post When You Have A Blockchain, Everything Looks Like a Nail appeared first on Bozho's tech blog.

Security updates for Monday

Post Syndicated from ris original https://lwn.net/Articles/745042/rss

Security updates have been issued by CentOS (bind), Debian (openocd), Mageia (unbound), Oracle (bind and microcode_ctl), Red Hat (bind, java-1.6.0-sun, libvirt, and qemu-kvm), Scientific Linux (bind), SUSE (kernel and perl-XML-LibXML), and Ubuntu (gimp, intel-microcode, mysql-5.5, mysql-5.7, and openssh).

Security updates for Friday

Post Syndicated from ris original https://lwn.net/Articles/744791/rss

Security updates have been issued by Arch Linux (bind, irssi, nrpe, perl-xml-libxml, and transmission-cli), CentOS (java-1.8.0-openjdk), Debian (awstats, libgd2, mysql-5.5, rsync, smarty3, and transmission), Fedora (keycloak-httpd-client-install and rootsh), and Red Hat (java-1.7.0-oracle and java-1.8.0-oracle).