The 9.2 release of Komodo IDE is live and includes new features such as Docker and Vagrant integration, collaboration improvements, a package installer, and UI changes.
Learn how to use the service registration and discovery services in ZooKeeper to manage microservices when refactoring from an existing monolithic application.
Liquibase is a great tool, and comparable to Git for databases. While it might not technically be a source control system, it's packed with similar functionality.
Distributed transaction tracing is a useful way to monitor or evaluate your microservices architecture for performance, particularly when measuring end-to-end requests.
In this article, excerpted from the book Docker in Action, I will show you how to open access to shared memory between containers. Linux provides a few tools for sharing memory between processes running on the same computer. This form of inter-process communication (IPC) performs at memory speeds. It is often used when the latency associated with network or pipe based IPC drags software performance below requirements. The best examples of shared memory based IPC usage is in scientific computing and some popular database technologies like PostgreSQL. Docker creates a unique IPC namespace for each container by default. The Linux IPC namespace partitions shared memory primitives like named shared memory blocks and semaphores, as well as message queues. It is okay if you are not sure what these are. Just know that they are tools used by Linux programs to coordinate processing. The IPC namespace prevents processes in one container from accessing the memory on the host or in other containers. Sharing IPC Primitives Between Containers I’ve created an image named allingeek/ch6_ipc that contains both a producer and consumer. They communicate using shared memory. Listing 1 will help you understand the problem with running these in separate containers. Listing 1: Launch a Communicating Pair of Programs # start a producer docker -d -u nobody --name ch6_ipc_producer \ allingeek/ch6_ipc -producer # start the consumer docker -d -u nobody --name ch6_ipc_consumer \ allingeek/ch6_ipc -consumer Listing 1 starts two containers. The first creates a message queue and starts broadcasting messages on it. The second should pull from the message queue and write the messages to the logs. You can see what each is doing by using the following commands to inspect the logs of each: docker logs ch6_ipc_producer docker logs ch6_ipc_consumer If you executed the commands in Listing 1 something should be wrong. The consumer never sees any messages on the queue. Each process used the same key to identify the shared memory resource but they referred to different memory. The reason is that each container has its own shared memory namespace. If you need to run programs that communicate with shared memory in different containers, then you will need to join their IPC namespaces with the --ipc flag. The --ipc flag has a container mode that will create a new container in the same IPC namespace as another target container. Listing 2: Joining Shared Memory Namespaces # remove the original consumer docker rm -v ch6_ipc_consumer # start a new consumer with a joined IPC namespace docker -d --name ch6_ipc_consumer \ --ipc container:ch6_ipc_producer \ allingeek/ch6_ipc -consumer Listing 2 rebuilds the consumer container and reuses the IPC namespace of the ch6_ipc_producer container. This time the consumer should be able to access the same memory location where the server is writing. You can see this working by using the following commands to inspect the logs of each: docker logs ch6_ipc_producer docker logs ch6_ipc_consumer Remember to cleanup your running containers before moving on: # remember: the v option will clean up volumes, # the f option will kill the container if it is running, # and the rm command takes a list of containers docker rm -vf ch6_ipc_producer ch6_ipc_consumer There are obvious security implications to reusing the shared memory namespaces of containers. But this option is available if you need it. Sharing memory between containers is safer alternative to sharing memory with the host.
This technical tip explains how to add a watermark to a document in Microsoft Word document inside Android Applications. Sometimes you need to insert a watermark into a Word document, for instance if you would like to print a draft document or mark it as confidential. In Microsoft Word, you can quickly insert a watermark using the Insert Watermark command. Not many people using this command realize that such “watermark” is just a shape with text inserted into a header or footer and positioned in the centre of the page. While Aspose.Words doesn't have a single insert watermark command like Microsoft Word, it is very easy to insert any shape or image into a header or footer and thus create a watermark of any imaginable type. The code below inserts a watermark into a Word document. [Java Code Sample] package AddWatermark; import java.awt.Color; import java.io.File; import java.net.URI; import com.aspose.words.Document; import com.aspose.words.Shape; import com.aspose.words.ShapeType; import com.aspose.words.RelativeHorizontalPosition; import com.aspose.words.RelativeVerticalPosition; import com.aspose.words.WrapType; import com.aspose.words.VerticalAlignment; import com.aspose.words.HorizontalAlignment; import com.aspose.words.Paragraph; import com.aspose.words.Section; import com.aspose.words.HeaderFooterType; import com.aspose.words.HeaderFooter; public class Program { public static void main(String[] args) throws Exception { // Sample infrastructure. URI exeDir = Program.class.getResource("").toURI(); String dataDir = new File(exeDir.resolve("../../Data")) + File.separator; Document doc = new Document(dataDir + "TestFile.doc"); insertWatermarkText(doc, "CONFIDENTIAL"); doc.save(dataDir + "TestFile Out.doc"); } /** * Inserts a watermark into a document. * * @param doc The input document. * @param watermarkText Text of the watermark. */ private static void insertWatermarkText(Document doc, String watermarkText) throws Exception { // Create a watermark shape. This will be a WordArt shape. // You are free to try other shape types as watermarks. Shape watermark = new Shape(doc, ShapeType.TEXT_PLAIN_TEXT); // Set up the text of the watermark. watermark.getTextPath().setText(watermarkText); watermark.getTextPath().setFontFamily("Arial"); watermark.setWidth(500); watermark.setHeight(100); // Text will be directed from the bottom-left to the top-right corner. watermark.setRotation(-40); // Remove the following two lines if you need a solid black text. watermark.getFill().setColor(Color.GRAY); // Try LightGray to get more Word-style watermark watermark.setStrokeColor(Color.GRAY); // Try LightGray to get more Word-style watermark // Place the watermark in the page center. watermark.setRelativeHorizontalPosition(RelativeHorizontalPosition.PAGE); watermark.setRelativeVerticalPosition(RelativeVerticalPosition.PAGE); watermark.setWrapType(WrapType.NONE); watermark.setVerticalAlignment(VerticalAlignment.CENTER); watermark.setHorizontalAlignment(HorizontalAlignment.CENTER); // Create a new paragraph and append the watermark to this paragraph. Paragraph watermarkPara = new Paragraph(doc); watermarkPara.appendChild(watermark); // Insert the watermark into all headers of each document section. for (Section sect : doc.getSections()) { // There could be up to three different headers in each section, since we want // the watermark to appear on all pages, insert into all headers. insertWatermarkIntoHeader(watermarkPara, sect, HeaderFooterType.HEADER_PRIMARY); insertWatermarkIntoHeader(watermarkPara, sect, HeaderFooterType.HEADER_FIRST); insertWatermarkIntoHeader(watermarkPara, sect, HeaderFooterType.HEADER_EVEN); } } private static void insertWatermarkIntoHeader(Paragraph watermarkPara, Section sect, int headerType) throws Exception { HeaderFooter header = sect.getHeadersFooters().getByHeaderFooterType(headerType); if (header == null) { // There is no header of the specified type in the current section, create it. header = new HeaderFooter(sect.getDocument(), headerType); sect.getHeadersFooters().add(header); } // Insert a clone of the watermark into the header. header.appendChild(watermarkPara.deepClone(true)); } }
This example is an improved version of my previous example Android GridView Example. Instead of using static images to display the grid items, let's make this example more realistic by downloading the data in real-time from the server and rendering the grid items. The following video depicts the output of this example. Without wasting much time, let us jump straight into what it takes to build this kind of GridView. You need to follow the following steps to complete this example. 1. Add GridView in Activity Layout First, create a new android project. For this example, I prefer to use Android Studio. Create a new layout file to your project res/layout folder and name it as activity_grid_view.xml. And add the following code blocks. The above layout is pretty straightforward. We have declared an GridView and a ProgressBar in activity layout. The progress bar will be displayed when the data is downloaded. 2. Declare GridView Item Layout Let us now add another file named grid_item_layout.xml to res/layout folder. This layout will be used by a custom grid adapter for laying out individual grid items. For the sake of simplicity, we are adding an ImageView and a TextView. 3. Adding Internet Permission You might be aware that, the Android application must declare all the permissions that are required for the application. As we need to download the data from the server, we need to add INTERNET permission. Add the following line to AndroidManifest.xml the file. Notice that we have also declared all the activities used in the application. 4. Adding Picasso Image Downloading Library Android open-source developer community brings some interesting libraries that can be integrated easily into Android applications. They serve a great deal of purpose and save a lot of time. Here in this example, I am talking about Picasso the image-loading library. We will add the Picasso library for downloading and caching images. Visit here to learn more about how to use the Picasso library on Android. You can add the Picasso library by adding the following dependency to the build.gradle file. dependencies { compile fileTree(dir: 'libs', include: ['*.jar']) compile 'com.android.support:appcompat-v7:21.0.3' compile 'com.squareup.picasso:picasso:2.5.2' } 5. Create a GridView Custom Adapter A grid view is an adapter view. It requires an adapter to render the collection of data items. Add a new class named GridViewAdapter.java to your project and add the following code snippets. package com.javatechig.gridviewexample; import java.util.ArrayList; import android.app.Activity; import android.content.Context; import android.text.Html; import android.view.LayoutInflater; import android.view.View; import android.view.ViewGroup; import android.widget.ArrayAdapter; import android.widget.ImageView; import android.widget.TextView; import com.squareup.picasso.Picasso; public class GridViewAdapter extends ArrayAdapter { private Context mContext; private int layoutResourceId; private ArrayList mGridData = new ArrayList(); public GridViewAdapter(Context mContext, int layoutResourceId, ArrayList mGridData) { super(mContext, layoutResourceId, mGridData); this.layoutResourceId = layoutResourceId; this.mContext = mContext; this.mGridData = mGridData; } /** * Updates grid data and refresh grid items. * @param mGridData */ public void setGridData(ArrayList mGridData) { this.mGridData = mGridData; notifyDataSetChanged(); } @Override public View getView(int position, View convertView, ViewGroup parent) { View row = convertView; ViewHolder holder; if (row == null) { LayoutInflater inflater = ((Activity) mContext).getLayoutInflater(); row = inflater.inflate(layoutResourceId, parent, false); holder = new ViewHolder(); holder.titleTextView = (TextView) row.findViewById(R.id.grid_item_title); holder.imageView = (ImageView) row.findViewById(R.id.grid_item_image); row.setTag(holder); } else { holder = (ViewHolder) row.getTag(); } GridItem item = mGridData.get(position); holder.titleTextView.setText(Html.fromHtml(item.getTitle())); Picasso.with(mContext).load(item.getImage()).into(holder.imageView); return row; } static class ViewHolder { TextView titleTextView; ImageView imageView; } } Notice the following in the above code snippets, The setGridData() method updates the data display on GridView. The Picasso.with().load() the method is used to download the image from the URL and display it on the image view. The GridViewAdapter class constructor requires the id of the grid item layout and the list of data to operate on. You might be surprised, where the GridItem class came from. It's not magic, we need to add GridItem.java class to our project. The GridItem class looks as follows. 6. Download Data and Hook it to the Activity Now we will be heading towards hooking the adapter to GridView and making it functional. Create a new Java class and name it as GridViewActivity.java and perform the following steps. Override the onCreate() method and set the layout by calling setContentView() method Initialize the GridView and ProgressBar components by using their declared layout id. Initialize the CustomGridView adapter bypassing the grid row layout id and the list of GridItem objects. Use AsyncTask to download data from the server, once the download is successful read the stream JSON response. Parse the JSON string into the list of GridItem objects. Once downloading and parsing is completed, in onPostExecute() callback update the UI elements. The following code does all the above steps as described. Add the following code to GridViewActivity class. import java.io.BufferedReader; import java.io.IOException; import java.io.InputStream; import java.io.InputStreamReader; import java.util.ArrayList; import android.content.Intent; import android.os.AsyncTask; import android.os.Bundle; import android.support.v7.app.ActionBarActivity; import android.util.Log; import android.view.View; import android.widget.AdapterView; import android.widget.GridView; import android.widget.ProgressBar; import android.widget.Toast; import org.apache.http.HttpResponse; import org.apache.http.client.HttpClient; import org.apache.http.client.methods.HttpGet; import org.apache.http.impl.client.DefaultHttpClient; import org.json.JSONArray; import org.json.JSONException; import org.json.JSONObject; public class GridViewActivity extends ActionBarActivity { private static final String TAG = GridViewActivity.class.getSimpleName(); private GridView mGridView; private ProgressBar mProgressBar; private GridViewAdapter mGridAdapter; private ArrayList mGridData; private String FEED_URL = "http://javatechig.com/?json=get_recent_posts&count=45"; @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.activity_gridview); mGridView = (GridView) findViewById(R.id.gridView); mProgressBar = (ProgressBar) findViewById(R.id.progressBar); //Initialize with empty data mGridData = new ArrayList<>(); mGridAdapter = new GridViewAdapter(this, R.layout.grid_item_layout, mGridData); mGridView.setAdapter(mGridAdapter); //Start download new AsyncHttpTask().execute(FEED_URL); mProgressBar.setVisibility(View.VISIBLE); } //Downloading data asynchronously public class AsyncHttpTask extends AsyncTask { @Override protected Integer doInBackground(String... params) { Integer result = 0; try { // Create Apache HttpClient HttpClient httpclient = new DefaultHttpClient(); HttpResponse httpResponse = httpclient.execute(new HttpGet(params[0])); int statusCode = httpResponse.getStatusLine().getStatusCode(); // 200 represents HTTP OK if (statusCode == 200) { String response = streamToString(httpResponse.getEntity().getContent()); parseResult(response); result = 1; // Successful } else { result = 0; //"Failed } } catch (Exception e) { Log.d(TAG, e.getLocalizedMessage()); } return result; } @Override protected void onPostExecute(Integer result) { // Download complete. Let us update UI if (result == 1) { mGridAdapter.setGridData(mGridData); } else { Toast.makeText(GridViewActivity.this, "Failed to fetch data!", Toast.LENGTH_SHORT).show(); } mProgressBar.setVisibility(View.GONE); } } String streamToString(InputStream stream) throws IOException { BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(stream)); String line; String result = ""; while ((line = bufferedReader.readLine()) != null) { result += line; } // Close stream if (null != stream) { stream.close(); } return result; } /** * Parsing the feed results and get the list * @param result */ private void parseResult(String result) { try { JSONObject response = new JSONObject(result); JSONArray posts = response.optJSONArray("posts"); GridItem item; for (int i = 0; i < posts.length(); i++) { JSONObject post = posts.optJSONObject(i); String title = post.optString("title"); item = new GridItem(); item.setTitle(title); JSONArray attachments = post.getJSONArray("attachments"); if (null != attachments && attachments.length() > 0) { JSONObject attachment = attachments.getJSONObject(0); if (attachment != null) item.setImage(attachment.getString("url")); } mGridData.add(item); } } catch (JSONException e) { e.printStackTrace(); } } } At this point, you will be able to run the app and notice that the app will download the data from the server and display it on GridView. 7. Handle GridView Click Event Right now GridView is not responding to user clicks. Let us make it more functional by adding the following code. mGridView.setOnItemClickListener(new AdapterView.OnItemClickListener() { public void onItemClick(AdapterView parent, View v, int position, long id) { //Get item at position GridItem item = (GridItem) parent.getItemAtPosition(position); //Pass the image title and url to DetailsActivity Intent intent = new Intent(GridViewActivity.this, DetailsActivity.class); intent.putExtra("title", item.getTitle()); intent.putExtra("image", item.getImage()); //Start details activity startActivity(intent); } }); When a user clicks on a grid item, we will start another activity that displays the full-screen image. You can start one activity from another by calling startActivity() method. We need to pass the details of the item such as the title, and image URL for displaying it on DetailsActivity. 8. Create Details Activity Layout Add a new layout file to res/layout directory, and name it as activity_details_view.xml and add the following code snippets. 9. Completing the Details Activity The DetailsActivity retrieves the details passed from GridViewActivity and renders the details on the screen. Create a new class named DetailsActivity and add the following code snippets. package com.javatechig.gridviewexample; import android.os.Bundle; import android.support.v7.app.ActionBar; import android.support.v7.app.ActionBarActivity; import android.text.Html; import android.widget.ImageView; import android.widget.TextView; import com.squareup.picasso.Picasso; public class DetailsActivity extends ActionBarActivity { private TextView titleTextView; private ImageView imageView; @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.activity_details_view); ActionBar actionBar = getSupportActionBar(); actionBar.hide(); String title = getIntent().getStringExtra("title"); String image = getIntent().getStringExtra("image"); titleTextView = (TextView) findViewById(R.id.title); imageView = (ImageView) findViewById(R.id.grid_item_image); titleTextView.setText(Html.fromHtml(title)); Picasso.with(this).load(image).into(imageView); } } 10. Download the Complete Example Download from GitHub. 11. Custom Activity Transition in GridView Continue reading in our next tutorial.
[This article was written by Sveta Smirnova] Like any good, thus lazy, engineer I don’t like to start things manually. Creating directories, configuration files, specify paths, ports via command line is too boring. I wrote already how I survive in case when I need to start MySQL server (here). There is also the MySQL Sandbox which can be used for the same purpose. But what to do if you want to start Percona XtraDB Cluster this way? Fortunately we, at Percona, have engineers who created automation solution for starting PXC. This solution uses Docker. To explore it you need: Clone the pxc-docker repository:git clone https://github.com/percona/pxc-docker Install Docker Compose as described here cd pxc-docker/docker-bld Follow instructions from the README file: a) ./docker-gen.sh 5.6 (docker-gen.sh takes a PXC branch as argument, 5.6 is default, and it looks for it on github.com/percona/percona-xtradb-cluster) b) Optional: docker-compose build (if you see it is not updating with changes). c) docker-compose scale bootstrap=1 members=2 for a 3 node cluster Check which ports assigned to containers: $docker port dockerbld_bootstrap_1 3306 0.0.0.0:32768 $docker port dockerbld_members_1 4567 0.0.0.0:32772 $docker port dockerbld_members_2 4568 0.0.0.0:32776 Now you can connect to MySQL clients as usual: $mysql -h 0.0.0.0 -P 32768 -uroot Welcome to the MySQL monitor. Commands end with ; or g. Your MySQL connection id is 10 Server version: 5.6.21-70.1 MySQL Community Server (GPL), wsrep_25.8.rXXXX Copyright (c) 2009-2015 Percona LLC and/or its affiliates Copyright (c) 2000, 2015, Oracle and/or its affiliates. All rights reserved. Oracle is a registered trademark of Oracle Corporation and/or its affiliates. Other names may be trademarks of their respective owners. Type 'help;' or 'h' for help. Type 'c' to clear the current input statement. mysql> 6. To change MySQL options either pass it as a mount at runtime with something like volume: /tmp/my.cnf:/etc/my.cnf in docker-compose.yml or connect to container’s bash (docker exec -i -t container_name /bin/bash), then change my.cnf and run docker restart container_name Notes. If you don’t want to build use ready-to-use images If you don’t want to run Docker Compose as root user add yourself to docker group
written by tim brock. scatter plots are a wonderful way of showing ( apparent ) relationships in bivariate data. patterns and clusters that you wouldn't see in a huge block of data in a table can become instantly visible on a page or screen. with all the hype around big data in recent years it's easy to assume that having more data is always an advantage. but as we add more and more data points to a scatter plot we can start to lose these patterns and clusters. this problem, a result of overplotting, is demonstrated in the animation below. the data in the animation above is randomly generated from a pair of simple bivariate distributions. the distinction between the two distributions becomes less and less clear as we add more and more data. so what can we do about overplotting? one simple option is to make the data points smaller. (note this is a poor "solution" if many data points share exactly the same values.) we can also make them semi-transparent. and we can combine these two options: these refinements certainly help when we have ten thousand data points. however, by the time we've reached a million points the two distributions have seemingly merged in to one again. making points smaller and more transparent might help things; nevertheless, at some point we may have to consider a change of visualization. we'll get on to that later. but first let's try to supplement our visualization with some extra information. specifically let's visualize the marginal distributions . we have several options. there's far too much data for a rug plot , but we can bin the data and show histograms . or we can use a smoother option - a kernel density plot . finally, we could use the empirical cumulative distribution . this last option avoids any binning or smoothing but the results are probably less intuitive. i'll go with the kernel density option here, but you might prefer a histogram. the animated gif below is the same as the gif above but with the smoothed marginal distributions added. i've left scales off to avoid clutter and because we're only really interested in rough judgements of relative height. adding marginal distributions, particularly the distribution of variable 2, helps clarify that two different distributions are present in the bivariate data. the twin-peaked nature of variable 2 is evident whether there are a thousand data points or a million. the relative sizes of the two components is also clear. by contrast, the marginal distribution of variable 1 only has a single peak, despite coming from two distinct distributions. this should make it clear that adding marginal distributions is by no means a universal solution to overplotting in scatter plots. to reinforce this point, the animation below shows a completely different set of (generated) data points in a scatter plot with marginal distributions. the data again comes from a random sample of two different 2d distributions, but both marginal distributions of the complete dataset fail to highlight this separation. as previously, when the number of data points is large the distinction between the two clusters can't be seen from the scatter plot either. returning to point size and opacity, what do we get if we make the data points very small and almost completely transparent? we can now clearly distinguish two clusters in each dataset. it's difficult to make out any fine detail though. since we've lost that fine detail anyway, it seems apt to question whether we really want to draw a million data points. it can be tediously slow and impossible in certain contexts. 2d histograms are an alternative. by binning data we can reduce the number of points to plot and, if we pick an appropriate color scale, pick out some of the features that were lost in the clutter of the scatter plot. after some experimenting i picked a color scale that ran from black through green to white at the high end. note, this is (almost) the reverse of the effect created by overplotting in the scatter plots above. in both 2d histograms we can clearly see the two different clusters representing the two distributions from which the data is drawn. in the first case we can also see that there are more counts from the upper-left cluster than the bottom-right cluster, a detail that is lost in the scatter plot with a million data points (but more obvious from the marginal distributions). conversely, in the case of the second dataset we can see that the "heights" of the two clusters are roughly comparable. 3d charts are overused, but here (see below) i think they actually work quite well in terms of providing a broad picture of where the data is and isn't concentrated. feature occlusion is a problem with 3d charts so if you're going to go down this route when exploring your own data i highly recommend using software that allows for user interaction through rotation and zooming. in summary, scatter plots are a simple and often effective way of visualizing bivariate data. if, however, your chart suffers from overplotting, try reducing point size and opacity. failing that, a 2d histogram or even a 3d surface plot may be helpful. in the latter case be wary of occlusion.
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