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globalmediacampaign · 5 years ago

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Streaming data to Amazon Managed Streaming for Apache Kafka using AWS DMS

Introduction AWS Database Migration Service (DMS) announced support of Amazon Managed Streaming for Apache Kafka (Amazon MSK) and self-managed Apache Kafka clusters as target. With AWS DMS you can replicate ongoing changes from any DMS supported sources such as Amazon Aurora (MySQL and PostgreSQL-compatible), Oracle, and SQL Server to Amazon Managed Streaming for Apache Kafka (Amazon MSK) and self-managed Apache Kafka clusters. In this post, we use an ecommerce use case and set up the entire pipeline with the order data being persisted in an Aurora MySQL database. We use AWS DMS to load and replicate this data to Amazon MSK. We then use the data to generate a live graph on our dashboard application. Then the data will be used to generate a live graph on our dashboard application. We will also discuss about how to use a custom S3 feeder application to send the data from MSK topics to an S3 bucket. Once the setup is completed, any new incoming orders into your Aurora MySQL database will be replicated and reflected on your dashboard graph in real time. Solution overview We divide this solution into two sections: Section 1: Setting up infrastructure for your pipeline which feeds a live dashboard to showcase incoming order data. Section 2: Consuming the data streams coming to Amazon MSK and pushing to Amazon S3 and query it with Amazon Athena. You can find the accompanying dashboards and sample applications in the GitHub repo. The repo contains the sample Java applications that we use in both sections in this post. It contains three modules: Dashboard A dashboard application showing incoming orders data and displaying it by states. This module contains a Spring Boot based Kafka listener. It shows how to build a custom application to listen to an incoming stream of data in Kafka topics and send it to a live dashboard. It uses a websocket connection to connect to the server and open source chartjs to build a simple graph on the data. Data-gen-utility You can use this small command line utility to generate dummy order data to feed to the source MySQL database. Msk-to-s3-feeder This independent Spring Boot application shows how you can take streaming data from Amazon MSK and implement a batch listener to club streaming data and feed it to an S3 bucket that you provide in one or more objects. Solution architecture The following architecture diagram shows how to replicate data in a database like MySQL to Amazon MSK in near-real time using AWS DMS. For our ecommerce use case, data is being generated and stored in our transaction database. This data flows to the live dashboard and Amazon S3 via AWS DMS and Amazon MSK. Setting up your pipeline In this section, you setup a pipeline to feed data to the live dashboard Creating an IAM role If you don’t have dms-vpc-role in your account, create an IAM role named “dms-vpc-role” for AWS DMS API. On the IAM console, choose Roles. Choose Create role. For Choose a use case, choose DMS”. For Filter policies, search for dms. Select permissions AmazonDMSCloudWatchLogsRole and AmazonDMSVPCManagementRole. Provisioning your resources You create the resources for this use case with the provided AWS CloudFormation stack. Run the stack in the region of your choice: The CloudFormation stack requires that you input parameters to configure the infrastructure to migrate the data into Managed streams of Kafka. A few parameters have default values filled. Please add two values below. IPCidrToAllowTraffic – An IP address to SSH to the client instance. Generally you provide your own system IP (such as x.x.x.x/32). KeyName – The key pair name for your client Amazon Elastic Compute Cloud (Amazon EC2) instance. You can choose an existing keypair or create a new one. Select the check box to allow CloudFormation to create IAM roles. When the stack is complete, you can find the status on the AWS CloudFormation console. The stack creates the following resources: EC2 instance: Streaming-DMS-MSK-Pipeline Amazon MSK cluster: MSKMMCluster1 MySQL source endpoint Target Amazon MSK endpoint AWS DMS task (full load and CDC) AWS Lambda functions: CustomClusterConfigLambda, CustomKafkaEndpointLambda and TestConnLambda Aurora MySQL DB cluster. Setting up source data generation After you create your resources, complete the follow steps to set up the source data generation on the client EC2 instance. Login to Amazon EC2 using putty (Windows) or terminal (MacBook) with the following code: ssh –i “ [email protected] Download the sample code: git clone https://github.com/aws-samples/aws-dms-msk-demo.git Enter the following code to build the applications: cd aws-dms-msk-demo mvn clean install Enter the following code to connect to MySQL. Replace the hostname with the Aurora MySQL DB host endpoint (writer endpoint) that was created by the CloudFormation stack. You can find it on the Amazon Relational Database service (Amazon RDS) console for the database. The default username is ‘master’ and the default password is ‘Password1’ unless you changed it in the CloudFormation stack parameters. MYSQL –u -p -h At the SQL prompt, enter the following code to create the sample table ‘orders’ in the database ‘testdb’: SQL > create table orders (orderid bigint(20) NOT NULL, source varchar(45) NOT NULL default 'andriod', amount varchar(45) NOT NULL default '0', state varchar(45) NOT NULL default 'New Jersey', date datetime NOT NULL default current_timestamp, Primary key (orderid)); Ensure that the binary logs are available to AWS DMS because Amazon-managed MySQL-compatible databases purge the binary logs as soon as possible, You should increase the length of time that the logs remain available. To increase log retention to 24 hours, enter the following command: call mysql.rds_set_configuration('binlog retention hours', 24); Hit cmd + z and come out of the SQL prompt. Setting up your dashboard application: You can now set up the sample dashboard application on your EC2 instance. You first get the Amazon MSK broker URLs On the Amazon MSK console, choose the cluster you created Choose view client information. Locate the broker connection details in plaintext. You want the plaintext link and not the TLS, because TLS requires some extra configuration on the client side to work. For more information about connecting via TLS, see Client Authentication. Run the following code on the client EC2 Instance to launch dashboard: java -jar aws-dms-msk-demo/dashboard/target/dashboard-1.0.jar --kafka.bootstrapEndpoints :9092 --kafka.topic dms-blog Open the application web URL in the browser. http://:8080/ The application looks similar to the following screenshot: This dashboard is volatile and all data is reset when you refresh your browser. Generating data on the source engine Now that we have the infrastructure and the pipelines set up, you can generate the data on the MySQL source engine and check the dashboard: Open a new SSH session to Amazon EC2. Use the datagen.jar utility present in the cloned GitHub repo to generate sample data in bulk of 2000 records. java -jar aws-dms-msk-demo/data-gen-utility/target/datagen.jar When prompted for the start index, enter 1. The *.sql file is generated with 2000 dummy order records. Connect to the database again using the following command and ingest the SQL file generated. It inserts your dummy data into database. MySQL –u -p database_name –h testdb --start-replication-task-type start-replication Check the task logs to confirm that the task migrated the data onto the target Amazon MSK. 2020-05-15T20:46:41 [SOURCE_UNLOAD ]I: Unload finished for table 'testdb'.'orders' (Id = 1). 2000 rows sent. (streamcomponent.c:3485) 2020-05-15T20:46:41 [TARGET_LOAD ]I: Load finished for table 'testdb'.'orders' (Id = 1). 2000 rows received. 0 rows skipped. Volume transferred 0. (streamcomponent.c:3770) Check the sample dashboard graphs that now shows the replicated data. You can generate more sample data following these steps and migrate the data onto the target. To test the ongoing changes and check the dashboard for replicated data, connect to the Aurora cluster and perform DML and DDL operations. The sample dashboard graph is incremented for inserts. For updates and deletes, only the counters on the app change. Consuming the data streams and querying the data: In this section, you consume the data streams pushed to Amazon S3 and use Athena to query the data. You must have an S3 bucket to push the data from the MSK topics to, and an external table in Athena pointing to that bucket. Creating your S3 bucket and consuming the data Complete the following steps: Create an s3 bucket in your account to act as the destination. Go to the SSH session where the dashboard application was running and stop the application by pressing CTRL/CMD + C. Run the msk-to-s3-feeder application using the following code: java –jar msk-to-s3-feeder.jar –aws.s3.bucketName --kafka.bootstrapEndpoints :9092 --kafka.topic dms-blog In a separate SSH window, generate 3000 records and insert via the MySQL command line to Aurora (see the previous section for the relevant code). You should see some msk-to-s3-feeder application logs Confirm the files were delivered to Amazon S3 via the AWS CLI: aws s3 ls s3:///output/ --recursive You should see entries like the following code: 2020-06-19 17:02:42 560 output/660fff82-3501-4476-b29a-66d6028b4c841592141618007.json Creating a table and querying the data Create the sample table named ‘kafkajson’ in database: ‘default’ (replace the bucket name before running): SQL> CREATE EXTERNAL TABLE IF NOT EXISTS default.kafkajson ( orderId int, source string, amount decimal, state string, date timestamp, operation string ) ROW FORMAT SERDE ‘org.openx.jsonserde.JsonSerDe’ With SEDREPROPERTIES ( ‘Serialization.format’ = ‘1’ ) LOCATION ‘s3://bsd-martech/output/’ TBLPROPERTIES (‘has_encrypted_data’ =’false’); The following screenshot shows the preceding code on the console. After you provide the required details and create the table, you can run a query and return the following result set: SQL> SELECT * FROM “default”.”kafkajson” The following screenshot shows your results. Cleaning Up To avoid incurring future charges, clean up the resources used in this post. On the AWS CloudFormation console, delete the CloudFormation stack. On the AWS DMS console, delete “dms-blog-kafka-target” AWS DMS endpoints. On the AWS DMS console, delete any log groups with name “Streaming-DMS-MSK” (or use the stack name if you changed it while creating the stack). On the Amazon S3 console, clean the folder output and delete the S3 bucket. On the Athena console, delete the Athena table you created. Conclusion In this post, we showed you how to set up a pipeline with a CloudFormation template to get data from a MySQL database and send it to Amazon MSK topics in real time. We also set up custom applications to consume the data from Amazon MSK and display dashboards based on it in real time. We pushed the data from Amazon S3 using a simple application and queried the data via Athena. We hope this post helps you get started on building similar pipelines. Good luck and happy building! About the authors Harish Bannai is a Technical Account Manager at AWS. He holds the AWS Solutions Architect Professional, Developer Associate and Solutions Architect Professional certifications. He works with enterprise customers providing technical assistance on RDS, Database Migration services operational performance and sharing database best practices. Aswin Sankarapillai is a database engineer in Database Migration Service at AWS. He works with our customers to provide guidance and technical assistance on database migration projects, helping them improve the value of their solutions when using AWS. Amandeep Bhatia works as an Architect. He is passionate about cloud technologies and focuses on building large distributed systems on cloud for various startups and enterprises. He has helped many large scale migrations to AWS as well as building cloud native architectures. In his free time he likes to learn new technologies and spend quality time with kids and family. https://aws.amazon.com/blogs/database/streaming-data-to-amazon-managed-streaming-for-apache-kafka-using-aws-dms/

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craigbrownphd-blog-blog · 7 years ago

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How to integrate Hadoop and Teradata using SQL-H

#ICYDK: I have tried Hadoop Connector for Teradata, Teradata Connector for Hadoop, Teradata Studio Express, Aster SQL-H, and many more cumbersome alternatives, finally to reach the Hadoop-Teradata integration without purchasing QueryGrid current version. However, without QueryGrid, you cannot do cross-platform querying. Here, we just demonstrate bidirectional data transfer between Teradata and Hadoop. All that I needed for Teradata seamlessly integrate with Hadoop were these: * Hadoop Sandbox 2.1 for VMware (http://hortonworks.com/hdp/downloads) * Teradata Express 15 for VMware (http://downloads.teradata.com/downloads) * Teradata Connector for Hadoop (TDCH) (http://downloads.teradata.com/downloads) * Teradata Studio (http://downloads.teradata.com/downloads) I didnt need to connect Teradata Aster, because all I needed was querying and data transfer between Hadoop and TD. Here is how it happened: 1. I converted the OVA file I got from Hortonworks Sandbox download page, into a VMX file for running into VMware Server. The command for converting is this ovftool.exe Hortonworks_Sandbox_2.1.ova D:/HDP_2.1_Extracted/HDP_2.1_vmware where HDP_2.1_vmware is the VMDK file extracted. The extraction took an hour on a fast server. 2. I loaded the HDP_2.1_vmware.vmdk into VMware Server by choosing to add a new virtual machine. VMDK file made the VMX as I specified the VM configurations. I chose NAT for network connection, also chose USB driver option for VM. When turning on the VM, it asked the question that SCSI device (USB) is not working so should the VM boot from IDE. Thats the recommended option so I chose it. VM worked, run and I could browse into Hortonworks Sandbox by typing http://sandbox.hortonworks.com:8000. I could also use the port 50070 to access WebHDFS. I just changed the password for hue in the user admin section of the site at http://sandbox.hortonworks.com:8000. 3. Now I needed to install Teradata 15 and Teradata Studio and connect the two. It worked well, and there is a lot of documentation to troubleshoot if anything comes in connecting TD15 to Teradata Studio. When I could not connect TD15 the first time, I got error in Teradata Administrator “Connection Refused”. I just restarted the SUSE Linux OS on which TD 15 VM resides, and I could connect well. 4. Now the last part was to install an RPM file of Teradata Connector for Hadoop (TDCH) in the Hadoop Hortonworks Sandbox I just launched in step 2. For this, I used Putty to connect to HDP2.1 shell. I put the IP designated to sandbox.hortonworks.com in PUTTY, and connected on default port 22. I logged in as root, hadoop as username, password. Then I went to /usr/lib/ . There were installations of java 1.7 , hive, sqoop, etc. I just needed to check that java version is 1.7 or above. Now using FileZilla I transferred TDCH rpm file to /usr/lib. Then I run the command to install rpm rpm -Uvh teradata-connector-1.3.2-hdp2.1.noarch.rpm It installed the rpm as verbose (-v), showing me all the details. 5. Now I needed to run the oozie configurations as specified on the Teradata Studio download page in the installation instuctions. namenode was set to sandbox.hortonworks.com . webHDFS hostname and webHDFS port need not be set as they default to name node and 50070 respectively which works. 6. Now open the Teradata Studio. Add a new database connection. Specify the Hadoop Database credentials including WebHDFS host name: sandbox.hortonworks.com WebHDFS port:50070 username: hue I tested the connection. Firstly ping failed. But after long pause of waiting, which meant that it was in the middle of processing. The java error exception showed “cannot access oozie service”. So I closed the root connection through PUTTY as I was first trying to give username root. I also later closed hue connections online on sandbox.hortonworks.com so that the connection does not get timed out. Then the ping succeeded after a 20 sec pause. 7. Once both Teradata and Hadoop Distributed File System were connected to Teradata Studio, I could transfer data to and from both databases. It is done. https://goo.gl/zroF44 #DataScience #Cloud

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robertbryantblog · 6 years ago

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How Mysql Connector Query Works

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johnattaway · 6 years ago

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How To Access Virtual Server From Another Computer

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quickclickhosting · 6 years ago

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stalen00bsblog · 6 years ago

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So your rPi is sitting there on your local network and you (hopefully!) haven't opened the telnet ports on your router, and you are not at home but you need to talk to your rPi. What's a poor boy to do? Well, if you've added this Slack Bot app to your Slack account, you can talk to your rPi from anywhere! Send bash commands, check on its health, control your devices, all from Slack!

Examples

Simple Ping

Get the Temperature of Your rPi Processor

Get Memory Utilization (Bash Command)

Get Free Disk Space (Bash Command)

Send a Command to a Device, like the LCD1602

Check on a Process (Bash Command)

Query Postgres

What You Should Already Know

You should already know:

how to create a .NET Core application

publish it with the linux-arm architecture

WinSCP (or PCSP) it over to the rPi

Fire up a terminal window in Putty to run it.

What if I Don't Have a rPi?

If you don't have an rPi, you can still run the application from Visual Studio though of course the Linux, rPi, and LCD1602 specific stuff won't work. That leaves you with sending "ping" but you can easily add additional behaviors. In fact, I did a lot of the Slack API testing from a Windows box with Visual Studio.

Creating a Bot App in Slack

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The first step is to create a bot app for your Slack account. IMPORTANT! At the point where you create your app, you will also need to create a bot. For example, my bot is called "rpichat" and is listed on the https://api.slack.com/apps page:

Click on the bot and you'll see this:

Click on "Add Features and functionality" and you'll see this:

Click on Bots:

and then "Add a Bot User". Set the display name and default user name, then click on Add Bot User:

Click on OAuth & Permissions on the left:

If you haven't already installed the app in your workspace, you'll see this button:

Install the app, authorize it, and now you can see the Bot User OAuth Access token.

The Code

To begin with, three packages need to be added to the project:

Main

This is really simple:

private static SlackSocketClient client; static void Main(string[] args) { Console.WriteLine("Initializing..."); InitializeSlack(); Console.WriteLine("Slack Ready."); Console.WriteLine("Press ENTER to exit."); Console.ReadLine(); }

SlackAPI

For this article, I'm using the SlackAPI, an open source C#, .NET Standard library which works great on the rPi. Please note that I have not investigated its robustness with regards to losing the websocket connection and restoring it.

Because we're using the Real Time Messaging (RTM) API and a bot app, we'll need to Bot User OAuth Access Token as found on the https://api.slack.com/apps page (navigate then to your app.) This is an important link to remember as it is the gateway to all your apps. In the OAuth Access section, you should see something like this, of course without the tokens blacked out:

Copy the Bot Token (and the OAuth Token if you want) into the appSettings.json file:

{ "Slack": { "AccessToken": "[you access token]", "BotToken": "your bot token]" } }

The "AccessToken" isn't used in this article but you might want it there for other things that you do.

Initializing the API and Receiving Messages

Using the API is straight forward. One method handles the startup and message routing, the comments and code, which I've modified a bit, come from the SlackAPI wiki page examples:

static void InitializeSlack() { string botToken = Configuration["Slack:BotToken"]; ManualResetEventSlim clientReady = new ManualResetEventSlim(false); client = new SlackSocketClient(botToken); client.Connect((connected) => { // This is called once the client has emitted the RTM start command clientReady.Set(); }, () => { // This is called once the RTM client has connected to the end point }); client.OnMessageReceived += (message) => { // Handle each message as you receive them Console.WriteLine(message.user + "(" + message.username + "): " + message.text); if (message.text.StartsWith("rpi:")) { // Skip any spaces after "rpi:" and get what's left of the first space, ignoring data. string cmd = message.text.RightOf("rpi:").Trim().LeftOf(" "); // Get everything to the right after the command and any number of spaces // separating the start of the data. string data = message.text.RightOf("rpi:").Trim().RightOf(" ").Trim(); Console.WriteLine("cmd: " + cmd); Console.WriteLine("data: " + data); string ret = "Error occurred."; if (router.TryGetValue(cmd, out Func<string, string> fnc)) { ret = fnc(data); } else { // Try as bash command. string cmdline = message.text.RightOf("rpi:").Trim(); ret = "```" + cmdline.Bash() + "```"; } client.PostMessage((mr) => { }, message.channel, ret); } }; clientReady.Wait(); }

There are three things to note about the message handler in the above code:

Any message that does not being with "rpi:" will be ignored. This is because when the application posts a message, the message event is fired so the application gets back the very message that was just posted. To distinguish between commands that you, the user, are sending, your commands must be prefixed with "rpi:".

For console output of bash commands is returned in markdown block quotes which uses a monospace font and preserves leading spaces, so you get back a nicely formatted result.

We always reply on the channel from which the message was received. You might be chatting with the bot in its app direct message channel, or if the bot has been invited to a "human" channel, we can chat with it there as well.

Setting Up the Configuration Parser

The configuration parser requires using Microsoft.Extensions.Configuration; and is implemented as a static getter (borrowed from here) and the two NuGet packages mentioned earlier:

public static IConfiguration Configuration { get; } = new ConfigurationBuilder() .SetBasePath(Directory.GetCurrentDirectory()) .AddJsonFile("appSettings.json", optional: false, reloadOnChange: true) .Build();

The file on the *nix side is case-sensitive, so make sure to preserve the case of the filename and how you reference it in the above code.

The Command Router

The router is simply a command key - function dictionary -- if a command has an associated function, that function is called, otherwise it's assumed to be a bash command and the process is invoked.

private static Dictionary<string, Func<string, string>> router = new Dictionary<string, Func<string, string>> { {"temp", GetTemp }, {"display", Display }, {"ping", (_) => "pong" }, };

Extend this for custom C# implementations.

The Bash Process Invoker

This code was borrowed from here:

public static string Bash(this string cmd) { var escapedArgs = cmd.Replace("\"", "\\\""); var process = new Process() { StartInfo = new ProcessStartInfo { FileName = "/bin/bash", Arguments = $"-c \"{escapedArgs}\"", RedirectStandardOutput = true, UseShellExecute = false, CreateNoWindow = true, } }; process.Start(); string result = process.StandardOutput.ReadToEnd(); process.WaitForExit(); return result; }

It implements an extension method, hence its usage looks like "df -h".Bash(). The -c in the arguments is actually telling bash the command (as an argument to bash) to run.

The Commands I've Implemented

Besides the "ping" command, I implemented a couple other things based on what I've got hooked up at the moment.

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Getting the rPi Temperature

The processor has a built-in temperature sensor and the temperature is exposed a file (I found this originally in some Python code and added the Fahrenheit math):

private static string GetTemp(string _) { string ret; string temp = System.IO.File.ReadAllText("/sys/class/thermal/thermal_zone0/temp"); Console.WriteLine(temp); double t = Convert.ToDouble(temp); string dc = String.Format("{0:N2}", t / 1000); string df = String.Format("{0:N2}", t / 1000 * 9 / 5 + 32); ret = dc + "C" + " " + df + "F"; return ret; }

Writing to the LCD1602

From my previous article, I can now display messages to the LCD1602 from Slack!

// Data must be in the format of one of these two options: // "This is line 1" // "This is line 1"[d]"This is line 2" // where [d] is an optional delimiter of any string. private static string Display(string data) { int numQuotes = data.Count(c => c == '\"'); if (data.First() != '\"' || data.Last() != '\"' && (numQuotes != 2 && numQuotes != 4)) { return "bad format"; } Lcd1602 lcd = new Lcd1602(); lcd.OpenDevice("/dev/i2c-1", LCD1602_ADDRESS); lcd.Init(); lcd.Clear(); if (numQuotes == 2) { lcd.Write(0, 0, data.Between("\"", "\"")); } else { // two lines lcd.Write(0, 0, data.Between("\"", "\"")); lcd.Write(0, 1, data.RightOf("\"").RightOf("\"").Between("\"", "\"")); } lcd.CloseDevice(); return "ok"; }

Querying Postgres

Actually, any Postgres SQL command can be executed through your Slack bot, here I show queries.

Executing the SQL, including queries, is straight forward using ADO.NET. For queries, an option for formatting (defaults to JSON) can be provided. My Match extension method would probably be replaced with the C# 8's switch statement and its lovely terseness.

The Northwind database was imported into Postgres using this GitHub repo.

Regarding fetch first 2 rows only, this is part of SQL 2008 but doesn't work in MS SQL Server!

Also, to get this to work, add the appropriate connection string to your appsettings.json file:

"ConnectionStrings": { "rpidb": "Host=[your IP];Database=Northwind;Username=pi;Password=[your password]" }

The ExecuteSQL method:

enum OutputFormat { JSON, CSV, Tabular, } private static string ExecuteSql(string data, List<string> options) { string sql = data; var outputFormat = OutputFormat.JSON; string ret = ""; string validOptionsErrorMessage = "Valid options are --json, --csv, --tabular"; try { options.Match( (o => o.Count == 0, _ => { }), (o => o.Count > 1, _ => throw new Exception(validOptionsErrorMessage)), (o => o[0] == "--json", _ => outputFormat = OutputFormat.JSON), (o => o[0] == "--csv", _ => outputFormat = OutputFormat.CSV), (o => o[0] == "--tabular", _ => outputFormat = OutputFormat.Tabular), (_ => true, _ => throw new Exception(validOptionsErrorMessage)) ); string connStr = Configuration.GetValue<string>("ConnectionStrings:rpidb"); var conn = new NpgsqlConnection(connStr); conn.Open(); var cmd = new NpgsqlCommand(sql, conn); NpgsqlDataAdapter da = new NpgsqlDataAdapter(cmd); DataTable dt = new DataTable(); da.Fill(dt); ret = outputFormat.MatchReturn( (f => f == OutputFormat.JSON, _ => Jsonify(dt)), (f => f == OutputFormat.CSV, _ => Csvify(dt)), (f => f == OutputFormat.Tabular, _ => Tabify(dt)) ); ret = "```\r\n" + ret + "```"; } catch (Exception ex) { ret = ex.Message; } return ret; }

Returning JSON

Dead simple using Newtsoft.JSON:

static string Jsonify(DataTable dt) { string ret = JsonConvert.SerializeObject(dt, Formatting.Indented); return ret.ToString(); }

Example:

Returning a CSV

It is also quite simple:

static string Csvify(DataTable dt) { StringBuilder sb = new StringBuilder(); sb.AppendLine(String.Join(", ", dt.Columns.Cast<DataColumn>().Select(dc => dc.ColumnName))); foreach (DataRow row in dt.Rows) { sb.AppendLine(String.Join(", ", dt.Columns.Cast<DataColumn>().Select(dc => row[dc].ToString()))); } return sb.ToString(); }

Example:

Returning Tabular Formatted Data

Here, the width of each column name and row's data is accounted for requires some of Math.Max for the column names and each row data. Tabify is probably not the greatest name!

static string Tabify(DataTable dt) { StringBuilder sb = new StringBuilder(); int[] colWidth = new int[dt.Columns.Count]; // Get max widths for each column. dt.Columns.Cast<DataColumn>().ForEachWithIndex((dc, idx) => colWidth[idx] = Math.Max(colWidth[idx], dc.ColumnName.Length)); // Get the max width of each row's column. dt.AsEnumerable().ForEach(r => { dt.Columns.Cast<DataColumn>().ForEachWithIndex((dc, idx) => colWidth[idx] = Math.Max(colWidth[idx], r[dc].ToString().Length)); }); // Bump all widths by 3 for better visual separation colWidth.ForEachWithIndex((n, idx) => colWidth[idx] = n + 3); // Padded column names: sb.AppendLine(string.Concat(dt.Columns.Cast<DataColumn>().Select((dc, idx) => dc.ColumnName.PadRight(colWidth[idx])))); // Padded row data: dt.AsEnumerable().ForEach(r => sb.AppendLine(string.Concat(dt.Columns.Cast<DataColumn>().Select((dc, idx) => r[dc].ToString().PadRight(colWidth[idx]))))); return sb.ToString(); }

Example:

Limitations

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There's a 4,000 character limit on what can be posted to a Slack channel, so don't go nuts querying hundreds of records and dozens of columns!

Leaving the Program Running

If you want to make the application a service so it always starts up, in the event of a power loss, or Alternatively, if you simply want to have the program keep running even after you close the terminal window:

nohup > /dev/null &

The & makes the process run in the background (this is true for any process that you start), and nohup is "no hang up" when the terminal closes. The redirect > /dev/null redirects console output to nothing rather than the nohup.out file. Read about nohup here.

In this case, I changed main, removing the "press ENTER to exit" and replaced it with a do nothing loop:

while (!stop) Thread.Sleep(1); Thread.Sleep(1000); // wait for final message to be sent. // Console.WriteLine("Press ENTER to exit."); //Console.ReadLine();

and I added a "stop" command to terminate the program from Slack:

{"stop", _ => {stop=true; return "Stopping..."; } }

Conclusion

While the code is very simple, this opens up a whole new world of bidirectional communication between Slack and the rPi (or any SBC for that matter). I can imagine using this to do things like starting and stopping services, getting the status of various devices attached to the rPi, issuing commands, and so forth. I can imagine mounting an rPi or an Arduino on a LEGO robot and using Slack messaging to drive the robot and even post image files! If I had the time and the hardware, I'd definitely play around with that some more.

#article #bot #general #programming

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craigbrownphd-blog-blog · 7 years ago

Text

How to integrate Hadoop and Teradata using SQL-H

#ICYMI: I have tried Hadoop Connector for Teradata, Teradata Connector for Hadoop, Teradata Studio Express, Aster SQL-H, and many more cumbersome alternatives, finally to reach the Hadoop-Teradata integration without purchasing QueryGrid current version. However, without QueryGrid, you cannot do cross-platform querying. Here, we just demonstrate bidirectional data transfer between Teradata and Hadoop. All that I needed for Teradata seamlessly integrate with Hadoop were these: * Hadoop Sandbox 2.1 for VMware (http://hortonworks.com/hdp/downloads) * Teradata Express 15 for VMware (http://downloads.teradata.com/downloads) * Teradata Connector for Hadoop (TDCH) (http://downloads.teradata.com/downloads) * Teradata Studio (http://downloads.teradata.com/downloads) I didnt need to connect Teradata Aster, because all I needed was querying and data transfer between Hadoop and TD. Here is how it happened: 1. I converted the OVA file I got from Hortonworks Sandbox download page, into a VMX file for running into VMware Server. The command for converting is this ovftool.exe Hortonworks_Sandbox_2.1.ova D:/HDP_2.1_Extracted/HDP_2.1_vmware where HDP_2.1_vmware is the VMDK file extracted. The extraction took an hour on a fast server. 2. I loaded the HDP_2.1_vmware.vmdk into VMware Server by choosing to add a new virtual machine. VMDK file made the VMX as I specified the VM configurations. I chose NAT for network connection, also chose USB driver option for VM. When turning on the VM, it asked the question that SCSI device (USB) is not working so should the VM boot from IDE. Thats the recommended option so I chose it. VM worked, run and I could browse into Hortonworks Sandbox by typing http://sandbox.hortonworks.com:8000. I could also use the port 50070 to access WebHDFS. I just changed the password for hue in the user admin section of the site at http://sandbox.hortonworks.com:8000. 3. Now I needed to install Teradata 15 and Teradata Studio and connect the two. It worked well, and there is a lot of documentation to troubleshoot if anything comes in connecting TD15 to Teradata Studio. When I could not connect TD15 the first time, I got error in Teradata Administrator “Connection Refused”. I just restarted the SUSE Linux OS on which TD 15 VM resides, and I could connect well. 4. Now the last part was to install an RPM file of Teradata Connector for Hadoop (TDCH) in the Hadoop Hortonworks Sandbox I just launched in step 2. For this, I used Putty to connect to HDP2.1 shell. I put the IP designated to sandbox.hortonworks.com in PUTTY, and connected on default port 22. I logged in as root, hadoop as username, password. Then I went to /usr/lib/ . There were installations of java 1.7 , hive, sqoop, etc. I just needed to check that java version is 1.7 or above. Now using FileZilla I transferred TDCH rpm file to /usr/lib. Then I run the command to install rpm rpm -Uvh teradata-connector-1.3.2-hdp2.1.noarch.rpm It installed the rpm as verbose (-v), showing me all the details. 5. Now I needed to run the oozie configurations as specified on the Teradata Studio download page in the installation instuctions. namenode was set to sandbox.hortonworks.com . webHDFS hostname and webHDFS port need not be set as they default to name node and 50070 respectively which works. 6. Now open the Teradata Studio. Add a new database connection. Specify the Hadoop Database credentials including WebHDFS host name: sandbox.hortonworks.com WebHDFS port:50070 username: hue I tested the connection. Firstly ping failed. But after long pause of waiting, which meant that it was in the middle of processing. The java error exception showed “cannot access oozie service”. So I closed the root connection through PUTTY as I was first trying to give username root. I also later closed hue connections online on sandbox.hortonworks.com so that the connection does not get timed out. Then the ping succeeded after a 20 sec pause. 7. Once both Teradata and Hadoop Distributed File System were connected to Teradata Studio, I could transfer data to and from both databases. It is done. https://goo.gl/mujNZJ #DataScience #Cloud

0 notes

craigbrownphd-blog-blog · 7 years ago

Text

How to integrate Hadoop and Teradata using SQL-H

I have tried Hadoop Connector for Teradata, Teradata Connector for Hadoop, Teradata Studio Express, Aster SQL-H, and many more cumbersome alternatives, finally to reach the Hadoop-Teradata integration without purchasing QueryGrid current version. However, without QueryGrid, you cannot do cross-platform querying. Here, we just demonstrate bidirectional data transfer between Teradata and Hadoop. All that I needed for Teradata seamlessly integrate with Hadoop were these: * Hadoop Sandbox 2.1 for VMware (http://hortonworks.com/hdp/downloads) * Teradata Express 15 for VMware (http://downloads.teradata.com/downloads) * Teradata Connector for Hadoop (TDCH) (http://downloads.teradata.com/downloads) * Teradata Studio (http://downloads.teradata.com/downloads) I didnt need to connect Teradata Aster, because all I needed was querying and data transfer between Hadoop and TD. Here is how it happened: 1. I converted the OVA file I got from Hortonworks Sandbox download page, into a VMX file for running into VMware Server. The command for converting is this ovftool.exe Hortonworks_Sandbox_2.1.ova D:/HDP_2.1_Extracted/HDP_2.1_vmware where HDP_2.1_vmware is the VMDK file extracted. The extraction took an hour on a fast server. 2. I loaded the HDP_2.1_vmware.vmdk into VMware Server by choosing to add a new virtual machine. VMDK file made the VMX as I specified the VM configurations. I chose NAT for network connection, also chose USB driver option for VM. When turning on the VM, it asked the question that SCSI device (USB) is not working so should the VM boot from IDE. Thats the recommended option so I chose it. VM worked, run and I could browse into Hortonworks Sandbox by typing http://sandbox.hortonworks.com:8000. I could also use the port 50070 to access WebHDFS. I just changed the password for hue in the user admin section of the site at http://sandbox.hortonworks.com:8000. 3. Now I needed to install Teradata 15 and Teradata Studio and connect the two. It worked well, and there is a lot of documentation to troubleshoot if anything comes in connecting TD15 to Teradata Studio. When I could not connect TD15 the first time, I got error in Teradata Administrator “Connection Refused”. I just restarted the SUSE Linux OS on which TD 15 VM resides, and I could connect well. 4. Now the last part was to install an RPM file of Teradata Connector for Hadoop (TDCH) in the Hadoop Hortonworks Sandbox I just launched in step 2. For this, I used Putty to connect to HDP2.1 shell. I put the IP designated to sandbox.hortonworks.com in PUTTY, and connected on default port 22. I logged in as root, hadoop as username, password. Then I went to /usr/lib/ . There were installations of java 1.7 , hive, sqoop, etc. I just needed to check that java version is 1.7 or above. Now using FileZilla I transferred TDCH rpm file to /usr/lib. Then I run the command to install rpm rpm -Uvh teradata-connector-1.3.2-hdp2.1.noarch.rpm It installed the rpm as verbose (-v), showing me all the details. 5. Now I needed to run the oozie configurations as specified on the Teradata Studio download page in the installation instuctions. namenode was set to sandbox.hortonworks.com . webHDFS hostname and webHDFS port need not be set as they default to name node and 50070 respectively which works. 6. Now open the Teradata Studio. Add a new database connection. Specify the Hadoop Database credentials including WebHDFS host name: sandbox.hortonworks.com WebHDFS port:50070 username: hue I tested the connection. Firstly ping failed. But after long pause of waiting, which meant that it was in the middle of processing. The java error exception showed “cannot access oozie service”. So I closed the root connection through PUTTY as I was first trying to give username root. I also later closed hue connections online on sandbox.hortonworks.com so that the connection does not get timed out. Then the ping succeeded after a 20 sec pause. 7. Once both Teradata and Hadoop Distributed File System were connected to Teradata Studio, I could transfer data to and from both databases. It is done. https://goo.gl/hDqfyv #DataScience #Cloud

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robertbryantblog · 6 years ago

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robertbryantblog · 6 years ago

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robertbryantblog · 6 years ago

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robertbryantblog · 6 years ago

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johnattaway · 6 years ago

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