lambda-arch | Applying Lambda Architecture with Spark Kafka

 by   apssouza22 Java Version: Current License: Apache-2.0

kandi X-RAY | lambda-arch Summary

kandi X-RAY | lambda-arch Summary

lambda-arch is a Java library typically used in Big Data, Kafka, Spark, Hadoop applications. lambda-arch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Read about the project here.
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            kandi-support Support

              lambda-arch has a low active ecosystem.
              It has 115 star(s) with 64 fork(s). There are 7 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 0 open issues and 2 have been closed. On average issues are closed in 4 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of lambda-arch is current.

            kandi-Quality Quality

              lambda-arch has 0 bugs and 0 code smells.

            kandi-Security Security

              lambda-arch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              lambda-arch code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              lambda-arch is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              lambda-arch releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              lambda-arch saves you 1082 person hours of effort in developing the same functionality from scratch.
              It has 2506 lines of code, 234 functions and 50 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed lambda-arch and discovered the below as its top functions. This is intended to give you an instant insight into lambda-arch implemented functionality, and help decide if they suit your requirements.
            • Main entry point
            • Calculate heat map
            • Gets the point of interest data
            • Get window traffic counts
            • Starts streaming
            • Returns a map of kafka s parameters
            • Starts Spark session
            • Compares two Measurements
            • Compares this object to another
            • Serialize an IoTData object to bytes
            • Returns a hashCode of this route
            • Convert an array of columns to an IoTData object
            • Entry point for the producer
            • Compare two IoTData data
            • Compare two Measurements
            • Update the running sum by the given key
            • Filter the streams of the vehicle
            • Rounds the given measurement by rounding the coordinates
            • Evaluate model
            • Convert the dataframe to a model
            • Creates the window traffic data object
            • Map tuples to TrafficData
            • Transform the TrafficData object to TotalTrafficData object
            • Transform to POITraData object
            • Train the model
            • Trigger traffic data message
            Get all kandi verified functions for this library.

            lambda-arch Key Features

            No Key Features are available at this moment for lambda-arch.

            lambda-arch Examples and Code Snippets

            No Code Snippets are available at this moment for lambda-arch.

            Community Discussions

            QUESTION

            Why can't i connect to Kafka with PySpark? Getting a cannot find data source 'kafka' error
            Asked 2019-Nov-26 at 01:22

            I am trying to build a real-time big data pipeline with the Lambda-Architecture. I have so far been able to create the data ingestion module with Kafka as well as the Batch-Layer with S3 and Redshift. However I can't seem to connect to my kafka server through PySpark. I am very new at Spark and I've looked for solutions around the Internet, but none seem to deal with the Python environment.

            Here is my code:

            ...

            ANSWER

            Answered 2019-Sep-25 at 03:20

            Thanks to the observatoins from the user pissall I was able to solve the issue. It was a version issue. I got it to run by running pyspark from the terminal with the following command:

            Source https://stackoverflow.com/questions/58090180

            QUESTION

            Spark structured streaming consistency across sinks
            Asked 2019-Aug-23 at 00:36

            I'd like to understand better the consistency model of Spark 2.2 structured streaming in the following case :

            • one source (Kinesis)
            • 2 queries from this source towards 2 different sinks : one file sink for archive purpose (S3), and another sink for processed data (DB or file, not yet decided)

            I'd like to understand if there's any consistency guarantee across sinks, at least under certain circumstances :

            • Can one of the sink be way ahead of the other ? Or are they consuming data at the same speed on the source (since its the same source) ? Can they be synchronous ?
            • If I (gracefully) stop the stream application, will the data on the 2 sinks consistent ?

            The reason is I'd like to build a Kappa-like processing app, with the ability to suspend/shutdown the streaming part when I want to reprocess some history, and, when I resume the streaming, avoid reprocessing something that has already been processed (as being in the history), or missing some (eg. some data that has not been committed to the archive, and then skipped as already processed when the streaming resume)

            ...

            ANSWER

            Answered 2019-Aug-23 at 00:36

            One important thing to keep in mind is the 2 sinks will be used from 2 distinct queries, each reading independently from the source. So checkpointing is done per-query.

            Whenever you call start on a DataStreamWriter that results in a new query and if you set checkpointLocation each query will have its own checkpointing to track offsets from the sink.

            Source https://stackoverflow.com/questions/47159685

            QUESTION

            Design: running pg_dump when tables are continuously created and dropped
            Asked 2019-Jan-23 at 09:01

            We run PostgreSQL (v9.5) as a Serving DB in a variant of the Kappa architecture:

            • Every instance of a compute job creates and populates its own result table, e.g. "t_jobResult_instanceId".
            • Once a job finishes, its output table is made available for access. Multiple result tables for the same job type may be in use concurrently.
            • When an output table is not needed, it is dropped.

            Compute results are not the only kind of tables in this database instance, and we need to take periodic hot backups. Here lies our problem. When tables come and go, pg_dump dies. Here's a simple test that reproduces our failure mode (it involves 2 sessions, S1 and S2):

            ...

            ANSWER

            Answered 2019-Jan-23 at 09:00

            That should be possible using the -T option of pg_dump:

            -T table
            --exclude-table=table
               Do not dump any tables matching the table pattern.

            The psql documentation has details about these patterns:

            Within a pattern, * matches any sequence of characters (including no characters) and ? matches any single character. (This notation is comparable to Unix shell file name patterns.) For example, \dt int* displays tables whose names begin with int. But within double quotes, * and ? lose these special meanings and are just matched literally.

            A pattern that contains a dot (.) is interpreted as a schema name pattern followed by an object name pattern. For example, \dt foo*.*bar* displays all tables whose table name includes bar that are in schemas whose schema name starts with foo. When no dot appears, then the pattern matches only objects that are visible in the current schema search path. Again, a dot within double quotes loses its special meaning and is matched literally.

            Advanced users can use regular-expression notations such as character classes, for example [0-9] to match any digit. All regular expression special characters work as specified in Section 9.7.3, except for . which is taken as a separator as mentioned above, * which is translated to the regular-expression notation .*, ? which is translated to ., and $ which is matched literally. You can emulate these pattern characters at need by writing ? for ., (R+|) for R*, or (R|) for R?. $ is not needed as a regular-expression character since the pattern must match the whole name, unlike the usual interpretation of regular expressions (in other words, $ is automatically appended to your pattern). Write * at the beginning and/or end if you don't wish the pattern to be anchored. Note that within double quotes, all regular expression special characters lose their special meanings and are matched literally.

            Source https://stackoverflow.com/questions/54319721

            QUESTION

            How to check Vagrant up progress
            Asked 2017-Sep-13 at 08:12

            I am using vagrant for first time.

            I am trying to download a VM by running "vagrant up" command. corresponding vagrant file is https://github.com/aalkilani/spark-kafka-cassandra-applying-lambda-architecture/tree/master/vagrant

            i have a slow internet connection ..., its been around 1 hour i am not sure how much of download has happened .... few questions

            1. How to check the % of download completed ( i know it will tell me when it reaches 20% ... but how to check % of downloaded )
            2. Which temp directory does the vagrant download to ( if i have to stop download in between and resume tomorrow ... not sure if i need to cleanup or it will resume from where it left)

            I am using Vagrant2.0.0 on windows7

            looking forward to learn from your experience.

            ...

            ANSWER

            Answered 2017-Sep-13 at 06:06

            Acutally when you execute the vagrant up in the console, it will show the download processes.

            But for your question, all the downloaded boxes are house in "C:\Users\USERNAME\.vagrant.d\boxes" folder.

            Baically due to the poor connection, vagrant download the boxes very slow, so it is high recommand to download your base box in http://www.vagrantbox.es/ or https://app.vagrantup.com/boxes/search with the download tool, then you can add it by

            vagrant box add vagrant init vagrant up </code></p>

            Source https://stackoverflow.com/questions/46189698

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install lambda-arch

            You can download it from GitHub.
            You can use lambda-arch like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the lambda-arch component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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