I have a running project with using of akka-persistence-jdbc plugin and postgresql as a backend.
Now I want to migrate to akka-persistence-cassandra.
But how can I convert the existing events (more than 4GB size in postgres) to cassandra?
Should I write a manual migration program? Reading from postgres and writing to right format in cassandra?
This is a classic migration problem. There are multiple solutions for this.
Spark SQL and Spark Cassandra Connector: Spark JDBC (called as Spark Dataframe, Spark SQL) API allows you to read from any JDBC source. You can read it in chunks by segmenting it otherwise you will go out of memory. Segmentation also makes the migration parallel. Then write the data into Cassandra by Cassandra Spark Connector. This is by far the simplest and efficient way I used in my tasks.
Java Agents: Java Agent can be written based on plain JDBC or other libraries and then write to Cassandra with Datastax driver. Spark program runs on multi machine - multi threaded way and recovers if something goes wrong automatically. But if you write an agent like this manually, then your agent only runs on single machine and multi threading also need to be coded.
Kafka Connectors: Kafka is a messaging broker. It can be used indirectly to migrate. Kafka has connector which can read and write to different databases. You can use JDBC connector to read from PostGres and Cassandra connector to write to Cassandra. It's not that easy to setup but it has the advantage of "no coding involved".
ETL Systems: Some ETL Systems have support for Cassandra but I haven't personally tried anything.
I saw some advantages in using Spark Cassandra and Spark SQL for migration, some of them are:
Code was concise. It was hardly 40 lines
Multi machine (Again multi threaded on each machine)
Job progress and statistics in Spark Master UI
Fault tolerance- if a spark node is down or thread/worker failed there then job is automatically started on other node - good for very long running jobs
If you don't know Spark then writing agent is okay for 4GB data.
Related
We are developing ETL tool using apache pyspark and apache airflow.
Apache airflow will be used for workflow management.
Can apache pyspark handle huge volume of data?
Can i get extract,transform count from apache airflow?
Yes, Apache (Py)Spark is built for dealing with big data
There is no magic out-of-the-box solution for getting metrics from PySpark into Airflow
Some solutions for #2 are:
Writing metrics from PySpark to another system (e.g. database, blob storage, ...) and reading those in a 2nd task in Airflow
Returning the values from the PySpark jobs and pushing them into Airflow XCom
My 2c: don't process large data in Airflow itself as it's built for orchestration and not data processing. If the intermediate data becomes big, use a dedicated storage system for that (database, blob storage, etc...). XComs are stored in the Airflow metastore itself (although custom XCom backends to store data in other systems are supported since Airflow 2.0 https://www.astronomer.io/guides/custom-xcom-backends), so make sure the data isn't too big if you're storing it in the Airflow metastore.
I have my MongoDB and Spark running on Zeppelin, both sharing the same HDFS. The MongoDB produces a .wt database stored in the same HDFS.
I want to load the database collection produced by that MongoDB from the HDFS into a Spark DataFrame.
Is it possible to load the database directly from HDFS into spark as a DataFrame? Or do I need to use a MongoDB Spark connector?
I would not recommend to read or modify the internal WiredTiger Storage Engine's *.wt files. Firstly, these internal files are subject to change without notifications (not a public facing API), also any unintended modifications to these files may cause the database to be in an invalid/corrupt state.
You can utilise MongoDB Spark Connector to load data from MongoDB to Spark. This connector is designed, developed and optimised for the purpose of read/write data between MongoDB and Apache Spark. For example, by accessing data via the database the client may utilise the database indexes to optimise read operations.
See also:
GitHub demo: Docker for MongoDB, Apache Spark and Apache Zeppelin
GitHub demo: Docker for MongoDB and Apache Spark
I want to do some analytics using Flink on the Data in Postgresql. How and where should I give the port address,username and password. I was trying with the table source as mentioned in the link:https://ci.apache.org/projects/flink/flink-docs-release-1.4/dev/table/common.html#register-tables-in-the-catalog.
final static ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
final static TableSource csvSource = new CsvTableSource("localhost", port);
I am unable to start with actually. I went through all the documents but detailed report about this not found.
The tables and catalog referred to the link you've shared are part of Flink's SQL support, wherein you can use SQL to express computations (queries) to be performed on data ingested into Flink. This is not about connecting Flink to a database, but rather it's about having Flink behave somewhat like a database.
To the best of my knowledge, there is no Postgres source connector for Flink. There is a JDBC table sink, but it only supports append mode (via INSERTs).
The CSVTableSource is for reading data from CSV files, which can then be processed by Flink.
If you want to operate on your data in batches, one approach you could take would be to export the data from Postgres to CSV, and then use a CSVTableSource to load it into Flink. On the other hand, if you wish to establish a streaming connection, you could connect Postgres to Kafka and then use one of Flink's Kafka connectors.
Reading a Postgres instance directly isn't supported as far as I know. However, you can get realtime streaming of Postgres changes by using a Kafka server and a Debezium instance that replicates from Postgres to Kafka.
Debezium connects using the native Postgres replication mechanism on the DB side and emits all record inserts, updates or deletes as a message on the Kafka side. You can then use the Kafka topic(s) as your input in Flink.
I am trying to stream changes in my Postgres database using the Kafka Connect JDBC Connector. I am running into issues upon startup as the database is quite big and the query dies every time as rows change in between.
What is the best practice for starting off the JDBC Connector on really huge tables?
Assuming you can't pause the workload on the database that you're streaming the contents in from to allow the initialisation to complete, I would look at Debezium.
In fact, depending on your use case, I would look at Debezium regardless :) It lets you do true CDC against Postgres (and MySQL and MongoDB), and is a Kafka Connect plugin just like the JDBC Connector is so you retain all the benefits of that.
I am trying to connect Apache Spark to MongoDB using Mesos. Here is my architecture: -
MongoDB: MongoDB Cluster of 2 shards, 1 config server and 1 query server.
Mesos: 1 Mesos Master, 4 Mesos slaves
Now I have installed Spark on just 1 node. There is not much information available on this out there. I just wanted to pose a few questions: -
As per what I understand, I can connect Spark to MongoDB via mesos. In other words, I end up using MongoDB as a storage layer. Do I really Need Hadoop? Is it mandatory to pull all the data into Hadoop just for Spark to read it?
Here is the reason I am asking this. The Spark Install expects the HADOOP_HOME variable to be set. This seems like very tight coupling !! Most of the posts on the net speak about MongoDB-Hadoop connector. It doesn't make sense if you're forcing me to move everything to hadoop.
Does anyone have an answer?
Regards
Mario
Spark itself takes a dependency on Hadoop and data in HDFS can be used as a datasource.
However, if you use the Mongo Spark Connector you can use MongoDB as a datasource for Spark without going via Hadoop at all.
Spark-mongo connector is good idea, moreover if your are executing Spark in a hadoop cluster you need set HADOOP_HOME.
Check your requeriments and test it (tutorial)
Basic working knowledge of MongoDB and Apache Spark. Refer to the MongoDB documentation and Spark documentation.
Running MongoDB instance (version 2.6 or later).
Spark 1.6.x.
Scala 2.10.x if using the mongo-spark-connector_2.10 package
Scala 2.11.x if using the mongo-spark-connector_2.11 package
The new MongoDB Connector for Apache Spark provides higher performance, greater ease of use and, access to more advanced Spark functionality than the MongoDB Connector for Hadoop. The following table compares the capabilities of both connectors.
Then you need to configure Spark with mesos:
Connecting Spark to Mesos
To use Mesos from Spark, you need a Spark binary package available in a place accessible by Mesos, and a Spark driver program configured to connect to Mesos.
Alternatively, you can also install Spark in the same location in all the Mesos slaves, and configure spark.mesos.executor.home (defaults to SPARK_HOME) to point to that location.