I'm using NIFI in a clustered mode with two nodes, and I have noticed that only one node that do all the work.
Any idea why is that ? and how can I make nifi2 do some of the processing of the dataflow ?
It depends how data is coming in to your cluster. It is up to you as the data flow designer to create an approach that allows the data to be partitioned across your cluster for processing.
See this post for an overview of strategies to do this:
https://community.hortonworks.com/articles/16120/how-do-i-distribute-data-across-a-nifi-cluster.html
Related
I am working on designing a scalable service(springboot) using which data will be indexed to elastic search.
Use case:
My application uses 6 databases(mySql) having same schema. Each database caters to specific region. I have a micro service that connects to all these dbs and indexes data from specific tables to elasicsearch server(v6.8.8) in similar fashion having 6 elasticsearch indexes one for each db.
Quartz jobs are employed for this purpose and RestHighLevelClient. Also there are delta jobs running each second to look for changes using audit and indexes.
Current problem:
Current design is not scalable - one service doing all the work(data loading, mapping, upsert in bulk). Because indexing is done through quarts jobs, scaling services(running multiple instances) will run the same job multiple times.
No failover - Looking for a distributed elasticsearch nodes and indexing data to both nodes. How to do this efficiently.
I am considering spring data elasticsearch to index data sametime when it is going to be persisted to db.
Does it offer all features ? I use :
Elasticsearch right from installing template to creating/deleting indexes, aliases.
Blue/green deployment - index to non-active nodes and change the aliases.
bulk upsert, querying, aggregations..etc
Any other solutions are welcome. Thanks for your time.
Your one of the use case is to move data from DB (Mysql) to ES in a scalable manner. It is basically a CDC (Change data capture) pipeline.
You can use kafka-connect framework for the same.
The flow should be like:
Read Mysql Transaction logs => Publish the data to Kafka (This can be accomplished using Debezium Source Connector)
Consume data from Kafka => Push it to Elastic Search (This can be accomplished using ES-SYNC Connector)
Why to use the framework ?
Using connect framework data can be read directly from Mysql Transaction logs without writing code.
Connect framework is a distributed & Scalable system
It will reduce the load on your database as you now don't need to query your database for detecting any changes
Easy to set-up
We are building a data workflow with NiFi and want the final (custom) processor (which runs the deduplication logic) to run only one one of the NiFi cluster nodes (instead of running on all of them). I see that NiFi 1.7.0 (which is not yet released) has a PrimaryNodeOnly annotation to enforce a single node execution behaviour. Is there a way or workaround to enforce such behaviour in NiFi 1.6.0?
NOTE: In addition to #PrimaryNodeOnly, it would be better if NiFi provides a way to run a processor on a single node only (i.e., some annotation like #SingleNodeOnly). This way the execution node need not necessarily be the primary node which therefore will reduce the load on primary node. This is just an ask for future and not necessary to solve the problem mentioned above.
There is no specific workaround to enforce it in previous versions, it is on the data flow designer to mark the intended processor(s) to run on the Primary Node only. You could write a script to query the NiFi API for processors of certain types or names, then check/set the strategy as Primary Node Only.
In NiFi 1.6.0 it's possible and looks like this:
I have seen that the Big Data community is very hot in using Flafka in many ways for data ingestion but I haven't really gotten why yet.
A simple example I have developed to better understand this is to ingest Twitter data and move them to multiple sinks(HDFS, Storm, HBase).
I have done the implementation for the ingestion part in the following two ways:
(1) Plain Kafka Java Producer with multiple consumers (2) Flume agent #1 (Twitter source+Kafka sink) | (potential) Flume agent #2(Kafka source+multiple sinks). I haven't really seen any difference in the complexity of developing any of these solutions(not a production system I can't comment on performance) - only what I found online is that a good use case for Flafka would be for data from multiple sources that need aggregating in one place before getting consumed in different places.
Can someone explain why would I use Flume+Kafka over plain Kafka or plain Flume?
People usually combine Flume and Kafka, because Flume has a great (and battle-tested) set of connectors (HDFS, Twitter, HBase, etc.) and Kafka brings resilience. Also, Kafka helps distributing Flume events between nodes.
EDIT:
Kafka replicates the log for each topic's partitions across a
configurable number of servers (you can set this replication factor on
a topic-by-topic basis). This allows automatic failover to these
replicas when a server in the cluster fails so messages remain
available in the presence of failures. -- https://kafka.apache.org/documentation#replication
Thus, as soon as Flume gets the message to Kafka, you have a guarantee that your data won't be lost. NB: you can integrate Kafka with Flume at every stage of your ingestion (ie. Kafka can be used as a source, channel and sink, too).
I have 3 different pool of clients in 3 different geographical locations.
I need configure Rethinkdb with 3 different clusters and replicate data between the (insert, update and deletes). I do not want to use shard, only replication.
I didn't found in documentation if this is possible.
I didn't found in documentation how to configure multi-cluster replication.
Any help is appreciated.
I think that multi cluster is just same a single clusters with nodes in different data center
First, you need to setup a cluster, follow this document: http://www.rethinkdb.com/docs/start-a-server/#a-rethinkdb-cluster-using-multiple-machines
Basically using below command to join a node into cluster:
rethinkdb --join IP_OF_FIRST_MACHINE:29015 --bind all
Once you have your cluster setup, the rest is easy. Go to your admin ui, select the table, in "Sharding and replication", click Reconfigure and enter how many replication you want, just keep shard at 1.
You can also read more about Sharding and Replication at http://rethinkdb.com/docs/sharding-and-replication/#sharding-and-replication-via-the-web-console
I am new to Apache-Hadoop. I have Apache-Hadoop cluster of 3 nodes. I am trying to load a file having 4.5 billion records,but its not getting distributed to all nodes. The behavior is kind of region hotspotting.
I have removed "hbase.hregion.max.filesize" parameter from hbase-site.xml config file.
I observed that if I use 4 node's cluster then it distributes data to 3 nodes and if I use 3 node's cluster then it distributes to 2 nodes.
I think, I am missing some configuration.
Generaly with HBase the main issue is to prepare rowkeys that are not monotonically.
If they are, only oneregion server is used at the time:
http://ikaisays.com/2011/01/25/app-engine-datastore-tip-monotonically-increasing-values-are-bad/
This is HBase Reference Guide about RowKey Design:
http://hbase.apache.org/book.html#rowkey.design
And one more really good article:
http://hortonworks.com/blog/apache-hbase-region-splitting-and-merging/
In our case predefinition of Region servers also improved the loading time:
create 'Some_table', { NAME => 'fam'}, {SPLITS=> ['a','d','f','j','m','o','r','t','z']}
Regards
Pawel