Skip to main content



Error to integrate Impala with Kudu


Create Table Failed
W0106 18:18:54.640544 368440 negotiation.cc:307] Unauthorized connection attempt: Server connection negotiation failed: server connection from 172.136.38.157:35678: unauthenticated connections from publicly routable IPs are prohibited. See --trusted_subnets flag for more information.: 172.136.38.157:35678
After setting up kudu, we can enable it to work with Impala.

We, can check the cluster status -
kudu cluster ksck <master>



The cluster doesn't have any matching tables
==================
Errors:
==================
error fetching info from tablet servers: Not found: No tablet servers found

FAILED

--
Also, tablet Server UI is not opening.

Solution- 

This error might be because Kudu Service has to know about trusted networks, which we can set -

Kudu Service Advanced Configuration Snippet (Safety Valve) for gflagfile Kudu (Service-Wide)

--trusted_subnets=127.0.0.0/8,10.0.0.0/8,172.16.0.0/12,192.168.0.0/16,169.254.0.0/16,172.136.38.0/24,X.X.X.0/24

Bounce the Kudu service -

refer - https://community.cloudera.com/t5/Support-Questions/Tablet-servers-failed-to-heartbeat-to-master-of-Kudu-1-4-0/td-p/57587

Verification- 
Execute following commands from Impala -


CREATE TABLE `my_first_table` ( `id` BIGINT PRIMARY KEY, `name` STRING ) PARTITION BY HASH(id) PARTITIONS 2  STORED AS KUDU TBLPROPERTIES ('kudu.num_tablet_replicas' = '1'); 

insert into my_first_table  select 2 id , 'Ganesh' name from invoice_headers limit 1;

select * from my_first_table;

Comments

Popular posts

Spring MongoDB Rename field with derived Value of another field

Input Collection -  [ { 'k' : 'Troubleshooting' , 'hour' : '2024-10-10T16' , 'v' : [ 'WebPage, Login' ] }, { 'k' : 'TroubleshootingMe' , 'hour' : '2024-10-07T01' , 'v' : [ 'Accounts, etc' ] }  ] Expected Output -  [ { 'hour' : '2024-10-10T16' , 'Troubleshooting' : [ 'WebPage, Login' ] }, { 'hour' : '2024-10-07T01' , 'TroubleshootingMe' : [ 'Accounts, etc' ] }  ]   Above Can be achieved by  $replaceRoot / $replaceWith as follows - { $replaceWith : { $mergeObjects : [ { hour : "$hour" }, { "$arrayToObject" : [ [ { k : "$k" , v : "$v" } ] ] } ] } } or { $replaceRoo...




Spark MongoDB Connector Not leading to correct count or data while reading

  We are using Scala 2.11 , Spark 2.4 and Spark MongoDB Connector 2.4.4 Use Case 1 - We wanted to read a Shareded Mongo Collection and copy its data to another Mongo Collection. We noticed that after Spark Job successful completion. Output MongoDB did not had many records. Use Case 2 -  We read a MongoDB collection and doing count on dataframe lead to different count on each execution. Analysis,  We realized that MongoDB Spark Connector is missing data on bulk read as a dataframe. We tried various partitioner, listed on page -  https://www.mongodb.com/docs/spark-connector/v2.4/configuration/  But, none of them worked for us. Finally, we tried  MongoShardedPartitioner  this lead to constant count on each execution. But, it was greater than the actual count of records on the collection. This seems to be limitation with MongoDB Spark Connector. But,  MongoShardedPartitioner  seemed closest possible solution to this kind of situation. But, it per...




Experience with MongoDB and Optimizations

  Experience with MongoDB and Optimizations Before reading below. I would like to point out that this  experience  is related to version  6.0.14-ent, having 6 shards, each shard having 3 machines, each machine is VM with 140 GB RAM and 2TB SSD. And, we had been hosting almost 36 TB of data. MongoDB is not good with Big Data Joins and/ or Big Data OLAP processing. It is mainly meant for OLTP purposes.  Instead of joining millions of keys between 2 collections. It is better to lookup data of one key from one collection then lookup it in other collection. Thus, merging data from 2 collection for same key. Its better to keep De-normalized data in one document.  Updating a document later is cumbersome.  MongoDB crash if data is overloaded. And, it has long downtime if crashed unlike other databases which fails write to database if disk space achieves certain limit. Thus, keeping database active and running for read traffic. MongoDB needs indexes for fast qu...




Spring MongoDB Log Connection Pool Details - Active, Used, Waiting

  We couldn't find any direct way to log Mongo Connection pool Size. So, we did implement an indirect way as below.  This may be incorrect at times when dealing with Sharded MongoDB having Primaty & Secondary nodes. Because, connection may be used based on read prefrence - Primary, primaryPreferred, Secondary, etc. But, this gives an understanding if connections are used efficiently and there is no wait to acquire connections from pool. This can be further enhanced to log correct connection pool statistics.  1) Implement  MyConnectionPoolListener  as below -  import java.util.concurrent.atomic.AtomicInteger; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import com.mongodb.event.ConnectionCheckOutFailedEvent; import com.mongodb.event.ConnectionCheckOutStartedEvent; import com.mongodb.event.ConnectionCheckedInEvent; import com.mongodb.event.ConnectionCheckedOutEvent; import com.mongodb.event.ConnectionClosedEvent; import com.mongodb.event.Conne...




Spark Streaming with Kafka Leading to increase in Open File Descriptors ( Kafka )

  Open File Descriptors w.r.t Kafka brokers relates with following -  number of file descriptors to just track log segment files. Additional file descriptors to communicate via network sockets with external parties (such as clients, other brokers, Zookeeper, and Kerberos). For # 1 this is formula -  (number of partitions)*(partition size / segment size) Reference -  https://docs.cloudera.com/cdp-private-cloud-base/7.1.6/kafka-performance-tuning/topics/kafka-tune-broker-syslevel-file-descriptors.html For #2, every connection made my consumer or producer or zookeeper or  Kerberos  opens file descriptors. Note that each TCP connection creates 2 file descriptors. These connections can be for internal communication of heartbeat, or  security handshake , or data transfer to or from client (producer or consumer) When we run a Spark application integrating it with  Kafka . And, if it is not stable, meaning -  Streaming window for micro batches is les...