Details
Major Description
Hi Team,
We are working on a POC where we need to insert 300k records/second in a table where we have already created Timeeries MVs with Minute,Hour,Day granularity.
As per our the Minute based MV should contain 300K records till the insertion of next minute data. Also the hour and Day based MVs should contain 300K records till the arrival of next hour and next day data respectively.
But The count of records in MV is not coming out as per our expectation.It is always more than our expectation.
But the strange thing is, When we drop the MV and create the MV after inserting the data in the table then the count if reocrds comes correct.So it is clear there is no problem with MV definition and the data.
Kindly help us in resolving this issue on priority.Please find more details below:
Table definition:
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spark.sql("create table Flow_Raw_TS(export_ms bigint,exporter_ip string,pkt_seq_num bigint,flow_seq_num int,src_ip string,dst_ip string,protocol_id smallint,src_tos smallint,dst_tos smallint,raw_src_tos smallint,raw_dst_tos smallint,src_mask smallint,dst_mask smallint,tcp_bits int,src_port int,in_if_id bigint,in_if_entity_id bigint,in_if_enabled boolean,dst_port int,out_if_id bigint,out_if_entity_id bigint,out_if_enabled boolean,direction smallint,in_octets bigint,out_octets bigint,in_packets bigint,out_packets bigint,next_hop_ip string,bgp_src_as_num bigint,bgp_dst_as_num bigint,bgp_next_hop_ip string,end_ms timestamp,start_ms timestamp,app_id string,app_name string,src_ip_group string,dst_ip_group string,policy_qos_classification_hierarchy string,policy_qos_queue_id bigint,worker_id int,day bigint ) stored as carbondata TBLPROPERTIES ('local_dictionary_enable'='false')
MV definition:
==============
Minute based
spark.sql("create materialized view Flow_Raw_TS_agg_001_min as select timeseries(end_ms,'minute') as end_ms,src_ip,dst_ip,app_name,in_if_id,src_tos,src_ip_group,dst_ip_group,protocol_id,bgp_src_as_num, bgp_dst_as_num,policy_qos_classification_hierarchy, policy_qos_queue_id,sum(in_octets) as octects, sum(in_packets) as packets, sum(out_packets) as out_packets, sum(out_octets) as out_octects FROM Flow_Raw_TS group by timeseries(end_ms,'minute'),src_ip,dst_ip,app_name,in_if_id,src_tos,src_ip_group, dst_ip_group,protocol_id,bgp_src_as_num,bgp_dst_as_num,policy_qos_classification_hierarchy, policy_qos_queue_id").show()
Hour Based
val startTime = System.nanoTime
spark.sql("create materialized view Flow_Raw_TS_agg_001_hour as select timeseries(end_ms,'hour') as end_ms,app_name,sum(in_octets) as octects, sum(in_packets) as packets, sum(out_packets) as out_packets, sum(out_octets) as out_octects, in_if_id,src_tos,src_ip_group, dst_ip_group,protocol_id,src_ip, dst_ip,bgp_src_as_num, bgp_dst_as_num,policy_qos_classification_hierarchy, policy_qos_queue_id FROM Flow_Raw_TS group by timeseries(end_ms,'hour'),in_if_id,app_name,src_tos,src_ip_group,dst_ip_group,protocol_id,src_ip, dst_ip,bgp_src_as_num,bgp_dst_as_num,policy_qos_classification_hierarchy, policy_qos_queue_id").show()
val endTime = System.nanoTime
val elapsedSeconds = (endTime - startTime) / 1e9d