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https://issues.apache.org/jira/browse/CARBONDATA-4132?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17380687#comment-17380687
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Sushant Sammanwar commented on CARBONDATA-4132:
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Thanks [~indhumuthumurugesh] for your response.
With Explain plan we have checked query hits MV if there isn't any condition
applied to timeseries column.
If no. of rows in MV can be more than expected ( 1/4th for a hour MV when data
is inserted for 15 min granularity), then it doesnot save purpose to use MV.
MV is using same storage as raw table and will affect query time.
Although MV has partially aggregated data but sometimes MV is found to have
same no. of rows as parent table.
In such cases, query time for MV will be same as that for a table .
> Numer of records not matching in MVs
> ------------------------------------
>
> Key: CARBONDATA-4132
> URL: https://issues.apache.org/jira/browse/CARBONDATA-4132
> Project: CarbonData
> Issue Type: Improvement
> Components: core
> Affects Versions: 2.0.1
> Environment: Apache carbondata 2.0.1
> Reporter: suyash yadav
> Priority: Major
> Fix For: 2.0.1
>
>
> 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:
> ===========
> 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
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