> Performance Issue with Materialized views - increased loading time due to full refresh
> --------------------------------------------------------------------------------------
>
> Key: CARBONDATA-4187
> URL:
https://issues.apache.org/jira/browse/CARBONDATA-4187> Project: CarbonData
> Issue Type: Bug
> Components: core
> Affects Versions: 2.1.0
> Reporter: Sushant Sammanwar
> Priority: Major
> Labels: materializedviews, performance
>
> Hi Team ,
> We have been doing a POC by using Carbon 2.1.0 and created a wrapper code around carbon and deployed it as docker container.
> Concurrent data loading is happening in many tables.
> Our objective if get optimal performance for aggregated queries and using materialized views .
> Our observation is after creating MVs data loading is slow and not able to keep-up the pace of incoming data .
> Process is also consuming a lot of memory when MVs are created .
> Data is received in continuous manner and MVs are refreshed which is resulting in increased load time.
> Ideally MVs should only perform incremental refresh as it doesnot require to calculate old data again.
> But it seems the full refresh is causing high memory usages and increased loading time.
> Testing involved loading data without MVs for 6 hrs , then creating MVs and load data again for 4 hours.
> Loading time with MVs increased there creating backlog of data ( loaded only 1/5 th no. of rows than expected).
> Below are major bottlenecks observed :
> 1. High Memory consumption after creating MVs
> 2. MVs doing a full refresh
> Please find attached details of testing with list of tables.
> Below is definition of table :
> create table if not exists fact_365_1_eutrancell_1 (ts timestamp, metric STRING, tags_id STRING, value DOUBLE, epoch bigint) partitioned by (ts2 timestamp) STORED AS carbondata TBLPROPERTIES ('SORT_COLUMNS'='metric')
> Below is definition of MV :
> create materialized view if not exists fact_365_1_eutrancell_1_hour as select tags_id ,metric,timeseries(ts,'hour') as ts,sum(value),avg(value),min(value),max(value) from fact_365_1_eutrancell_1 group by metric, tags_id, timeseries(ts,'hour')
> Can you suggest why MV creation is slowing down the ingestion so much and what can be done to improve ?
> Is there any way to have incremental refresh of MV - refresh only that hour for which we are loading the data ?
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