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Create Hive table based on Parquet file schema

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.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty height:90px;width:728px;box-sizing:border-box;

0

So I have a directory of about 600 parquet files, and using parquet-tools I've extracted the files' schema:

message spark_schema 
 optional int64 af;
 optional binary dst_addr (STRING);
 optional binary dst_name (STRING);
 optional binary from (STRING);
 optional int64 fw;
 optional int64 group_id;
 optional binary li (STRING);
 optional int64 lts;
 optional binary mode (STRING);
 optional int64 msm_id;
 optional binary msm_name (STRING);
 optional int64 poll;
 optional int64 prb_id;
 optional double precision;
 optional binary proto (STRING);
 optional binary refid (STRING);
 optional double refts;
 optional group result (LIST) 
 repeated group bag 
 optional group array 
 optional binary error (STRING);
 optional double finalts;
 optional binary li (STRING);
 optional double offset;
 optional double origints;
 optional int64 poll;
 optional double precision;
 optional double receivets;
 optional binary refid (STRING);
 optional double refts;
 optional double rootdelay;
 optional double rootdispersion;
 optional double rtt;
 optional binary stratum (STRING);
 optional double transmitts;
 optional binary x (STRING);
 
 
 
 optional double rootdelay;
 optional double rootdispersion;
 optional binary src_addr (STRING);
 optional binary stratum (STRING);
 optional int64 timestamp;
 optional double ttr;
 optional binary type (STRING);
 optional int64 version;

My question is how do I use this to create a Hive table? Then populate it with the data from the files? Ideally I would need to have all the data from the 600 of files be queryable with Hive.

hadoop hive parquet

share|improve this question

asked Mar 23 at 13:06

crystyxncrystyxn

306727

add a comment | 

0

So I have a directory of about 600 parquet files, and using parquet-tools I've extracted the files' schema:

message spark_schema 
 optional int64 af;
 optional binary dst_addr (STRING);
 optional binary dst_name (STRING);
 optional binary from (STRING);
 optional int64 fw;
 optional int64 group_id;
 optional binary li (STRING);
 optional int64 lts;
 optional binary mode (STRING);
 optional int64 msm_id;
 optional binary msm_name (STRING);
 optional int64 poll;
 optional int64 prb_id;
 optional double precision;
 optional binary proto (STRING);
 optional binary refid (STRING);
 optional double refts;
 optional group result (LIST) 
 repeated group bag 
 optional group array 
 optional binary error (STRING);
 optional double finalts;
 optional binary li (STRING);
 optional double offset;
 optional double origints;
 optional int64 poll;
 optional double precision;
 optional double receivets;
 optional binary refid (STRING);
 optional double refts;
 optional double rootdelay;
 optional double rootdispersion;
 optional double rtt;
 optional binary stratum (STRING);
 optional double transmitts;
 optional binary x (STRING);
 
 
 
 optional double rootdelay;
 optional double rootdispersion;
 optional binary src_addr (STRING);
 optional binary stratum (STRING);
 optional int64 timestamp;
 optional double ttr;
 optional binary type (STRING);
 optional int64 version;

My question is how do I use this to create a Hive table? Then populate it with the data from the files? Ideally I would need to have all the data from the 600 of files be queryable with Hive.

hadoop hive parquet

share|improve this question

asked Mar 23 at 13:06

crystyxncrystyxn

306727

add a comment | 

So I have a directory of about 600 parquet files, and using parquet-tools I've extracted the files' schema:

message spark_schema 
 optional int64 af;
 optional binary dst_addr (STRING);
 optional binary dst_name (STRING);
 optional binary from (STRING);
 optional int64 fw;
 optional int64 group_id;
 optional binary li (STRING);
 optional int64 lts;
 optional binary mode (STRING);
 optional int64 msm_id;
 optional binary msm_name (STRING);
 optional int64 poll;
 optional int64 prb_id;
 optional double precision;
 optional binary proto (STRING);
 optional binary refid (STRING);
 optional double refts;
 optional group result (LIST) 
 repeated group bag 
 optional group array 
 optional binary error (STRING);
 optional double finalts;
 optional binary li (STRING);
 optional double offset;
 optional double origints;
 optional int64 poll;
 optional double precision;
 optional double receivets;
 optional binary refid (STRING);
 optional double refts;
 optional double rootdelay;
 optional double rootdispersion;
 optional double rtt;
 optional binary stratum (STRING);
 optional double transmitts;
 optional binary x (STRING);
 
 
 
 optional double rootdelay;
 optional double rootdispersion;
 optional binary src_addr (STRING);
 optional binary stratum (STRING);
 optional int64 timestamp;
 optional double ttr;
 optional binary type (STRING);
 optional int64 version;

My question is how do I use this to create a Hive table? Then populate it with the data from the files? Ideally I would need to have all the data from the 600 of files be queryable with Hive.

hadoop hive parquet

share|improve this question

asked Mar 23 at 13:06

crystyxncrystyxn

306727

message spark_schema 
 optional int64 af;
 optional binary dst_addr (STRING);
 optional binary dst_name (STRING);
 optional binary from (STRING);
 optional int64 fw;
 optional int64 group_id;
 optional binary li (STRING);
 optional int64 lts;
 optional binary mode (STRING);
 optional int64 msm_id;
 optional binary msm_name (STRING);
 optional int64 poll;
 optional int64 prb_id;
 optional double precision;
 optional binary proto (STRING);
 optional binary refid (STRING);
 optional double refts;
 optional group result (LIST) 
 repeated group bag 
 optional group array 
 optional binary error (STRING);
 optional double finalts;
 optional binary li (STRING);
 optional double offset;
 optional double origints;
 optional int64 poll;
 optional double precision;
 optional double receivets;
 optional binary refid (STRING);
 optional double refts;
 optional double rootdelay;
 optional double rootdispersion;
 optional double rtt;
 optional binary stratum (STRING);
 optional double transmitts;
 optional binary x (STRING);
 
 
 
 optional double rootdelay;
 optional double rootdispersion;
 optional binary src_addr (STRING);
 optional binary stratum (STRING);
 optional int64 timestamp;
 optional double ttr;
 optional binary type (STRING);
 optional int64 version;

share|improve this question

asked Mar 23 at 13:06

crystyxncrystyxn

306727

share|improve this question

asked Mar 23 at 13:06

crystyxncrystyxn

306727

asked Mar 23 at 13:06

crystyxncrystyxn

306727

asked Mar 23 at 13:06

crystyxncrystyxn

306727