[ https://issues.apache.org/jira/browse/SQOOP-3014?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Pavel Benes updated SQOOP-3014: ------------------------------- Description: When using sqoop with HCatalog to import data from JDBC (I have tried Oracle11) all numbers that does not fit into double are loosing its precision or are distorted. Steps to reproduce: 1) Create test table in Oracle and fill it with test data {code} CREATE TABLE TEST_SQOOP_ERROR(ID VARCHAR(10), TYPE_NUMBER DECIMAL(22,5)) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-1', 454018528782.42006329) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-2', 87658675864540185.123456789123456789) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-3', 87658675864540185.12345) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-4', 87658675864540185.123) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-5', 7658675864540185.12345) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-6', 7658675864540185.123456789) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-7', 658675864540185.12345) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-8', 58675864540185.12345) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-9', 8675864540185.12345) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-10', 675864540185.12345) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-11', 75864540185.12345) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-12', 35864540185.12345) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-13', 5864540185.12345) {code} 2) Create table in Hive database {code} CREATE TABLE pbe_test_sqoop_error(id string, type_number decimal(22,5)) STORED AS ORC; {code} 3) Import data from Oracle to Hive using sqoop {code} export HADOOP_CLASSPATH=/opt/mantis/jdbc/oracle-11.2/ojdbc6.jar sqoop import -connect jdbc:oracle:thin:@//1.1.1.1:1521/XE --username XXX --password XXX --hcatalog-database default --hcatalog-table pbe_test_sqoop_error --driver oracle.jdbc.OracleDriver --query 'SELECT id, type_number FROM MMDINGEST.TEST_SQOOP_ERROR WHERE $CONDITIONS' -m 1 {code} 4) Display data from Hive table {code} hive> select * from pbe_test_sqoop_error; OK row-1 454018528782.42004 row-2 87658675864540192 row-3 87658675864540192 row-4 87658675864540192 row-5 7658675864540185 row-6 7658675864540185 row-7 658675864540185.125 row-8 58675864540185.125 row-9 8675864540185.12305 row-10 675864540185.12341 row-11 75864540185.12344 row-12 35864540185.12345 row-13 5864540185.12345 Time taken: 0.455 seconds, Fetched: 13 row(s) {code} Only the values at line 1, 12, 13 are correct. At the lines 2-4 even the part of the number before dot is wrong. All looks correctly in Oracle as can be seen on the attached screenshot. The problem seems to be in the java class https://www.codatlas.com/github.com/apache/sqoop/branch-1.4.6/src/java/org/apache/sqoop/mapreduce/hcat/SqoopHCatImportHelper.java starting at line 437. {code} } else if (hfsType == HCatFieldSchema.Type.DECIMAL) { BigDecimal bd = new BigDecimal(n.doubleValue(), MathContext.DECIMAL128); return HiveDecimal.create(bd); } {code} all numbers, even those that are stored in BigDecimal are squeezed through double which leads to the precision lost The same issue could be at some places when working with large numbers. The following code fixes this issue: {code} } else if (hfsType == HCatFieldSchema.Type.DECIMAL) { BigDecimal bd = val instanceof BigDecimal ? (BigDecimal) val : new BigDecimal(n.doubleValue(), MathContext.DECIMAL128); return HiveDecimal.create(bd); } {code} . was: When using sqoop with HCatalog to import data from JDBC (I have tried Oracle11) all numbers that does not fit into double are loosing its precision or are distorted. Steps to reproduce: 1) Create test table in Oracle and fill it with test data {code} CREATE TABLE TEST_SQOOP_ERROR(ID VARCHAR(10), TYPE_NUMBER DECIMAL(22,5)) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-1', 454018528782.42006329) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-2', 87658675864540185.123456789123456789) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-3', 87658675864540185.12345) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-4', 87658675864540185.123) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-5', 7658675864540185.12345) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-6', 7658675864540185.123456789) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-7', 658675864540185.12345) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-8', 58675864540185.12345) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-9', 8675864540185.12345) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-10', 675864540185.12345) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-11', 75864540185.12345) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-12', 35864540185.12345) INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-13', 5864540185.12345) {code} 2) Create table in Hive database {code} CREATE TABLE pbe_test_sqoop_error(id string, type_number decimal(22,5)) STORED AS ORC; {code} 3) Import data from Oracle to Hive using sqoop {code} export HADOOP_CLASSPATH=/opt/mantis/jdbc/oracle-11.2/ojdbc6.jar sqoop import -connect jdbc:oracle:thin:@//54.40.19.81:1521/XE --username XXX --password XXX --hcatalog-database default --hcatalog-table pbe_test_sqoop_error --driver oracle.jdbc.OracleDriver --query 'SELECT id, type_number FROM MMDINGEST.TEST_SQOOP_ERROR WHERE $CONDITIONS' -m 1 {code} 4) Display data from Hive table {code} hive> select * from pbe_test_sqoop_error; OK row-1 454018528782.42004 row-2 87658675864540192 row-3 87658675864540192 row-4 87658675864540192 row-5 7658675864540185 row-6 7658675864540185 row-7 658675864540185.125 row-8 58675864540185.125 row-9 8675864540185.12305 row-10 675864540185.12341 row-11 75864540185.12344 row-12 35864540185.12345 row-13 5864540185.12345 Time taken: 0.455 seconds, Fetched: 13 row(s) {code} Only the values at line 1, 12, 13 are correct. At the lines 2-4 even the part of the number before dot is wrong. All looks correctly in Oracle as can be seen on the attached screenshot. The problem seems to be in the java class https://www.codatlas.com/github.com/apache/sqoop/branch-1.4.6/src/java/org/apache/sqoop/mapreduce/hcat/SqoopHCatImportHelper.java starting at line 437. {code} } else if (hfsType == HCatFieldSchema.Type.DECIMAL) { BigDecimal bd = new BigDecimal(n.doubleValue(), MathContext.DECIMAL128); return HiveDecimal.create(bd); } {code} all numbers, even those that are stored in BigDecimal are squeezed through double which leads to the precision lost The same issue could be at some places when working with large numbers. The following code fixes this issue: {code} } else if (hfsType == HCatFieldSchema.Type.DECIMAL) { BigDecimal bd = val instanceof BigDecimal ? (BigDecimal) val : new BigDecimal(n.doubleValue(), MathContext.DECIMAL128); return HiveDecimal.create(bd); } {code} . > Sqoop with HCatalog import loose precision for large numbers that does not > fit into double > ------------------------------------------------------------------------------------------ > > Key: SQOOP-3014 > URL: https://issues.apache.org/jira/browse/SQOOP-3014 > Project: Sqoop > Issue Type: Bug > Components: hive-integration > Affects Versions: 1.4.6 > Reporter: Pavel Benes > Priority: Critical > > When using sqoop with HCatalog to import data from JDBC (I have tried > Oracle11) all numbers that does not fit into double are loosing its precision > or are distorted. > Steps to reproduce: > 1) Create test table in Oracle and fill it with test data > {code} > CREATE TABLE TEST_SQOOP_ERROR(ID VARCHAR(10), TYPE_NUMBER DECIMAL(22,5)) > > INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-1', > 454018528782.42006329) > INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-2', > 87658675864540185.123456789123456789) > INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-3', > 87658675864540185.12345) > INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-4', > 87658675864540185.123) > INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-5', > 7658675864540185.12345) > INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-6', > 7658675864540185.123456789) > INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-7', > 658675864540185.12345) > INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-8', > 58675864540185.12345) > INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-9', > 8675864540185.12345) > INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-10', > 675864540185.12345) > INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-11', > 75864540185.12345) > INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-12', > 35864540185.12345) > INSERT INTO MMDINGEST.TEST_SQOOP_ERROR(ID, TYPE_NUMBER) VALUES ( 'row-13', > 5864540185.12345) > {code} > 2) Create table in Hive database > {code} > CREATE TABLE pbe_test_sqoop_error(id string, type_number decimal(22,5)) > STORED AS ORC; > {code} > 3) Import data from Oracle to Hive using sqoop > {code} > export HADOOP_CLASSPATH=/opt/mantis/jdbc/oracle-11.2/ojdbc6.jar > sqoop import -connect jdbc:oracle:thin:@//1.1.1.1:1521/XE --username XXX > --password XXX --hcatalog-database default --hcatalog-table > pbe_test_sqoop_error --driver oracle.jdbc.OracleDriver --query 'SELECT id, > type_number FROM MMDINGEST.TEST_SQOOP_ERROR WHERE $CONDITIONS' -m 1 > {code} > 4) Display data from Hive table > {code} > hive> select * from pbe_test_sqoop_error; > OK > row-1 454018528782.42004 > row-2 87658675864540192 > row-3 87658675864540192 > row-4 87658675864540192 > row-5 7658675864540185 > row-6 7658675864540185 > row-7 658675864540185.125 > row-8 58675864540185.125 > row-9 8675864540185.12305 > row-10 675864540185.12341 > row-11 75864540185.12344 > row-12 35864540185.12345 > row-13 5864540185.12345 > Time taken: 0.455 seconds, Fetched: 13 row(s) > {code} > Only the values at line 1, 12, 13 are correct. At the lines 2-4 even the part > of the number before dot is wrong. All looks correctly in Oracle as can be > seen on the attached screenshot. > The problem seems to be in the java class > https://www.codatlas.com/github.com/apache/sqoop/branch-1.4.6/src/java/org/apache/sqoop/mapreduce/hcat/SqoopHCatImportHelper.java > starting at line 437. > {code} > } else if (hfsType == HCatFieldSchema.Type.DECIMAL) { > BigDecimal bd = new BigDecimal(n.doubleValue(), > MathContext.DECIMAL128); > return HiveDecimal.create(bd); > } > {code} > all numbers, even those that are stored in BigDecimal are squeezed through > double which leads to the precision lost The same issue could be at some > places when working with large numbers. > The following code fixes this issue: > {code} > } else if (hfsType == HCatFieldSchema.Type.DECIMAL) { > BigDecimal bd = val instanceof BigDecimal ? (BigDecimal) val : new > BigDecimal(n.doubleValue(), MathContext.DECIMAL128); > return HiveDecimal.create(bd); > } > {code} . -- This message was sent by Atlassian JIRA (v6.3.4#6332)