我正在通过 Apache calcite 将数据从 SQL 插入到文件
Class.forName("org.apache.calcite.jdbc.Driver");
Connection connection = DriverManager.getConnection("jdbc:calcite:");
CalciteConnection calciteConnection = connection.unwrap(CalciteConnection.class);
SchemaPlus rootSchema = calciteConnection.getRootSchema();
JdbcSchema jdbcSchema = JdbcSchema.create(rootSchema, "TRUNKDB", dataSource, null, "dbo");
rootSchema.add("XXXX", jdbcSchema);
/*CSV Schema*/
File csvDir = new File("/home/nanobi/Drill/CSV/");
// SchemaPlus schema = rootSchema.add("s", new CsvSchema(csvDir,null));
rootSchema.add("CSV", new CsvSchema(csvDir, Flavor.SCANNABLE));
Statement statement = connection.createStatement();
int resultSet = statement.executeUpdate("INSERT into \"CSV\".\"p\"(\"Name\") select \"name\" from \"TRUNKDB\".\"nbmdc_nanomarts\"");
我收到以下错误
Exception in thread "main" java.sql.SQLException: Error while executing SQL "INSERT into "CSV"."p"("Name") select 'p' from "TRUNKDB"."nbmdc_nanomarts"": Node [rel#29:Subset#3.ENUMERABLE.[]] could not be implemented; planner state:
Root: rel#29:Subset#3.ENUMERABLE.[]
Original rel:
Sets:
Set#0, type: RecordType(VARCHAR(45) row_id, VARCHAR(45) si_id, VARCHAR(500) name, VARCHAR(500) description, VARCHAR(255) icon_path,
VARCHAR(255) icon_content, VARCHAR(255) active_flag, TIMESTAMP(3)
created_datetime, VARCHAR(45) created_by_user_id, TIMESTAMP(3)
updated_datetime, VARCHAR(45) updated_by_user_id, VARCHAR(500)
nanomart_xml_filepath, VARCHAR(255) db_username, VARCHAR(255)
db_user_password, VARCHAR(255) dbase_name, VARCHAR(255) db_url,
VARCHAR(255) db_schema_name, CHAR(1) is_mandatory, CHAR(1)
is_load_lock, VARCHAR(45) mart_type, VARCHAR(255) db_driver,
VARCHAR(255) load_frequency, CHAR(1) is_date_table, CHAR(1) is_alias,
VARCHAR(500) nbmdc_n, VARCHAR(45) master_flag, VARCHAR(50)
nbmdm_repository_row_id, CHAR(1) is_hierarchical, VARCHAR(4000)
inplacedetail)
rel#8:Subset#0.JDBC.TRUNKDB.[], best=rel#0, importance=0.6561
rel#0:JdbcTableScan.JDBC.TRUNKDB.[](table=[TRUNKDB, nbmdc_nanomarts]), rowcount=100.0, cumulative cost={100.0 rows, 101.0
cpu, 0.0 io}