partitioning

问题 There is next partitioned table: CREATE TABLE "ERMB_LOG_TEST_BF"."OUT_SMS"( "TRX_ID" NUMBER(19,0) NOT NULL ENABLE, "CREATE_TS" TIMESTAMP (3) DEFAULT systimestamp NOT NULL ENABLE, /* other fields... */ ) PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 STORAGE(BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ERMB_LOG_TEST_BF" PARTITION BY RANGE ("TRX_ID") INTERVAL (281474976710656) (PARTITION "SYS_P1358" VALUES LESS THAN (59109745109237760) SEGMENT CREATION IMMEDIATE

问题 There is next partitioned table: CREATE TABLE "ERMB_LOG_TEST_BF"."OUT_SMS"( "TRX_ID" NUMBER(19,0) NOT NULL ENABLE, "CREATE_TS" TIMESTAMP (3) DEFAULT systimestamp NOT NULL ENABLE, /* other fields... */ ) PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 STORAGE(BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ERMB_LOG_TEST_BF" PARTITION BY RANGE ("TRX_ID") INTERVAL (281474976710656) (PARTITION "SYS_P1358" VALUES LESS THAN (59109745109237760) SEGMENT CREATION IMMEDIATE

问题 (Note: updated with adopted answer below.) For a PostgreSQL 8.1 (or later) partitioned table, how does one define an UPDATE trigger and procedure to "move" a record from one partition to the other, if the UPDATE implies a change to the constrained field that defines the partition segregation? For example, I've a table records partitioned into active and inactive records like so: create table RECORDS (RECORD varchar(64) not null, ACTIVE boolean default true); create table ACTIVE_RECORDS (

问题 This question already has answers here : How to split data into 3 sets (train, validation and test)? (4 answers) Closed 3 years ago . I'm trying to read a fairly large CSV file with Pandas and split it up into two random chunks, one of which being 10% of the data and the other being 90%. Here's my current attempt: rows = data.index row_count = len(rows) random.shuffle(list(rows)) data.reindex(rows) training_data = data[row_count // 10:] testing_data = data[:row_count // 10] For some reason,

问题 This question already has answers here : How to split data into 3 sets (train, validation and test)? (4 answers) Closed 3 years ago . I'm trying to read a fairly large CSV file with Pandas and split it up into two random chunks, one of which being 10% of the data and the other being 90%. Here's my current attempt: rows = data.index row_count = len(rows) random.shuffle(list(rows)) data.reindex(rows) training_data = data[row_count // 10:] testing_data = data[:row_count // 10] For some reason,

问题 I need an algorithm of dividing different manufacturing parts in to uneven groups. The main condition is that difference between maximum number in the group and all others should be as low as possible. For example: if we have list [1,3,4,11,12,19,20,21] and we decide that it should be divided in 3 parts it should be divided into [1,3,4],[11,12],[19,20,21] . In the same case if we decide to divide it in to 4 we would get : [1,3,4],[11],[12],[19,20,21]. In order to clarify "difference between

问题 I need an algorithm of dividing different manufacturing parts in to uneven groups. The main condition is that difference between maximum number in the group and all others should be as low as possible. For example: if we have list [1,3,4,11,12,19,20,21] and we decide that it should be divided in 3 parts it should be divided into [1,3,4],[11,12],[19,20,21] . In the same case if we decide to divide it in to 4 we would get : [1,3,4],[11],[12],[19,20,21]. In order to clarify "difference between

问题 I'm familiar with the concept of using partitions in Oracle as a technique to pubish incremental additions to tables (in a DW context). (like this example) For example. a daily snapshot for a data mart fact table is loaded behind the scenes in a partition within a table. for example with date as the partition key (1 partitioned table, with only one partition). once the load is complete, and the contents are validated, the partition can be 'exchanged' into the true destination table (1

问题 I am running a hive query which selects data from a table and inserts result into another hive partitioned table using spark-sql . While inserting it requires 1536 partitions. But spark is not able to insert data with 1536 partitions eventhough I increased max partitions to 2000. Below is command: spark-sql --master yarn --num-executors 14 --executor-memory 45G --executor-cores 30 --driver-memory 10G --conf spark.dynamicAllocation.enabled=false -e "SET hive.exec.dynamic.partition = true;SET

问题 Please take a look at this code below . I am getting error for the below code when I pass value for the number of partitions. def loadDataFromPostgress(sqlContext: SQLContext, tableName: String, columnName: String, dbURL: String, userName: String, pwd: String, partitions: String): DataFrame = { println("the no of partitions are : "+partitions) var dataDF = sqlContext.read.format("jdbc").options( scala.collection.Map("url" -> dbURL, "dbtable" -> tableName, "driver" -> "org.postgresql.Driver",