createTable
Syntax
createTable(dbHandle, table, tableName, [compressMethods])
Arguments
dbHandle is a distributed database handle.
table is a table object. An empty dimension table is created based on the schema of table.
tableName is a string indicating the name of the dimension table to be created.
compressMethods a dictionary indicating which compression methods are used for specified columns. The keys are columns name and the values are compression methods (“lz4” or “delta”). If unspecified, use LZ4 compression method. Please note that the delta compression method can only be used for SHORT, INT, LONG or temporal data types.
Details
This function creates an empty dimension table. A dimension table is a non-partitioned table in a DFS database, used to store small datasets with infrequent updates.
After a dimension table is loaded into memory with function loadTable, the memory taken up by the table will not be released by the system automatically. It is not recommended to store more than 2 million records in a dimension table. To clear the cached data, you can manually call command clearCachedDatabase().
Just like partitioned tables, a dimension table can have multiple replicas (determined by the configuration parameter dfsReplicationFactor).
To enable concurrent writes, updates or deletes on a dimension table, set the configuration parameter enableConcurrentDimensionalTableWrite to true.
Examples
Example1
$ db=database("dfs://db1",VALUE,1 2 3)
$ timestamp = [09:34:07,09:36:42,09:36:51,09:36:59,09:32:47,09:35:26,09:34:16,09:34:26,09:38:12]
$ sym = `C`MS`MS`MS`IBM`IBM`C`C`C
$ price= 49.6 29.46 29.52 30.02 174.97 175.23 50.76 50.32 51.29
$ qty = 2200 1900 2100 3200 6800 5400 1300 2500 8800
$ t = table(timestamp, sym, qty, price);
$ dt=db.createTable(t,`dt).append!(t);
$ select * from dt;
timestamp |
sym |
qty |
price |
|---|---|---|---|
09:34:07 |
C |
2200 |
49.6 |
09:36:42 |
MS |
1900 |
29.46 |
09:36:51 |
MS |
2100 |
29.52 |
09:36:59 |
MS |
3200 |
30.02 |
09:32:47 |
IBM |
6800 |
174.97 |
09:35:26 |
IBM |
5400 |
175.23 |
09:34:16 |
C |
1300 |
50.76 |
09:34:26 |
C |
2500 |
50.32 |
09:38:12 |
C |
8800 |
51.29 |
Example 2
$ db = database("dfs://demodb", VALUE, 1..10)
$ t=table(take(1, 86400) as id, 2020.01.01T00:00:00 + 0..86399 as timestamp, rand(1..100, 86400) as val)
$ dt = db.createTable(t, "dt", {timestamp:"delta", val:"delta"})
$ dt.append!(t)
Related functions: createPartitionedTable