keyedTable

keyedTable(keyColumns, X, [X1], [X2], .....)

or

keyedTable(keyColumns, capacity:size, colNames, colTypes)

or

keyedTable(keyColumns, table)

keyColumn is a string scalar or vector indicating the name(s) of the primary key column(s). The column type must be INTEGRAL, TEMPORAL or LITERAL.

For the first scenario: X, X1, .... are vectors.

For the second scenario:

capacity is the amount of memory (in terms of the number of rows) allocated to the table. When the number of rows exceeds capacity, the system will first allocate memory of 1.2-2 times of capacity, copy the data to the new memory space, and release the original memory. For large tables, these steps may use significant amount of memory.

size can be 0 or 1, indicating the initial size (in terms of the number of rows) of the table. It must be 0 if the table contains array vector columns.

• If size=0, create an empty table.

• If size=1, create a table with one record, and the initialized values are:

  • false for Boolean type.

  • 0 for numeric, temporal, IPADDR, COMPLEX, and POINT types.

  • NULL for LITERAL and INT128 types.

colNames is a string vector of column names.

colTypes is a string vector of data types.

For the third scenario, table is a table. Please note that keyColumns in table cannot have duplicate values.

Create an keyed table, which is a special type of in-memory table with primary key. The primary key can be one column or multiple columns.

When appending to the keyed table, if a new row has the same primary key value as an existing row, the existing row will be overwritten with the new row.

If the filtering conditions in a SQL statement don't use "or" operator, contain all keyColumns, and each condition uses "=" or "in" operator, and there are at most 2 "in" operators, query performance on a keyed table is optimized and is better than that on an ordinary in-memory table.

Please refer to the optimized SQL query in indexed table .

Example 1. Create a keyed table.

The first scenario:

sym=`A`B`C`D`E
id=5 4 3 2 1
val=52 64 25 48 71
t=keyedTable(`sym`id,sym,id,val)
t;

The second scenario:

t=keyedTable(`sym`id,1:0,`sym`id`val,[SYMBOL,INT,INT])
insert into t values(`A`B`C`D`E,5 4 3 2 1,52 64 25 48 71);

The third scenario:

tmp=table(sym, id, val)
t=keyedTable(`sym`id, tmp);

Create a keyed in-memory partitioned table:

sym=`A`B`C`D`E
id=5 4 3 2 1
val=52 64 25 48 71
t=keyedTable(`sym`id,sym,id,val)
db=database("",VALUE,sym)
pt=db.createPartitionedTable(t,`pt,`sym).append!(t);

Example 2. Update a keyed table.

t=keyedTable(`sym,1:0,`sym`datetime`price`qty,[SYMBOL,DATETIME,DOUBLE,DOUBLE])
insert into t values(`APPL`IBM`GOOG,2018.06.08T12:30:00 2018.06.08T12:30:00 2018.06.08T12:30:00,50.3 45.6 58.0,5200 4800 7800)
t;

Insert a new row with duplicate primary key value as an existing row. The existing row will be overwritten:

insert into t values(`APPL`IBM`GOOG,2018.06.08T12:30:01 2018.06.08T12:30:01 2018.06.08T12:30:01,65.8 45.2 78.6,5800 8700 4600)
t;

Insert new rows among which there are duplicate primary key values:

insert into t values(`MSFT`MSFT,2018.06.08T12:30:01 2018.06.08T12:30:01,45.7 56.9,3600 4500)
t;

The primary key cannot be updated:

update t set sym="C_"+sym;
// Error: Can't update a key column.

Example 3. Query on a keyed table.

In some cases, queries on a keyed table are optimized. In this section we will compare the performance of queries on keyed tables and ordinary in-memory tables.

For the following examples, we first create a keyed table and an ordinary in-memory table with 1 million records each:

id=shuffle(1..1000000)
date=take(2012.06.01..2012.06.10, 1000000)
type=rand(9, 1000000)
val=rand(100.0, 1000000)
t=table(id, date, type, val)
kt=keyedTable(`id`date`type, id, date, type, val);

Example 3.1

timer(100) select * from t where id=500000, date=2012.06.01, type=0;
// Time elapsed: 161.574 ms

timer(100) select * from kt where id=500000, date=2012.06.01, type=0;
// Time elapsed: 1.483 ms

timer(100) sliceByKey(t1, (500000, 2012.06.01, 0))
// Time elapsed: 0.705 ms

Example 3.2

timer(100) select * from t where id in [1, 500000], date in 2012.06.01..2012.06.05, type=5;
// Time elapsed: 894.241 ms

timer(100) select * from kt where id in [1, 500000], date in 2012.06.01..2012.06.05, type=5;
// Time elapsed: 2.322 ms

With more than 2 "in" operators in the filtering conditions, however, a query on a keyed table is not optimized.

Example 3.3

timer(100) select * from t where id in [1, 500000], date in 2012.06.01..2012.06.05, type in 1..5;
// Time elapsed: 801.347 ms

timer(100) select * from kt where id in [1, 500000], date in 2012.06.01..2012.06.05, type in 1..5;
// Time elapsed: 834.184 ms

If the filtering conditions do not include all key columns, a query on a keyed table is not optimized.

Example 3.4

timer(100) select * from t where id=500000, date in 2012.06.01..2012.06.05;
// Time elapsed: 177.113 ms

timer(100) select * from kt where id=500000, date in 2012.06.01..2012.06.05;
// Time elapsed: 163.265 ms

Example 4. Use a keyed table with array vectors to record the 5 levels of quotes data.

sym=["a","b","c "] 
time=22:58:52.827 22:58:53.627 22:58:53.827 
volume=array(INT[]).append!([[100,110,120,115,125],[200,230,220,225,230],[320,300,310,315,310]])
price=array(DOUBLE[]).append!([[10.5,10.6,10.7,10.77,10.85],[8.6,8.7,8.76,8.83,8.9],[6.3,6.37,6.42,6.48,6.52]])
t=keyedTable(`sym,sym,time,volume,price)
t;

// latest quote volume and price
newVolume=array(INT[]).append!([[130,110,110,115,120]])
newPrice= array(DOUBLE[]).append!([[10.55,10.57,10.62,10.68,10.5]])
// update for stock a
update t set volume=newVolume, price=newPrice where sym="a"
t;

Note that when updating the array vector column, the number of elements in each column must be consistent with the original column. For example, if the vector of new record contains 4 elements, while the original contains 5 elements, an error is raised:

newVolume=array(INT[]).append!([[130,110,110,120]])
newPrice= array(DOUBLE[]).append!([[10.55,10.57,10.62,10.5]])

update t set volume=newVolume, price=newPrice where sym="a"
// error: Failed to update column: volume

Related function: indexedTable