Parallelizing queries
Deephaven supports using multiple processors to speed up query evaluation. The extent to which Deephaven employs multiple processors depends on both the phase of operation and the query itself.
Query initialization and updates
When considering Deephaven query performance, there are two distinct phases to consider: initialization and updates.
Query initialization
Every table operation method — .where
,
.update
,
.naturalJoin
, etc. — undergoes an initialization phase
when the method is called. Initialization produces a result table based on the data in the source table. For example,
with a 100,000-row table called myTable
, running myTable.update("X = random()")
will run the random()
method
100,000 times (once per row).
If an operation's source table is refreshing, then initialization will create a new node in the update graph as well.
Query updates
After initialization, the Periodic Update Graph (UG) monitors source tables for changes and process updates to any
table. For example, if 25,000 rows are added to myTable
, the UG will run the random()
method 25,000
more times, calculating the value of column X
for each of the new rows.
Parallelizing queries
Parallelizing query initialization
Deephaven is a column-oriented query engine — it focuses on handling data one column at a time, instead of one row at a time like many traditional databases. Since Deephaven column sources support random access to data, different segments of a column can be processed in parallel. When possible, the Deephaven engine will do this automatically, based on the number of threads in the Operation Initialization Thread Pool.
Parallelizing query updates
As with query initialization, some operations can process different sections of a column in parallel. However, update processing can also be parallelized across independent nodes of the DAG. Parallel processing of updates depends on the size of the Periodic Update Graph Thread Pool.
Consider the following hypothetical example:
// Retrieve a live table:
my_table = get_my_kafka_feed_table()
// Run several independent query operations on 'my_table':
my_table_updated = my_table.update("MyCalculation = computeValue(Col1, Col2, ColRed, ColBlue)")
my_table_filtered1 = my_table.where("ColX < 10000")
my_table_filtered2 = my_table.where("ColY > ColZ")
// Create a result table that depends on the three prior tables:
merged_tables = merge(
my_table_updated,
my_table_filtered1,
my_table_filtered2
)
The three intermediate tables my_table_updated
, my_table_filtered1
and my_table_filtered2
all depend on only one
other table — the original my_table
. Since they are independent of each other, when my_table
is updated with new or
modified rows it is possible for the query engine to process the new rows into my_table_updated
, my_table_filtered1
and my_table_filtered2
at the same time. However, since merged_tables
depends on those three tables, the query
engine cannot update the result of the merge
operation until after
the update()
and where()
s for those three tables have been processed.
Controlling Concurrency for select
, update
and where
The select
, update
, and where
operations can parallelize within a single where clause or column expression. This can greatly improve throughput by using multiple threads to read existing columns or compute functions. Deephaven can only parallelize an expression if it is stateless, meaning it does not depend on any mutable external inputs or the order in which rows are evaluated. Many operations, such as String manipulation or arithmetic on one or more input columns are stateless. By default, the Deephaven engine assumes that expressions are not stateless. For select
and update
, you can change the configuration property QueryTable.statelessSelectByDefault
to true
to make columns stateless by default. For filters, change the property QueryTable.statelessFiltersByDefault
.
Note
In a future version of Deephaven, filters and selectables will be stateless by default.
The ConcurrencyControl
interface allows you to control the behavior of Filter
(where clause) and Selectable
(column formula) objects.
ConccurencyControl cannot be applied to Selectables passed to view
or updateView
. The view
and updateView
operations compute results on demand, and therefore cannot enforce ordering constraints.
To explicitly mark a Selectable or Filter as stateful, use the withSerial
method.
- A serial Filter cannot be reordered with respect to other Filters. Every input row to a stateful Filter is evaluated in order.
- When a Selectable is serial, then every row for that column is evaluated in order.
- For Selectables, additional ordering constraints are controlled by the value of the
QueryTable.SERIAL_SELECT_IMPLICIT_BARRIERS
. This is set by the propertyQueryTable.serialSelectImplicitBarriers
. The default value is the inverse ofQueryTable.statelessSelectByDefault
. WhenSelectables
are stateless by default, no implicit barriers are added (i.e.,QueryTable.SERIAL_SELECT_IMPLICIT_BARRIERS
is false). WhenSelectables
are stateful by default, then implicit barriers are added (i.e.QueryTable.SERIAL_SELECT_IMPLICIT_BARRIERS
is true). - If
QueryTable.SERIAL_SELECT_IMPLICIT_BARRIERS
is false, no additional ordering between expressions is imposed. As with everyselect
orupdate
call, if column B references column A, then the necessary inputs to column B from column A are evaluated before column B is evaluated. To impose further ordering constraints, use barriers. - If
QueryTable.SERIAL_SELECT_IMPLICIT_BARRIERS
is true, then a serial selectable is an absolute barrier with respect to all other serial selectables. This prohibits serial selectables from being evaluated concurrently, permitting them to access global state. Selectables that are not serial may be reordered with respect to a serial selectable.
Filters and Selectables may declare a barrier. A barrier is an opaque object (compared using reference equality) that is used to mark a particular Filter or Selectable. Subsequent Filters or Selectables may respect a previously declared barrier. If a Filter respects a barrier, that Filter cannot begin evaluation until the Filter which declares the barrier has been completely evaluated. Similarly, if a Selectable respects a barrier, then it cannot begin evaluation until the Selectable which declared the barrier has been completely evaluated.
In this code block, two columns reference the AtomicInteger a
:
import java.util.concurrent.atomic.AtomicInteger
a = new AtomicInteger(0)
t = emptyTable(1_000_000).update("A=a.getAndIncrement()", "B=a.getAndIncrement()")
Deephaven's default behavior is to treat both A
and B
statefully, therefore the table is equivalent to:
t = emptyTable(1_000_000).update("A=i", "B=1_000_000 + i")
However, when the columns are stateless, then the rows from either column can be evaluated in any order. To indicate that A
must be evaluated before B
, we can use a barrier:
import java.util.concurrent.atomic.AtomicInteger
a = new AtomicInteger(0)
t = emptyTable(1_000_000).update(List.of(
Selectable.of(ColumnName.of("A"), RawString.of("a.getAndIncrement()")).withDeclaredBarriers(a),
Selectable.of(ColumnName.of("B"), RawString.of("a.getAndIncrement()")).withRespectedBarriers(a)))
Similarly, we can prevent values of A from appearing out of order using withSerial
:
import java.util.concurrent.atomic.AtomicInteger
a = new AtomicInteger(0)
t=emptyTable(1_000_000).update(List.of(
Selectable.of(ColumnName.of("A"), RawString.of("a.getAndIncrement()")).withSerial(),
Selectable.of(ColumnName.of("B"), RawString.of("a.getAndIncrement()"))))
Managing thread pool sizes
The maximum parallelism of query initialization and update processing is determined by the Operation Initialization Thread Pool and the Periodic Update Graph Thread Pool. The size of these values is controlled using the properties described in the table below:
Thread Pool Property | Default Value | Description |
---|---|---|
OperationInitializationThreadPool.threads | -1 | Determines the number of threads available for parallel processing of initialization operations. |
PeriodicUpdateGraph.updateThreads | -1 | Determines the number of threads available for parallel processing of the Periodic Update Graph refresh cycle. |
Setting either of these properties to -1
instructs Deephaven to use all available processors. The number of available
processors is retrieved from the Java Virtual Machine at Deephaven startup,
using Runtime.availableProcessors().