At the end of the previous article, we were halfway through running our first distributed query:

SELECT l_partkey, count(*) FROM lineitem GROUP BY l_partkey;

We discussed how a query starts, how tasks are set up, and the interactions between plans, operators, and drivers. We have also presented how the first stage of the query is executed, from table scan to partitioned output - or the producer side of the shuffle.  

In this article, we will discuss the second query stage, or the consumer side of the shuffle.

Shuffle Consumer​

As presented in the previous post, on the first query stage each worker reads the table, then produces a series of information packets (SerializedPages) intended for different workers of stage 2. In our example, the lineitem table has no particular physical partitioning or clustering key. This means that any row of the table can have any value of l_partkey in any of the files forming the table. So in order to group data based on l_partkey, we first need to make sure that rows containing the same values for l_partkey are processed by the same worker – the data shuffle at the end of stage 1.

The overall query structure is as follows:

The query coordinator distributes table scan splits to stage 1 workers in no particular order. The workers process these and, as a side effect, fill destination buffers that will be consumed by stage 2 workers. Assuming there are 100 stage 2 workers, every stage 1 Driver has its own PartitionedOutput which has 100 destinations. When the buffered serializations grow large enough, these are handed off to the worker's OutputBufferManager. 

Now let’s focus on the stage 2 query fragment. Each stage 2 worker has the following plan fragment: PartitionedOutput over Aggregation over LocalExchange over Exchange.  

Each stage 2 Task corresponds to one destination in the OutputBufferManager of each stage 1 worker. The first stage 2 Tasks fetches destination zero from all the stage 1 Tasks. The second stage 2 fetches destination 1 from the first stage Tasks and so on.  Everybody talks to everybody else. The shuffle proceeds without any central coordination.

But let's zoom in to what actually happens at the start of stage 2.

The plan has a LocalExchange node after the Exchange. This becomes two pipelines: Exchange and LocalPartition on one side, and LocalExchange, HashAggregation, and PartitionedOutput on the other side. 

The Velox Task is intended to be multithreaded, with typically 5 to 30 Drivers. There can be hundreds of Tasks per stage, thus amounting to thousands of threads per stage. So, each of the 100 second stage workers is consuming 1/100 of the total output of the first stage. But it is doing this in a multithreaded manner, with many threads consuming from the ExchangeSource. We will explain this later.

In order to execute a multithreaded group by, we can either have a thread-safe hash table or we can partition the data in n disjoint streams, and then proceed aggregating each stream on its own thread. On a CPU, we almost always prefer to have threads working on their own memory, so data will be locally partitioned based on l_partkey using a local exchange. CPUs have complex cache coherence protocols to give observers a consistent ordered view of memory, so reconciliation after many cores have written the same cache line is both mandatory and expensive. Strict memory-to-thread affinity is what makes multicore scalability work. 

LocalPartition and LocalExchange​

To create efficient and independent memory access patterns, the second stage reshuffles the data using a local exchange. In concept, this is like the remote exchange between tasks, but is scoped inside the Task. The producer side (LocalPartition) calculates a hash on the partitioning column l_partkey, and divides this into one destination per Driver in the consumer pipeline. The consumer pipeline has a source operator LocalExchange that reads from the queue filled by LocalPartition. See LocalPartition.h for details. Also see the code in Task.cpp for setting up the queues between local exchange producers and consumers.

While remote shuffles work with serialized data, local exchanges pass in-memory vectors between threads. This is also the first time we encounter the notion of using columnar encodings to accelerate vectorized execution. Velox became known by its extensive use of such techniques, which we call compressed execution. In this instance, we use dictionaries to slice vectors across multiple destinations – we will discuss it next.

Dictionary Magic​

Query execution often requires changes to the cardinality (number of rows in a result) of the data. This is essentially what filters and joins do – they either reduce the cardinality of the data, e.g., filters or selective joins, or increase the cardinality of the data, e.g. joins with multiple key matches.

Repartitioning in LocalPartition assigns a destination to each row of the input based on the partitioning key. It then makes a vector for each destination with just the rows for that destination. Suppose rows 2, 8 and 100 of the current input hash to 1. Then the vector for destination 1 would only have rows 2, 8 and 100 from the original input. We could make a vector of three rows by copying the data. Instead, we save the copy by wrapping each column of the original input in a DictionaryVector with length 3 and indices 2, 8 and 100. This is much more efficient than copying, especially for wide and nested data.

Later on, the LocalExchange consumer thread running destination 1 will see a DictionaryVector of 3 rows. When this is accessed by the HashAggregation Operator, the aggregation sees a dictionary and will then take the indirection and will access for row 0 the value at 2 in the base (inner) vector, for row 1 the value at 8, and so forth. The consumer for destination 0 does the exact same thing but will access, for example, rows 4, 9 and 50.

The base of the dictionaries is the same on all the consumer threads. Each consumer thread just looks at a different subset. The cores read the same cache lines, but because the base is not written to (read-only), there is no cache-coherence overhead. 

To summarize, a DictionaryVector<T> is a wrapper around any vector of T. The DictionaryVector specifies indices, which give indices into the base vector. Dictionary encoding is typically used when there are few distinct values in a column. Take the strings “experiment” and “baseline”. If a column only has these values, it is far more efficient to represent it as a vector with “experiment” at 0 and “baseline” at 1, and a DictionaryVector that has, say, 1000 elements, where these are either the index 0 or 1.  

Besides this, DictionaryVectors can also be used to denote a subset or a reordering of elements of the base. Because all places that accept vectors also accept DictionaryVectors, the DictionaryVector becomes the universal way of representing selection. This is a central precept of Velox and other modern vectorized engines. We will often come across this concept.

Aggregation and Pipeline Barrier​

We have now arrived at the second pipeline of stage 2. It begins with LocalExchange, which then feeds into HashAggregation. The LocalExchange picks a fraction of the Task's input specific to its local destination, about 1/number-of-drivers of the task's input.

We will talk about hash tables, their specific layout and other tricks in a later post. For now, we look at HashAggregation as a black box. This specific aggregation is a final aggregation, which is a full pipeline barrier that only produces output after all input is received.

How does the aggregation know it has received all its input? Let's trace the progress of the completion signal through the shuffles. A leaf worker knows that it is at end if the Task has received the “no more splits” message in the last task update from the coordinator.

So, if one DataSource inside a tableScan is at end and there are no more splits, this particular TableScan is not blocked and it is at end. This will have the Driver call PartitionedOutput::noMoreInput() on the tableScan's PartitionedOutput. This will cause any buffered data for any destination to get flushed and moved over to OutputBufferManager with a note that no more is coming. OutputBufferManager knows how many Drivers there are in the TableScan pipeline. After it has received this many “no more data coming” messages, it can tell all destinations that this Task will generate no more data for them. 

Now, when stage 2 tasks query stage 1 producers, they will know that they are at end when all the producers have signalled that there is no more data coming. The response to the get data for destination request has a flag identifying the last batch. The ExchangeSource on the stage 2 Task set the no-more-data flag. This is then queried by all the Drivers and each of the Exchange Operators sees this. This then calls noMoreInput in the LocalPartition. This queues up a “no more data” signal in the local exchange queues. If the LocalExchange at the start of the second pipeline of stage 2 sees a “no more data” from each of its sources, then it is at end and noMoreInput is called on the HashAggregation. 

This is how the end of data propagates. Up until now, HashAggregation has produced no output, since the counts are not known until all input is received. Now, HashAggregation starts producing batches of output, which contain the l_partkey value and the number of times this has been seen. This reaches the last PartitionedOutput, which in this case has only one destination, the final worker that produces the result set. This will be at end when all the 100 sources have reported their own end of data.

Recap​

We have finally walked through the distributed execution of a simple query. We presented how data is partitioned between workers in the cluster, and then a second time over inside each worker.

Velox and Presto are designed to aggressively parallelize execution, which means creating distinct, non-overlapping sets of data to process on each thread. The more threads, the more throughput. Also remember that for CPU threads to be effective, they must process tasks that are large enough (often over 100 microseconds worth of cpu time), and not communicate too much with other threads or write to memory other threads are writing to. This is accomplished with local exchanges.

Another important thing to remember from this article is how columnar encodings (DictionaryVectors, in particular) can be used as a zero-copy way of representing selection/reorder/duplication. We will see this pattern again with filters, joins, and other relational operations.

Next we will be looking at joins, filters, and hash aggregations. Stay tuned!

In this article, we will discuss how a distributed compute engine executes a query similar to the one presented in our first article:

SELECT l_partkey, count(*) FROM lineitem GROUP BY l_partkey;

We use the TPC-H schema to illustrate the example, and Prestissimo as the compute engine orchestrating distributed query execution. Prestissimo is responsible for the query engine frontend (parsing, resolving metadata, planning, optimizing) and distributed execution (allocating resources and shipping query fragments), and Velox is responsible for the execution of plan fragments within a single worker node. Throughout this article, we will present which functions are performed by Velox and which by the distributed engine - Prestissimo, in this example.

Query Setup​

Prestissimo first receives the query through a coordinator node, which is responsible for parsing and planning the query. For our sample query, a distributed query plan with three query fragments will be created:

1. The first fragment reads the l_partkey column from the lineitem table and divides its output according to a hash of l_partkey.
2. The second fragment reads the output from the first fragment and updates a hash table from l_partkey containing the number of times the particular value of l_partkey has been seen (the count(*) aggregate function implementation).
3. The final fragment then reads the content of the hash tables, once the second fragment has received all the rows from the first fragment.

The shuffle between the two first fragments partitions the data according to l_partkey. Suppose there are 100 instances of the second fragment. If the hash of l_partkey modulo 100 is 0, the row goes to the first task in the second stage; if it is 1, the row goes to the second task, and so forth. In this way, each second stage Task gets a distinct subset of the rows. The shuffle between the second and third stage is a gather, meaning that there is one Task in the third stage that will read the output of all 100 tasks in the second stage.

A Stage is the set of Tasks that share the same plan fragment. A Task is the main integration point between Prestissimo and Velox; it’s the Velox execution instance that physically processes all or part of the data that passes through the stage.

To set up the distributed execution, Prestissimo first selects the workers from the pool of Prestissimo server processes it manages. Assuming that stage 1 is 10 workers wide, it selects 10 server processes and sends the first stage plan to them. It then selects 100 workers for stage 2 and sends the second stage plan to these. The last stage that gathers the result has only one worker, so Prestissimo sends the final plan to only one worker. The set of workers for each stage may overlap, so a single worker process may host multiple stages of one query.

Let's now look more closely at what each worker does at query setup time.

Task Setup​

In Prestissimo, the message that sets up a Task in a worker is called Task Update. A Task Update has the following information: the plan, configuration settings, and an optional list of splits. Splits are further qualified by what plan node they are intended for, and whether more splits for the recipient plan node and split group will be coming.

Since split generation involves enumerating files from storage (so they may take a while), Presto allows splits to be sent to workers asynchronously, such that the generation of splits can run in parallel with the execution of the first splits. Therefore, the first task update specifies the plan and the config. Subsequent ones only add more splits.

Besides the plan, the coordinator provides configs as maps from string key to string value, both top level and connector level. The connector configs have settings for each connector; connectors are used by table scan and table writer to deal with storage and file formats. These configs and other information, like thread pools, top level memory pool etc. are handed to the Task in a QueryCtx object. See velox/core/QueryCtx.h in the Velox repo for details.

From Plans to Drivers and Operators​

Once the Velox Task is created, a TaskManager hands it splits to work on. This is done with Task::addSplit(), and can be done after the Task has started executing. See velox/exec/Task.h for details.

Let's zoom into what happens at Task creation: A PlanNode tree specifying what the Task does is given to the Task as part of a PlanFragment. The most important step done at Task creation is splitting the plan tree into pipelines. Each pipeline then gets a DriverFactory, which is the factory class that makes Drivers for the pipeline. The Drivers, in their turn, contain the Operators that do the work of running the query. The DriverFactories are made in LocalPlanner.cpp. See LocalPlanner::plan for details.

Following the execution model known as Volcano, the plan is represented by an operator tree where each node consumes the output of its child operators, and returns output to the parent operator. The root node is typically a PartitionedOutputNode or a TableWriteNode. The leaf nodes are either TableScanNode, ExchangeNode or ValuesNode (used for query literals). The full set of Velox PlanNode can be found at velox/core/PlanNode.h.

The PlanNodes mostly correspond to Operators. PlanNodes are not executable as such; they are only a structure describing how to make Drivers and Operators, which do the actual execution. If the tree of nodes has a single branch, then the plan is a single pipeline. If it has nodes with more than one child (input), then the second input of the node becomes a separate pipeline.

Task::start() creates the DriverFactories, which then create the Drivers. To start execution, the Drivers are queued on a thread pool executor. The main function that runs Operators is Driver::runInternal(). See this function for the details of how Operators and the Driver interface: Operator::isBlocked() determines if the Driver can advance. If it cannot, it goes off thread until a future is realized, which then puts it back on the executor.

getOutput() retrieves data from an Operator and addInputs() feeds data into another Operator. The order of execution is to advance the last Operator which can produce output and then feed this to the next Operator. if an Operator cannot produce output, then getOutput() is called on the Operator before it until one is found that can produce data. If no operator is blocked and no Operator can produce output, then the plan is at end. The noMoreInput() method is called on each operator. This can unblock production of results, for example, an OrderBy can only produce its output after it knows that it has all the input.

The Minimal Pipeline: Table Scan and Repartitioning​

Table Scan. With the table scan stage of our sample query, we have one pipeline with two operators: TableScan and PartitionedOutput. Assume this pipeline has five Drivers, and that all these five Drivers go on the thread pool executor. The PartitionedOutput cannot do anything because it has no input. The TableScan::getOutput() is then called. See velox/exec/TableScan.cpp for details. The first action TableScan takes is to look for a Split with task::getSplitOrFuture(). If there is no split available, this returns a future. The Driver will then park itself off thread and install a callback on the future that will reschedule the Driver when a split is available.

It could also be the case that there is no split and the Task has been notified that no more splits will be coming. In this case TableScan would be at the end. Finally, if a Split is available, TableScan interprets it. Given a TableHandle specification provided as part of the plan (list of columns and filters), the Connector (as specified in the Split) makes a DataSource. The DataSource handles the details of IO and file and table formats.

The DataSource is then given the split. After this, DataSource::next() can be called repeatedly to get vectors (batches) of output from the file/section of file specified by the Split. If the DataSource is at the end, TableScan looks for the next split. See Connector.h for the Connector and DataSource interfaces.

Repartitioning. Now we have traced execution up to the TableScan returning its first batch of output. The Driver feeds this to PartitionedOutput::addInput(). See PartitionedOutput.cpp for details. PartitionedOutput first calculates a hash on the partitioning key, in this case, l_partkey, producing a destination number for each row in the batch RowVectorPtr input_.

Each destination has a partly filled serialization buffer (VectorStreamGroup) for each destination worker. If there are 100 Tasks in the second stage, each PartitionedOutput has 100 destinations, each with one VectorStreamGroup. The main function of a VectorStreamGroup is append(), which takes a RowVectorPtr and a set of row numbers in it. It serializes each value identified by the row numbers and adds it to the partly formed serialization. When enough rows are accumulated in the VectorStreamGroup, it produces a SerializedPage. See flush() in PartitionedOutput.cpp.

The SerializedPage is a self contained serialized packet of information that can be transmitted over the wire to the next stage. Each such page only contains rows intended for the same recipient. These pages are then queued up in the worker process' OutputBufferManager. Note the code with BlockingReason in flush(). The buffer manager maintains separate queues of all consumers of all Tasks. If a queue is full, adding output may block. This returns a future that is realized when there is space to add more data to the queue. This depends on when the Task's consumer Task fetches the data.

Shuffles in Prestissimo are implemented by PartitionedOutput at the producer and Exchange at the consumer end. The OutputBufferManager keeps ready serialized data for consumers to pick up. The binding of these to the Presto wire protocols is in TaskManager.cpp for the producer side, and in PrestoExchangeSource.cpp for the consumer side.

Recap​

We presented how a plan becomes executable and how data moves between and inside Operators. We discussed that a Driver can block (go off thread) to wait for a Split to become available, or to wait for its output to be consumed. We have now scratched the surface of running a leaf stage of a distributed query. There is much more to Operators and vectors, though. In the next installment of Velox Primer, we will look at what the second stage of our minimal sample query does.

This is the first part of a series of short articles that will take you through Velox’s internal structures and concepts. In this first part, we will discuss how distributed queries are executed, how data is shuffled among different stages, and present Velox concepts such as Tasks, Splits, Pipelines, Drivers, and Operators that enable such functionality.

Distributed Query Execution​

Velox is a library that provides the functions that go into a query fragment in a distributed compute engine. Distributed compute engines, like Presto and Spark run so-called exchange parallel plans. Exchange is also known as a data shuffle, and allows data to flow from one stage to the next. Query fragments are the things connected by shuffles, and comprise the processing that is executed within a single worker node. A shuffle takes input from a set of fragments and routes rows of input to particular consumers based on a characteristic of the data, i.e., a partitioning key. The consumers of the shuffle read from the shuffle and get rows of data from whatever producer, such that the partitioning key of these rows matches the consumer.

Take the following query as an example:

SELECT key, count(*) FROM T GROUP BY key

Suppose it has n leaf fragments that scan different parts of T (the green circles at stage 1). At the end of the leaf fragment, assume there is a shuffle that shuffles the rows based on key. There are m consumer fragments (the yellow circles at stage 2), where each gets a non-overlapping selection of rows based on column key. Each consumer then constructs a hash table keyed on key, where they store a count of how many times the specific value of key has been seen.

Now, if there are 100B different values of key, the hash table would not conveniently fit on a single machine. For efficiency, there is a point in dividing this into 100 hash tables of 1B entries each. This is the point of exchange parallel scale-out. Think of shuffle as a way for consumers to each consume their own distinct slice of a large stream produced by multiple workers.

Distributed query engines like Presto and Spark both fit the above description. The difference is in, among other things, how they do the shuffle, but we will get back to that later.

Tasks and Splits​

Within a worker node, the Velox representation of a query fragment is called a Task (velox::exec::Task). A task is informed by mainly two things: 

• Plan - velox::core::PlanNode: specifies what the Task does.
• Splits - velox::exec::Split: specifies the data the Task operates on.

The Splits correspond to the plan that the Task is executing. For the first stage Tasks (table scanning), the Splits specify pieces of files to scan. For the second stage Tasks (group by), their Splits identify the table scan Tasks from which the group by reads its input. There are file splits (velox::connector::ConnectorSplit) and remote splits (velox::exec::RemoteConnectorSplit). The first identifies data to read, the second identifies a running Task.

The distributed engine makes PlanNodes and Splits. Velox takes these and makes Tasks. Tasks send back statistics, errors and other status information to the distributed engine. 

Pipelines, Drivers and Operators​

Inside a Task, there are Pipelines. Each pipeline is a linear sequence of operators (velox::exec::Operator), and operators are the objects that implement relational logic. In the case of the group by example, the first task has one pipeline, with a TableScan (velox::exec::TableScan) and a PartitionedOutput (velox::exec::PartitionedOutput). The second Task too has one pipeline, with an Exchange, a LocalExchange, HashAggregation, and a PartitionedOutput.

Each pipeline has one or more Drivers. A Driver is a container for one linear sequence of Operators, and typically runs on its own thread. The pipeline is the collection of Drivers with the same sequence of Operators. The individual Operator instances belong to each Driver. The Drivers belong to the Task. These are interlinked with smart pointers such that they are kept alive for as long as needed.

An example of a Task with two pipelines is a hash join, with separate pipelines for the build and for the probe side. This makes sense because the build must be complete before the probe can proceed. We will talk more about this later.

The Operators on each Driver communicate with each other through the Driver. The Driver picks output from one Operator, keeps tabs on stats and passes it to the next Operator. The data passed between Operators consists of vectors. In particular, an Operator produces/consumes a RowVector, which is a vector with a child vector for every column of the relation - it is the equivalent of RecordBatch in Arrow. All vectors are subclasses of velox::BaseVector.

Operator Sources, Sinks and State​

The first Operator in a Driver is called a source. The source operator takes Splits to figure the file/remote Task that provides its data, and produces a sequence of RowVectors. The last operator in the pipeline is called a sink. The sink operator does not produce an output RowVector, but rather puts the data somewhere for a consumer to retrieve. This is typically a PartitionedOutput. The consumer of the PartitionedOutput is an Exchange in a different task, where the Exchange is in the source position. Operators that are neither sources or sinks are things like FilterProject, HashProbe, HashAggregation and so on, more on these later.

Operators also contain a state. They can be blocked, accepting input, have output to produce or not, or may be notified that no more input is coming. Operators do not call the API functions of other Operators directly, but instead the Driver decides which Operator to advance next. This has the benefit that no part of the Driver's state is captured in nested function calls. The Driver has a flat stack and therefore can go on and off thread at any time, without having to unwind and restore nested function frames. 

Recap​

We have seen how a distributed query consists of fragments joined by shuffles. Each fragment has a Task, which has pipelines, Drivers and Operators. PlanNodes represent what the task is supposed to do. These tell the Task how to set up Drivers and Operators. Splits tell the Task where to find input, e.g. from files or from the other Tasks. A Driver corresponds to a thread of execution. It may be running or blocked, e.g. waiting for data to become available or for its consumer to consume already produced data. Operators pass data between themselves as vectors. 

In the next article we will discuss the different stages in the life of a query.

TL;DR​

The query trace tool helps analyze and debug query performance and correctness issues. It helps prevent interference from external noise in a production environment (such as storage, network, etc.) by allowing replay of a part of the query plan and dataset in an isolated environment, such as a local machine. This is much more efficient for query performance analysis and issue debugging, as it eliminates the need to replay the whole query in a production environment.

How Tracing Works​

The tracing process consists of two distinct phases: the tracing phase and the replaying phase. The tracing phase is executed within a production environment, while the replaying phase is conducted in a local development environment.

Tracing Phase

1. Trace replay required metadata, including the query plan fragment, query configuration, and connector properties, is recorded during the query task initiation.
2. Throughout query processing, each traced operator logs the input vectors or splits storing them in a designated storage location.
3. The metadata and splits are serialized in JSON format, and the operator data inputs are serialized using a presto serializer.

Replaying Phase

1. Read and deserialize the recorded query plan, extract the traced plan node, and assemble a plan fragment with customized source and sink nodes.
2. The source node reads the input from the serialized operator inputs on storage and the sink operator prints or logs out the execution stats.
3. Build a task with the assembled plan fragment in step 1. Apply the recorded query configuration and connector properties to replay the task with the same input and configuration setup as in production.

NOTE: The presto serialization might lose input vector encoding, such as lazy vector and nested dictionary encoding, which affects the operator’s execution. Hence, it might not always be the same as in production.

Tracing Framework​

Trace Writers​

There are three types of writers: TaskTraceMetadataWriter, OperatorTraceInputWriter, and OperatorTraceSplitWriter. They are used in the prod or shadow environment to record the real execution data.

• The TaskTraceMetadataWriter records the query metadata during task creation, serializes it, and saves it into a file in JSON format.
• The OperatorTraceInputWriter records the input vectors from the target operator, it uses a Presto serializer to serialize each vector batch and flush immediately to ensure that replay is possible even if a crash occurs during execution.
• The OperatorTraceSplitWriter captures the input splits from the target TableScan operator. It serializes each split and immediately flushes it to ensure that replay is possible even if a crash occurs during execution.

Storage Location​

It is recommended to store traced data in a remote storage system to ensure its preservation and accessibility even if the computation clusters are reconfigured or encounter issues. This also helps prevent nodes in the cluster from failing due to local disk exhaustion.

Users should start by creating a root directory. Writers will then create subdirectories within this root directory to organize the traced data. A well-designed directory structure will keep the data organized and accessible for replay and analysis.

Metadata Location

The TaskTraceMetadataWriter is set up during the task creation so it creates a trace directory named $rootDir/$queryId/$taskId.

Input Data and Split Location

The node ID consolidates the tracing for the same tracing plan node. The pipeline ID isolates the tracing data between operators created from the same plan node (e.g., HashProbe and HashBuild from the HashJoinNode). The driver ID isolates the tracing data of peer operators in the same pipeline from different drivers.

Correspondingly, to ensure the organized and isolated tracing data storage, the OperatorTraceInputWriter and OpeartorTraceSplitWriter are set up during the operator initialization and create a data or split tracing directory in

$rootDir/$queryId$taskId/$nodeId/$pipelineId/$driverId

Memory Management​

Add a new leaf system pool named tracePool for tracing memory usage, and expose it like memory::MemoryManager::getInstance()->tracePool().

Trace Readers​

Three types of readers correspond to the query trace writers: TaskTraceMetadataReader, OperatorTraceInputReader, and OperatorTraceSplitReader. The replayers typically use them in the local environment, which will be described in detail in the Query Trace Replayer section.

• The TaskTraceMetadataReader can load the query metadata JSON file and extract the query configurations, connector properties, and a plan fragment. The replayer uses these to build a replay task.
• The OperatorTraceInputReader reads and deserializes the input vectors in a tracing data file. It is created and used by a QueryTraceScan operator which will be described in detail in the Query Trace Scan section.
• The OperatorTraceSplitReader reads and deserializes the input splits in tracing split info files, and produces a list of exec::Split for the query replay.

Trace Scan​

As outlined in the How Tracing Works section, replaying a non-leaf operator requires a specialized source operator. This operator is responsible for reading data records during the tracing phase and integrating with Velox’s LocalPlanner with a customized plan node and operator translator.

TraceScanNode

We introduce a customized ‘TraceScanNode’ to replay a non-leaf operator. This node acts as the source node and creates a specialized scan operator, known as OperatorTraceScan with one per driver during the replay. The TraceScanNode contains the trace directory for the designated trace node, the pipeline ID associated with it, and a driver ID list passed during the replaying by users so that the OperatorTraceScan can locate the right trace input data or split directory.

OperatorTraceScan

As described in the Storage Location section, a plan node may be split into multiple pipelines, each pipeline can be divided into multiple operators. Each operator corresponds to a driver, which is a thread of execution. There may be multiple tracing data files for a single plan node, one file per driver.

Query Trace Replayer​

The query trace replayer is typically used in the local environment and works as follows:

1. Load traced query configurations, connector properties, and a plan fragment.
2. Extract the target plan node from the plan fragment using the specified plan node ID.
3. Use the target plan node in step 2 to create a replay plan node. Create a replay plan.
4. If the target plan node is a TableScanNode, add the replay plan node to the replay plan as the source node. Get all the traced splits using OperatorInputSplitReader. Use the splits as inputs for task replaying.
5. For a non-leaf operator, add a QueryTraceScanNode as the source node to the replay plan and then add the replay plan node.
6. Add a sink node, apply the query configurations (disable tracing), and connector properties, and execute the replay plan.

Detail usage please see the tracing doc in https://facebookincubator.github.io/velox/develop/debugging/tracing.html

Future Work​

• Add support for more operators
• Customize the replay task execution instead of AssertQueryBuilder
• Supports IcebergHiveConnector
• Add trace replay for an entire pipeline

TL;DR​

In late 2023, the Meta OSS (Open Source Software) Team requested all Meta teams to move the CI deployments from CircleCI to Github Actions. Voltron Data and Meta in collaboration migrated all the deployed Velox CI jobs. For the year 2024, Velox CI spend was on track to overshoot the allocated resources by a considerable amount of money. As part of this migration effort, the CI workloads were consolidated and optimized by Q2 2024, bringing down the projected 2024 CI spend by 51%.

Velox’s Continuous Integration Workload​

Continuous Integration (CI) is crucial for Velox’s success as an open source project as it helps protect from bugs and errors, reduces likelihood of conflicts and leads to increased community trust in the project. This is to ensure the Velox builds works well on a myriad of system architectures, operating systems and compilers - along with the ones used internally at Meta. The OSS build version of Velox also supports additional features that aren't used internally in Meta (for example, support for Cloud blob stores, etc.).

When a pull request is submitted to Velox, the following jobs are executed:

1. Linting and Formatting workflows:
   1. Header checks
   2. License checks
   3. Basic Linters
2. Ensure Velox builds on various platforms
   1. MacOS (Intel, M1)
   2. Linux (Ubuntu/Centos)
3. Ensure Velox builds under its various configurations
   1. Debug / Release builds
   2. Build default Velox build
   3. Build Velox with support for Parquet, Arrow and External Adapters (S3/HDFS/GCS etc.)
   4. PyVelox builds
4. Run prerequisite tests
   1. Unit Tests
   2. Benchmarking Tests
      1. Conbench is used to store and compare results, and also alert users on regressions
   3. Various Fuzzer Tests (Expression / Aggregation/ Exchange / Join etc)
   4. Signature Check and Biased Fuzzer Tests ( Expression / Aggregation)
   5. Fuzzer Tests using Presto as source of truth
5. Docker Image build jobs
   1. If an underlying dependency is changed, a new Docker CI image is built for
      1. Ubuntu Linux
      2. Centos
      3. Presto Linux image
6. Documentation build and publish Job
   1. If underlying documentation is changed, Velox documentation pages are rebuilt and published
   2. Netlify is used for publishing Velox web pages

Velox CI Optimization​

Previous implementation of CI in CircleCI grew organically and was unoptimized, resulting in long build times, and also significantly costlier. This opportunity to migrate to Github Actions helped to take a holistic view of CI deployments and actively optimized to reduce build times and CI spend. Note however, that there has been continued investment in reducing test times to further improve Velox reliability, stability and developer experience. Some of the optimizations completed are:

1. Persisting build artifacts across builds: During every build, the object files and binaries produced are cached. In addition to this, artifacts such as scalar function signatures and aggregate function signatures are produced. These signatures are used to compare with the baseline version, by comparing against the changes in the current PR to determine if the current changes are backwards incompatible or bias the newly added changes. Using a stash to persist these artifacts helps save one build cycle.

2. Optimizing our Instances: Building Velox is Memory and CPU intensive job. Some beefy instances (16-core machines) are used to build Velox. After the build, the build artifacts are copied to smaller instances (4 core machines) to run fuzzer tests and other jobs. Since these fuzzers often run for an hour and are less intensive than the build process, it resulted in significant CI savings while increasing the test coverage.

Instrumenting Velox CI Builds​

Velox CI builds were instrumented in Conbench so that it can capture various metrics about the builds:

1. Build times at translation unit / library/ project level.
2. Binary sizes produced at TLU/ .a,.so / executable level.
3. Memory pressure
4. Measure across time how our changes affect binary sizes

A nightly job is run to capture these build metrics and it is uploaded to Conbench. Velox build metrics report is available here: Velox Build Metrics Report

Acknowledgements​

A large part of the credit goes to Jacob Wujciak and the team at Voltron Data. We would also like to thank other collaborators in the Open Source Community and at Meta, including but not limited to:

Meta: Sridhar Anumandla, Pedro Eugenio Rocha Pedreira, Deepak Majeti, Meta OSS Team, and others

Voltron Data: Jacob Wujciak, Austin Dickey, Marcus Hanwell, Sri Nadukudy, and others

TL;DR​

Queries that use TRY or TRY_CAST may experience poor performance and high CPU usage due to excessive exception throwing. We optimized CAST to indicate failure without throwing and introduced a mechanism for scalar functions to do the same. Microbenchmark measuring worst case performance of CAST improved 100x. Samples of production queries show 30x cpu time improvement.

TRY and TRY(CAST)​

TRY construct can be applied to any expression to suppress errors and turn them into NULL results. TRY_CAST is a version of CAST that suppresses errors and returns NULL instead.

For example, parse_datetime('2024-05-', 'YYYY-MM-DD') fails:

	 Invalid format: "2024-05-" is too short

, but TRY(parse_datetime('2024-05-', 'YYYY-MM-DD')) succeeds and returns NULL.

Similarly, CAST('foo' AS INTEGER) fails:

	Cannot cast 'foo' to INT

, but TRY_CAST('foo' AS INTEGER) succeeds and returns NULL.

TRY_CAST vs. TRY(CAST)​

TRY can wrap any expression, so one can wrap CAST as well:

  TRY(CAST('foo' AS INTEGER))

Wrapping CAST in TRY is similar to TRY_CAST, but not equivalent. TRY_CAST suppresses only cast errors, while TRY suppresses any error in the expression tree.

For example, CAST(1/0 AS VARCHAR) fails:

  Division by zero

, TRY_CAST(1/0 AS VARCHAR) also fails:

  Division by zero

, but TRY(CAST(1/0 AS VARCHAR)) succeeds and returns NULL.

In this case, the error is generated by division operation (1/0). TRY_CAST cannot suppress that error, but TRY can. More generally, TRY(CAST(...)) suppresses all errors in all expressions that are evaluated to produce an input for CAST as well as errors in CAST itself, but TRY_CAST suppresses errors in CAST only.

What happens when many rows fail?​

In most cases only a fraction of rows generates an error. However, there are queries where a large percentage of rows fail. In these cases, a lot of CPU time goes into handling exceptions.

For example, one Prestissimo query used 3 weeks of CPU time, 93% of which was spent processing try(date_parse(...)) expressions where most rows failed. Here is a profile for that query that shows that all the time went into stack unwinding:

This query processes 14B rows, ~70% of which fail in date_parse(...) function due to the date string being empty.

    presto> select try(date_parse('', '%Y-%m-%d'));
     _col0
    -------
     NULL
    (1 row)


    – TRY suppressed Invalid format: "" error and produced a NULL.

Velox tracks the number of suppressed exceptions per operator / plan node and reports these as numSilentThrow runtime stat. For this query, Velox reported 21B throws for a single FilterProject node that processed 14B rows. Before the optimizations, each failing row used to throw twice. An earlier blog post from Laith Sakka explains why. After the optimizations this query’s CPU time dropped to 17h: 30x difference from the original cpu time. Compared to Presto Java, this query uses 4x less cpu time (originally it used 6x more).

We observed similar issues with queries that use other functions that parse strings as well as casts from strings.

Solution​

To avoid the performance penalty of throwing exceptions we need to report errors differently. Google’s Abseil library uses absl::Status to return errors from void functions and absl::StatusOr to return value or error from non-void functions. Arrow library has similar Status and Result. Our own Folly has folly::Expected. Inspired by these examples we introduced velox::Status and velox::Expected.

velox::Status holds a generic error code and an error message.

velox::Expected<T> is a typedef for folly::Expected<T, velox::Status>.

For example, a non-throwing modulo operation can be implemented like this:

  Expected<int> mod(int a, int b) {
    if (b == 0) {
      return folly::makeUnexpected(Status::UserError(“Division by zero”));
    }

    return a % b;
  }

Non-throwing Simple Functions​

We extended the Simple Function API to allow authoring non-throwing scalar functions. The function author can now define a ‘call’ method that returns Status. Such a function can indicate an error by returning a non-OK status.

  Status call(result&, arg1, arg2,..)

These functions are still allowed to throw and exceptions will be handled properly, but not throwing improves performance of expressions that use TRY.

Modulo SQL function would look like this:

    template <typename TExec>
    struct NoThrowModFunction {
      VELOX_DEFINE_FUNCTION_TYPES(TExec);

      Status call(int64_t& result, const int64_t& a, const int64_t& b) {
        if (b == 0) {
          return Status::UserError("Division by zero");
        }

        result = a % b;
        return Status::OK();
      }
    };

We changed date_parse, parse_datetime, and from_iso8601_date Presto functions to use the new API and report errors without throwing.

Non-throwing Vector functions​

Vector functions can implement non-throwing behavior by leveraging the new EvalCtx::setStatus(row, status) API. However, nowadays we expect virtually all functions to be written using Simple Function API.

Non-throwing CAST​

CAST is complex. A single name refers to multiple dozen individual operations. The full matrix of supported conversions is available in the Velox documentation. Not all casts throw. For example, cast from an integer to a string does not throw. However, casts from strings may fail in multiple ways. A common failure scenario is cast from an empty string. Laith Sakka optimized this use case earlier.

> select cast('' as integer);
Cannot cast '' to INT

However, we are also seeing failures in casting non-empty strings and NaN floating point values to integers.

> select cast(nan() as bigint);
Unable to cast NaN to bigint

> select cast('123x' as integer);
Cannot cast '123x' to INT

CAST from string to integer and floating point value is implemented using folly::to template. Luckily there is a non-throwing version: folly::tryTo. We changed our CAST implementation to use folly::tryTo to avoid throwing. Not throwing helped improve performance of TRY_CAST by 20x.

Still, the profile showed that there is room for further improvement.

Do not produce or store error messages under TRY​

After switching to non-throwing implementation, the profile showed that half the cpu time went into folly::makeConversionError. folly::tryTo returns result or ConversionCode enum. CAST uses folly::makeConversionError to convert ConversionCode into a user-friendly error message. This involves allocating and populating a string for the error message, copying it into the std::range_error object, then copying it again into Status. This error message is very helpful if it is being propagated all the way to the user, but it is not needed if the error is suppressed via TRY or TRY_CAST.

To solve this problem we introduced a thread-local flag, threadSkipErrorDetails, that indicates whether Status needs to include a detailed error message or not. By default, this flag is ‘false’, but TRY and TRY_CAST set it to ‘true’. CAST logic checks this flag to decide whether to call folly::makeConversionError or not. This change gives a 3x performance boost to TRY_CAST and 2x to TRY.

    if (threadSkipErrorDetails()) {
      return folly::makeUnexpected(Status::UserError());
    }

    return folly::makeUnexpected(Status::UserError(
        "{}", folly::makeConversionError(result.error(), "").what()));

After this optimization, we observed that TRY(CAST(...)) is up to 5x slower than TRY_CAST when many rows fail.

The profile revealed that 30% of cpu time went to EvalCtx::ensureErrorsVectorSize. For every row that fails, we call EvalCtx::ensureErrorsVectorSize to resize the error vector to accommodate that row. When many rows fail we end up resizing a lot: resize(1), resize(2), resize(3),...resize(n). We fixed this by pre-allocating the error vector in the TRY expression.

Another 30% of cpu time went into managing reference counts for std::shared_ptr<std::exception_ptr> stored in the errors vector. We do not need error details for TRY, hence, no need to store these values. We fixed this by making error values in error vector optional and updating EvalCtx::setStatus to skip writing these under TRY.

After all these optimizations, the microbenchmark that measures performance of casting invalid strings into integers showed 100x improvement. The benchmark evaluates 4 expressions:

• TRY_CAST(‘’ AS INTEGER)
• TRY(CAST(‘’ AS INTEGER))
• TRY_CAST(‘$’ AS INTEGER)
• TRY(CAST(‘$’ AS INTEGER))

When we started, the benchmark results were:

===============================================================
[...]hmarks/ExpressionBenchmarkBuilder.cpp     relative  time/iter   iters/s
================================================================
cast##try_cast_invalid_empty_input                          2.40ms    417.47
cast##tryexpr_cast_invalid_empty_input                    402.63ms      2.48
cast##try_cast_invalid_nan                                392.14ms      2.55
cast##tryexpr_cast_invalid_nan                            827.09ms      1.21

At the end the numbers improved 100x:

cast##try_cast_invalid_empty_input                          2.16ms    463.62
cast##tryexpr_cast_invalid_empty_input                      4.29ms    232.95
cast##try_cast_invalid_nan                                  5.47ms    182.83
cast##tryexpr_cast_invalid_nan                              7.76ms    128.81

Note: The performance of TRY_CAST(‘’ AS INTEGER) hasn’t changed because this particular use case has been optimized by Laith Sakka earlier.

Next steps​

We can identify queries with a high percentage of numSilentThrow rows and change throwing functions to not throw.

For simple functions this involves changing the ‘call’ method to return Status and replacing ‘throw’ statements with return Status::UserError(...). You get extra points for producing error messages conditionally based on thread-local flag threadSkipErrorDetails().

template <typename TExec>
struct NoThrowModFunction {
  VELOX_DEFINE_FUNCTION_TYPES(TExec);

  Status call(int64_t& result, const int64_t& a, const int64_t& b) {
    if (b == 0) {
      If (threadSkipErrorDetails()) {
          return Status::UserError();
      }
      return Status::UserError("Division by zero");
    }

    result = a % b;
    return Status::OK();
  }
};

We are changing CAST(varchar AS date) to not throw.

We provided a non-throwing ‘call’ API for simple functions that never return a NULL for a non-NULL input. This covers the majority of Presto functions. For completeness, we would want to provide non-throwing ‘call’ APIs for all other use cases:

• bool call() for returning NULL sometimes
• callAscii for processing all-ASCII inputs
• callNullable for processing possibly NULL inputs
• callNullFree for processing complex inputs with all NULLs removed.

Acknowledgements​

Thank you Laith Sakka for doing the initial work to investigate and optimize TRY_CAST for empty strings and sharing your findings in a blog post.

Thank you Orri Erling for adding numSilentThrow runtime stat to report number of suppressed exceptions.

Thank you Pedro Eugenio Rocha Pedreira for introducing the velox::Status class.

Thank you Bikramjeet Vig, Jimmy Lu, Orri Erling, Pedro Eugenio Rocha Pedreira and Xiaoxuan Meng for brainstorming and helping with code reviews.

What is LIKE?​

LIKE is a very useful SQL operator. It is used to do string pattern matching. The following examples for LIKE usage are from the Presto doc:

SELECT * FROM (VALUES ('abc'), ('bcd'), ('cde')) AS t (name)
WHERE name LIKE '%b%'
--returns 'abc' and  'bcd'

SELECT * FROM (VALUES ('abc'), ('bcd'), ('cde')) AS t (name)
WHERE name LIKE '_b%'
--returns 'abc'

SELECT * FROM (VALUES ('a_c'), ('_cd'), ('cde')) AS t (name)
WHERE name LIKE '%#_%' ESCAPE '#'
--returns 'a_c' and  '_cd'

These examples show the basic usage of LIKE:

• Use % to match zero or more characters.
• Use _ to match exactly one character.
• If we need to match % and _ literally, we can specify an escape char to escape them.

When we use Velox as the backend to evaluate Presto's query, LIKE operation is translated into Velox's function call, e.g. name LIKE '%b%' is translated to like(name, '%b%'). Internally Velox converts the pattern string into a regular expression and then uses regular expression library RE2 to do the pattern matching. RE2 is a very good regular expression library. It is fast and safe, which gives Velox LIKE function a good performance. But some popularly used simple patterns can be optimized using direct simple C++ string functions instead of regex. e.g. Pattern hello% matches inputs that start with hello, which can be implemented by direct memory comparison of prefix ('hello' in this case) bytes of input:

// Match the first 'length' characters of string 'input' and prefix pattern.
bool matchPrefixPattern(
    StringView input,
    const std::string& pattern,
    size_t length) {
  return input.size() >= length &&
      std::memcmp(input.data(), pattern.data(), length) == 0;
}

It is much faster than using RE2. Benchmark shows it gives us a 750x speedup. We can do similar optimizations for some other patterns:

• %hello: matches inputs that end with hello. It can be optimized by direct memory comparison of suffix bytes of the inputs.
• %hello%: matches inputs that contain hello. It can be optimized by using std::string_view::find to check whether inputs contain hello.

These simple patterns are straightforward to optimize. There are some more relaxed patterns that are not so straightforward:

• hello_velox%: matches inputs that start with 'hello', followed by any character, then followed by 'velox'.
• %hello_velox: matches inputs that end with 'hello', followed by any character, then followed by 'velox'.
• %hello_velox%: matches inputs that contain both 'hello' and 'velox', and there is a single character separating them.

Although these patterns look similar to previous ones, but they are not so straightforward to optimize, _ here matches any single character, we can not simply use memory comparison to do the matching. And if user's input is not pure ASCII, _ might match more than one byte which makes the implementation even more complex. Also note that the above patterns are just for illustrative purpose. Actual patterns can be more complex. e.g. h_e_l_l_o, so trivial algorithm will not work.

Optimizing Relaxed Patterns​

We optimized these patterns as follows. First, we split the patterns into a list of sub patterns, e.g. hello_velox% is split into sub-patterns: hello, _, velox, %, because there is a % at the end, we determine it as a kRelaxedPrefix pattern, which means we need to do some prefix matching, but it is not a trivial prefix matching, we need to match three sub-patterns:

• kLiteralString: hello
• kSingleCharWildcard: _
• kLiteralString: velox

For kLiteralString we simply do a memory comparison:

if (subPattern.kind == SubPatternKind::kLiteralString &&
    std::memcmp(
        input.data() + start + subPattern.start,
        patternMetadata.fixedPattern().data() + subPattern.start,
        subPattern.length) != 0) {
  return false;
}

Note that since it is a memory comparison, it handles both pure ASCII inputs and inputs that contain Unicode characters.

Matching _ is more complex considering that there are variable length multi-bytes character in unicode inputs. Fortunately there are existing libraries which provides unicode related operations: utf8proc. It provides functions that tells us whether a byte in input is the start of a character or not, how many bytes current character consists of etc. So to match a sequence of _ our algorithm is:

if (subPattern.kind == SubPatternKind::kSingleCharWildcard) {
  // Match every single char wildcard.
  for (auto i = 0; i < subPattern.length; i++) {
    if (cursor >= input.size()) {
      return false;
    }

    auto numBytes = unicodeCharLength(input.data() + cursor);
    cursor += numBytes;
  }
}

Here:

• cursor is the index in the input we are trying to match.
• unicodeCharLength is a function which wraps utf8proc function to determine how many bytes current character consists of.

So the logic is basically repeatedly calculate size of current character and skip it.

It seems not that complex, but we should note that this logic is not effective for pure ASCII input. Every character is one byte in pure ASCII input. So to match a sequence of _, we don't need to calculate the size of each character and compare in a for-loop. In fact, we don't need to explicitly match _ for pure ASCII input as well. We can use the following logic instead:

for (const auto& subPattern : patternMetadata.subPatterns()) {
    if (subPattern.kind == SubPatternKind::kLiteralString &&
        std::memcmp(
            input.data() + start + subPattern.start,
            patternMetadata.fixedPattern().data() + subPattern.start,
            subPattern.length) != 0) {
      return false;
    }
}

It only matches the kLiteralString pattern at the right position of the inputs, _ is automatically matched(actually skipped). No need to match it explicitly. With this optimization we get 40x speedup for kRelaxedPrefix patterns, 100x speedup for kRelaxedSuffix patterns.

Thank you Maria Basmanova for spending a lot of time reviewing the code.

Definition​

Reduce_agg is the only lambda aggregate Presto function. It allows users to define arbitrary aggregation logic using 2 lambda functions.

reduce_agg(inputValue T, initialState S, inputFunction(S, T, S), combineFunction(S, S, S)) → S

Reduces all non-NULL input values into a single value. inputFunction will be invoked for
each non-NULL input value. If all inputs are NULL, the result is NULL. In addition to taking
the input value, inputFunction takes the current state, initially initialState, and returns the
new state. combineFunction will be invoked to combine two states into a new state. The final
state is returned. Throws an error if initialState is NULL or inputFunction or combineFunction
returns a NULL.

Once can think of reduce_agg as using inputFunction to implement partial aggregation and combineFunction to implement final aggregation. Partial aggregation processes a list of input values and produces an intermediate state:

auto s = initialState;
for (auto x : input) {
   s = inputFunction(s, x);
}

return s;

Final aggregation processes a list of intermediate states and computes the final state.

auto s = intermediates[0];
for (auto i = 1; i < intermediates.size(); ++i)
   s = combineFunction(s, intermediates[i]);
}

return s;

For example, one can implement SUM aggregation using reduce_agg as follows:

reduce_agg(c, 0, (s, x) -> s + x, (s, s2) -> s + s2)

Implementation of AVG aggregation is a bit trickier. For AVG, state is a tuple of sum and count. Hence, reduce_agg can be used to compute (sum, count) pair, but it cannot compute the actual average. One needs to apply a scalar function on top of reduce_agg to get the average.

SELECT id, sum_and_count.sum / sum_and_count.count FROM (
  SELECT id, reduce_agg(value, CAST(row(0, 0) AS row(sum double, count bigint)),
    (s, x) -> CAST(row(s.sum + x, s.count + 1) AS row(sum double, count bigint)),
    (s, s2) -> CAST(row(s.sum + s2.sum, s.count + s2.count) AS row(sum double, count bigint))) AS sum_and_count
  FROM t
  GROUP BY id
);

The examples of using reduce_agg to compute SUM and AVG are for illustrative purposes. One should not use reduce_agg if a specialized aggregation function is available.

One use case for reduce_agg we see in production is to compute a product of input values.

reduce_agg(c, 1.0, (s, x) -> s * x, (s, s2) -> s * s2)

Another example is to compute a list of top N distinct values from all input arrays.

reduce_agg(x, array[],
            (a, b) -> slice(reverse(array_sort(array_distinct(concat(a, b)))), 1, 1000),
            (a, b) -> slice(reverse(array_sort(array_distinct(concat(a, b)))), 1, 1000))

Note that this is equivalent to the following query:

SELECT array_agg(v) FROM (
    SELECT DISTINCT v
    FROM t, UNNEST(x) AS u(v)
    ORDER BY v DESC
    LIMIT 1000
)

Implementation​

Efficient implementation of reduce_agg lambda function is not straightforward. Let’s consider the logic for partial aggregation.

auto s = initialState;
for (auto x : input) {
   s = inputFunction(s, x);
}

This is a data-dependent loop, i.e. the next loop iteration depends on the results of the previous iteration. inputFunction needs to be invoked on each input value x separately. Since inputFunction is a user-defined lambda, invoking inputFunction means evaluating an expression. And since expression evaluation in Velox is optimized for processing large batches of values at a time, evaluating expressions on one value at a time is very inefficient. To optimize the implementation of reduce_agg we need to reduce the number of times we evaluate user-defined lambdas and increase the number of values we process each time.

One approach is to

1. convert all input values into states by evaluating inputFunction(initialState, x);
2. split states into pairs and evaluate combineFunction on all pairs;
3. repeat step (2) until we have only one state left.

Let’s say we have 1024 values to process. Step 1 evaluates inputFunction expression on 1024 values at once. Step 2 evaluates combineFunction on 512 pairs, then on 256 pairs, then on 128 pairs, 64, 32, 16, 8, 4, 2, finally producing a single state. Step 2 evaluates combineFunction 9 times. In total, this implementation evaluates user-defined expressions 10 times on multiple values each time. This is a lot more efficient than the original implementation that evaluates user-defined expressions 1024 times.

In general, given N inputs, the original implementation evaluates expressions N times while the new one log2(N) times.

Note that in case when N is not a power of two, splitting states into pairs may leave an extra state. For example, splitting 11 states produces 5 pairs + one extra state. In this case, we set aside the extra state, evaluate combineFunction on 5 pairs, then bring extra state back to a total of 6 states and continue.

A benchmark, velox/functions/prestosql/aggregates/benchmarks/ReduceAgg.cpp, shows that initial implementation of reduce_agg is 60x slower than SUM, while the optimized implementation is only 3x slower. A specialized aggregation function will always be more efficient than generic reduce_agg, hence, reduce_agg should be used only when specialized aggregation function is not available.

Finally, a side effect of the optimized implementation is that it doesn't support applying reduce_agg to sorted inputs. I.e. one cannot use reduce_agg to compute an equivalent of

	SELECT a, array_agg(b ORDER BY b) FROM t GROUP BY 1

The array_agg computation depends on order of inputs. A comparable implementation using reduce_agg would look like this:

	SELECT a,
        reduce_agg(b, array[],
                    (s, x) -> concat(s, array[x]),
                    (s, s2) -> concat(s, s2)
                    ORDER BY b)
    FROM t GROUP BY 1

To respect ORDER BY b, the reduce_agg would have to apply inputFunction to each input value one at a time using a data-dependent loop from above. As we saw, this is very expensive. The optimization we apply does not preserve the order of inputs, hence, cannot support the query above. Note that Presto doesn't support applying reduce_agg to sorted inputs either.

Thank you Orri Erling for brainstorming and Xiaoxuan Meng and Pedro Eugênio Rocha Pedreira for reviewing the code.

One of the queries shadowed internally at Meta was much slower in Velox compared to presto(2 CPU days vs. 4.5 CPU hours). Initial investigation identified that the overhead is related to casting empty strings inside a try_cast.

In this blogpost I summarize my learnings from investigating and optimizing try_cast.

Start and end results​

Initial benchmark:

name                                             total time
try_cast(empty_string_col as int)                     4.88s
try_cast(valid_string_col as int)                    2.15ms

The difference between casting a valid and invalid input is huge (>1000X), although ideally casting an invalid string should be just setting a null and should not be that expensive.

Benchmark results after optimization:

name                                             total time
try_cast(empty_string_col as int)                    1.24ms
try_cast(valid_string_col as int)                    2.15ms

Sources of regression​

The investigation revealed several factors that contributed to the huge gap, summarized in the points below in addition to their approximate significance.

Error logs overhead.

Whenever a VeloxUserError is thrown an error log used to be generated, however those errors are expected to, (1) either get converted to null if is thrown from within a try, (2) or show up to the user otherwise. Hence, no need for that expensive logging .

Moreover, each failing row used to generate two log message because VELOX_USER_FAIL was called twice. Disabling logging for user error helped save 2.6s of the 4.88s.

Throwing overhead.

Each time a row is casted four exception were thrown:

1. From within Folly library.
2. From Cast in Conversions.h, the function catch the exception thrown by Folly and convert it to Velox exception and throw it.
3. From castToPrimitive function, which catch the exception and threw a new exception with more context.
4. Finally, a forth throw came from applyToSelectedNoThrow which caught an exception and called toVeloxException to check exception type and re-throw.

Those are addressed and avoided using the following:

1. When the input is an empty string, avoid calling folly by directly checking if the input is empty.
2. Remove the catch and re-throw from Conversions.h
3. Introduce setVeloxExceptionError, which can be used to set the error directly in evalContext without throwing (does not call toVeloxException).
4. Optimize applyToSelectedNoThrow to call setVeloxExceptionError if it catches Velox exception.

With all those changes throwing exceptions is completely avoided when casting empty strings. This takes the runtime down to 382.07ms, but its still much higher than 2.15ms.

** Velox exception construction overhead.**

Constructing Velox exception is expensive, even when there is no throw at all! Luckily this can be avoided for try_cast, since the output can be directly set to null without having to use the errorVector to track errors. By doing so the benchmark runtime goes down to 1.24ms.

Follow up tasks​

After all the changes we have the following performance numbers for other patterns of similar expressions (much better than before but still can be optimized a lot).

try_cast(empty_string_col as int)                     1.24ms    808.79

try_cast(invalid_string_col as int)                  393.61ms     2.54

try(cast(empty_string_col as int))                   375.82ms     2.66

try(cast(invalid_string_col as int))                767.74ms      1.30

All these can be optimized to have the same runtime cost of the first expression 1.24ms.

To do that two thing are needed:

1) Tracking errors for try, should not require constructing exceptions

The way errors are tracked when evaluating a try expression is by setting values in an ErrorVector; which is a vector of VeloxException pointers. This forces the construction of a Velox exception for each row, but that is not needed (for try expressions) since only row numbers need to be tracked to be converted eventually to nulls, but not the actual errors.

This can be changed such that errors are tracked using a selectivity vector. Its worth noting that for other expressions such as conjunct expressions this tracking is needed, hence we need to distinguish between both.

This would help optimize any try(x) expression where x throws for large number of rows.

2)Use throw-free cast library

Avoiding throw and instead returning a boolean should allow us to directly set null in try_cast and avoid construction of exceptions completely.

While this is done now for empty strings, its not done for all other types of errors. Folly provides a non-throwing API (folly::tryTo) that can be tried for that purpose. folly::tryTo. According to the folly documentation On the error path, you can expect tryTo to be roughly three orders of magnitude faster than the throwing to and to completely avoid any lock contention arising from stack unwinding.

Presto provides an array_sort function to sort arrays in ascending order with nulls placed at the end.

presto> select array_sort(array[2, 5, null, 1, -1]);
        _col0
---------------------
 [-1, 1, 2, 5, null]

There is also an array_sort_desc function that sorts arrays in descending order with nulls placed at the end.

presto> select array_sort_desc(array[2, 5, null, 1, -1]);
        _col0
---------------------
 [5, 2, 1, -1, null]

Both array_sort and array_sort_desc place nulls at the end of the array.

There is also a version of array_sort function that takes a comparator lambda function and uses it to sort the array.

array_sort(array(T), function(T, T, int)) -> array(T)

A common use case is to sort an array of structs using one of the struct fields as the sorting key.

presto> select array_sort(array[row('apples', 23), row('bananas', 12), row('grapes', 44)],
        -> (x, y) -> if (x[2] < y[2], -1, if(x[2] > y[2], 1, 0)));

                                         _col0
---------------------------------------------------------------------------------------
 [{f0=bananas, f1=12}, {f0=apples, f1=23}, {f0=grapes, f1=44}]

This is all very nice and convenient, but there is a catch.

The documentation says that the "comparator will take two nullable arguments representing two nullable elements of the array."" Did you notice the word "nullable" in "nullable arguments" and "nullable elements"? Do you think it is important? It is Ok if the answer is No or Not Really. Turns out this "nullable" thing is very important. The comparator is expected to handle null inputs and should not assume that inputs are not null or that nulls are handled automatically.

Why is it important to handle null inputs? Let’s see what happens if the comparator doesn’t handle nulls.

presto> select array_sort(array[2, 3, null, 1],
                            (x, y) -> if (x < y, -1, if (x > y, 1, 0)));
      _col0
-----------------
 [2, 3, null, 1]

The result array is not sorted! If subsequent logic relies on the array to be sorted the query will silently return wrong results. And if there is no logic that relies on the sortedness of the array then why waste CPU cycles on sorting?

Why is the array not sorted? That’s because the comparator returns 0 whenever x or y is null.

	x < y  returns null if x or y is null, then
	x > y  returns null if x or y is null, then
	result is 0

This confuses the sorting algorithm as it sees that 1 == null, 2 == null, 3 == null, but 1 != 2 and 1 != 3. The algorithm assumes that the comparator function is written correctly, e.g. if a < b then b > a and if a == b and b == c then a == c. Comparator function that doesn’t handle nulls does not satisfy these rules and causes unpredictable results.

To handle null inputs, the comparator function needs to be modified, for example, like so:

	(x, y) -> CASE WHEN x IS NULL THEN 1
								WHEN y IS NULL THEN -1
								WHEN x < y THEN -1
								WHEN x > y THEN 1
								ELSE 0 END

presto> select array_sort(array[2, 3, null, 1],
        -> (x, y) -> CASE WHEN x IS NULL THEN 1
        -> WHEN y IS NULL THEN -1
        -> WHEN x < y THEN -1
        -> WHEN x > y THEN 1
        -> ELSE 0 END
        -> );
      _col0
-----------------
 [1, 2, 3, null]

This is longer and harder to read, but the result array is sorted properly. The new comparator says that null is greater than any other value, so null is placed at the end of the array.

Note: When (x, y) return -1, the algorithm assumes that x <= y.

Writing comparators correctly is not easy. Writing comparators that handle null inputs is even harder. Having no feedback when a comparator is written incorrectly makes it yet harder to spot bugs and fix them before a query lands in production and starts producing wrong results.

I feel that Presto’s array_sort function with a custom comparator is dangerous and hard to use and I wonder if it makes sense to replace it with a safer, easier to use alternative.

array_sort(array(T), function(T, U)) -> array(T)

This version takes an array and a transform lambda function that specifies how to compute sorting keys from the array elements.

To sort array of structs by one of the struct fields, one would write

presto> select array_sort(array[row('apples', 23), row('bananas', 12), row('grapes', 44)],
                            x -> x[2])

                                         _col0
---------------------------------------------------------------------------------------
 [{f0=bananas, f1=12}, {f0=apples, f1=23}, {f0=grapes, f1=44}]

This version would sort the array by the sorting keys computed using the specified lambda in ascending order placing nulls at the end of the array.

A matching array_sort_desc function would sort in descending order placing nulls at the end of the array.

These functions would be easier to write and read and null handling will happen automatically.

We implemented these functions in Velox.

We also added partial support for array_sort with a comparator lambda function. Expression compiler in Velox analyzes the comparator expression to determine whether it can be re-written to the alternative version of array_sort. If so, it re-writes the expression and evaluates it. Otherwise, it throws an unsupported exception.

For example,

	array_sort(a, (x, y) -> if (x[2] < y[2], -1, if(x[2] > y[2], 1, 0)))

is re-written to

	array_sort(a, x -> x[2])

This rewrite allows Prestissimo and Presto-on-Spark-on-Velox to support common use cases and do so efficiently.

The rewrite handles a few different ways to express the same comparator. Some examples:

    // becomes array_sort(a, f(x))
	(x, y) -> if(f(x) < f(y), -1, if(f(x) > f(y), 1, 0))

	// becomes array_sort_desc(a, f(x))
	(x, y) -> if(f(x) < f(y), 1, if(f(x) > f(y), -1, 0))

	// becomes array_sort(a, f(x))
	(x, y) -> if(f(x) < f(y), -1, if(f(x) = f(y), 0, 1))

	// becomes array_sort(a, f(x))
	(x, y) -> if(f(x) = f(y), 0, if(f(x) < f(y), -1, 1))

	// becomes array_sort(a, f(x))
	(x, y) -> if(f(y) < f(x), 1, if(f(x) < f(y), -1, 0))

Why didn’t we implement full support for comparator lambda functions in array_sort? Actually, we couldn’t think of an efficient way to do that in a vectorized engine. Velox doesn’t use code generation and interprets expressions. It can do that efficiently if it can process data in large batches. array_sort with custom comparator doesn’t lend itself well to such processing.

array_sort with a transform lambda works well in a vectorized engine. To process a batch of arrays, Velox first evaluates the transform lambda on all the elements of the arrays, then sorts the results.

For further reading, consider the Vectorized and performance-portable Quicksort blog post from Google.

Thank you Orri Erling for brainstorming and Xiaoxuan Meng for reviewing the code.