10 posts tagged with "tech-blog"

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,

What is LIKE?​

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(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

Thank you Orri Erling for brainstorming and Xiaoxuan Meng and

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.

This blogpost is part of a series of blog posts that discuss different features and optimizations of the simple function interface.

Efficient Complex Types​

In this blogpost, we will discuss two major recent changes to the simple function interface to make its performance comparable to the vector function implementations for functions that produce or consume complex types (Arrays, Maps and Rows).

To show how much simpler simple functions are. The figure below shows a function NestedMapSum written in both the simple and vector interfaces. The function consumes a nested map and computes the summations of all values and keys. Note that the vector function implementation is minimal without any special optimization (ex: vector reuse, fast path for flat inputs ..etc). Adding optimizations will make it even longer.

View types for inputs​

The previous representations of input complex types in the simple function interface were computationally expensive. Data from vectors used to be copied into std containers and passed to simple functions to process it. Arrays, Maps and Structs used to be materialized into std::vectors, folly::F14FastMap and std::tuples. The graph below illustrates the previous approach.

The previous approach has two key inefficiencies; Eager materialization : For each row, all the data in the input vector is decoded and read before calling the function. And Double reading, the data is read twice once when the input is constructed, and again in the function when it's used.

In order to mitigate those regressions, Velox introduced View types: ArrayViews, MapViews ...etc. The goal is to keep the authoring simple but achieve at least the performance of a basic vector implementation that decodes input and applies some logic for every row without any special optimizations.

The view types are Lazy, very cheap to construct and do not materialize the underlying data unless the code accesses it.For example, the function array_first only needs to read the first element in every array, moreover the cardinality function does not need to read any elements in the array. They view types have interfaces similar to those of std::containers.

In a simplified form, an ArrayView stores the length and the offset of the array within the vector, in addition to a pointer to the elements array. Only when an element is accessed then an OptionalAccessor is created, which contains the index of the accessed element and a pointer to the containing vector. Only when the user calls value() or has_value() on that accessor then the nullity or the value is read. Other view types are implemented in a similar way, The graph below illustrates the process.

The graph below compares the runtime of some functions written in the simple interface before and after the introduction of the view types. The speedup for arrays is around 2X, for maps the speed is much higher > 10X because materializing the intermediate representation previously involves hashing the elements while constructing the hashmap. Furthermore, the overhead of materialization for nested complex types is very high as well, as reflected in row_arrays_sum.

The graph below compares the runtimes of some functions written using the simple interface, a basic vector function implementation with no special optimizations for the non general case, and a vector implementation that is specialized for flat and null free. The bars are annotated with the line of codes (LOC) used to implement each function.

We can see that LOC are significantly lower for simple functions. ArraySum with flat and null free optimization is faster because the summation can be optimized much better when it's performed over a sequential array of data. The reason the simple is faster than the vector for some benchmarks is because we have several optimizations in the simple interface that are not implemented in the basic vector versions.

Writer types for outputs​

A similar pattern of inefficiency existed for functions with complex output types. The graph below shows the previous path of writing complex types through the simple function interface. In the previous path, for each row, the result is first written to a temporary object (std::vector, folly::f14FastMap<>, etc.), then serialized into the Velox vector.

We changed the writing path so that the data is written directly into the Velox vector during the function evaluation. By introducing writer types: ArrayWriter, MapWriter, RowWriter. This avoids the double materialization and the unnecessary sorting and hashing for maps.

Consider the function below for example that constructs an array [0, n).

outerArray is an array writer and whenever push_back is called, the underlying vector array is updated directly and a new element is written to it.

In order & final elements writing: Unlike the previous interface, the new writer interface needs to write things in order, since it directly serializes elements into Velox vector buffers. Written elements also can not be modified.

For example, for a function with an Array<Map> output , we can't add three maps, and write to them concurrently. The new interface should enforce that one map is written completely before the next one starts. This is because we are serializing things directly in the map vector, and to determine the offset of the new map we need first to know the end offset of the previous one.

The code below shows a function with Array<Map> output:

Compatibility with std::like containers.: Unfortunately, the new interface is not completely compatible with std::like interfaces, in fact, it deviates syntactically and semantically (for example a std::map enforces unique keys and ordering of elements) while map writer does not. When the element type is primitive (ex: Array<int>) we enable std::like APIs (push_back, emplace()).

But we can not do that for nested complex types (ex:Array<Array<int>>) since it breaks the in-order & final elements writing rule mentioned above.

The figure below shows the performance gain achieved by this change, functions' performance is evaluated before and after the change.

The chart below compares the performance of those functions with vector functions implementations, a vector function with an optimization that precomputes the total size needed for the output vector and a single resize is also added. Note that those functions do almost no computation other than constructing the output map. Hence the resize cost becomes very critical, if those were doing more work, then its effect would be less. Furthermore, the gap indicates that it might be worth it to add a way in the simple interface that enables pre-computing/resizing the output vector size.

Examples:​

For full documentation of the view and writer types, APIs, and how to write simple functions follow thelink.

When Velox was open sourced in August 2021, it was not nearly as easily usable and portable as it is today. In order for Velox to become the unified execution engine blurring the boundaries for data analytics and ML, we needed Velox to be easy to build and package on multiple platforms, and support a wide range of hardware architectures. If we are supporting all these platforms, we also need to ensure that Velox remains fast and regressions are caught early.

To improve the Velox experience for users and community developers, Velox has partnered with Voltron Data to help make Velox more accessible and user-friendly. In this blog post, we will examine the challenges we faced, the improvements that have already been made, and the ones yet to come.

Enhancements & Improvements​

Velox was a product of the mono repo and required installation of dependencies on the system via a script. Any change in the state of the host system could cause a build failure and introduce version conflicts of dependencies. Fixing these challenges was a big focus to help the Velox Community and we worked in collaboration with the Voltron Data Team. We wanted to improve the overall Velox user experience by making Velox easy to consume across a wide range of platforms to accelerate its adoption.

We choose hermetic builds as a solution to the aforementioned problems, as they provide a number of benefits. Hermetic builds¹ improve reproducibility by providing isolation from the state of the host machine and produce the same result for any given commit in the Velox repository. This requires precise dependency management.

The first major step in moving towards hermetic builds was the integration of a new dependency management system that is able to download, configure and build the necessary dependencies within the Velox build process. This new system also gives users the option to use already installed system dependencies. We hope this work will increase adoption of Velox in downstream projects and make troubleshooting of build issues easier, as well as improve overall reliability and stability.

We also wanted to lower the barrier to entry for contributions to Velox. Therefore, we created Docker Development images for both Ubuntu and CentOS, and we now publish them automatically when changes are merged. We hope this work will help speed up the development process by allowing developers to stand up a development environment quickly, without the requirement of installing third-party dependencies locally. We also use these images in the Velox CI to lower build times and speed up the feedback loop for proposing a PR.

# Run the development image from the root of the Velox repository
# to build and test Velox
docker compose run --rm ubuntu-cpp

An important non-technical improvement is the introduction of new issue templates and utility scripts. These will help guide troubleshooting and getting support from the relevant Velox developers via Github. This helps to improve the experience for the community and make it easier for users and contributors to get help and support when they need it.

Lastly, we implemented new nightly builds to increase the overall reliability and stability of Velox, as well as test the integration with downstream community projects.

To enable easy access to Velox from Python, we built CI infrastructure to generate and publish pre-built binary wheels for PyVelox (the Velox Python Bindings). We also improved Conda support by contributing to upstream feedstocks.

# Try PyVelox today!
pip install pyvelox

Future Goals​

We will continue the work of moving all dependencies to the new dependency management system to move closer to hermetic builds and make development and usage as smooth as possible.

In the same theme, the next major goal is the refactoring of the existing CMake build system to use a target based "modern" style. This will allow us to properly install Velox as a system library to be used by other projects. This project will improve the development experience overall by creating a stable, reliable build infrastructure, but also allows us to publish Velox as a conda-forge package and make it easier to further improve support for non x86_64 architectures like Apple Silicon, arm64 systems, various compilers and older CPUs that don’t support the currently obligatory instructions sets like BMI2 and make Velox available to an even larger community.

Confidence in the stability and reliability of a project are key when you want to deploy it as part of your stack. Therefore, we are working on a release process and versioning scheme for Velox so that you can deploy with confidence!

In conclusion, the collaboration between Velox and Voltron Data has led to several key improvements in Velox's packaging and CI. Setting up a new environment with Velox has never been this easy! With the new improvements, this new broader community of developers and contributors can expect a smoother and more user-friendly experience when using Velox. The Velox team is continuously working towards further improving the developer and user experience, and we invite you to join us in building the next generation unified execution engine!

This blogpost is part of a series of blog posts that discuss different features and optimizations of the simple function interface in Velox.

Introduction to Simple Functions​

Scalar functions are one of the most used extension points in Velox. Since Velox is a vectorized engine, by nature functions are "vector functions" that consume Velox vectors (batches of data) and produce vectors. Velox allows users to write functions as vector functions or as single-row operations "simple functions" that are converted to vector functions using template expansion through SimpleFunctionAdapter.

Writing functions as vector functions directly gives the user complete control over the function implementations and optimizations, however it comes with some cost that can be summarized in three points:

• Complexity : Requires an understanding of Velox vectorized data representation and encodings, which requires additional work for our customers, specially those without DB background. Moreover, Writing optimized vector functions requires even deeper understanding.
• Repetition : Involves repeated efforts and code; in each function, authors have to decode the input vectors, apply the same optimizations, and build the output vectors. For example, most arithmetic functions need benefits from a fast path when all the inputs are flat-encoded, authors need to implement that for every function that benefits from it.
• Reliability : More code means more bugs, especially in such a complex context.

Writing functions through the simple interface mitigates the previously mentioned drawbacks, and significantly simplifies the function authoring process. For example, to add the function plus the user only needs to implement the PlusFunction struct shown in the graph above , which is then expanded using template expansion to a vector function.

However, the simple function interface does not give the user full control over the authoring and has its own limitations, for example the function map_keys can be implemented in O(1) as a vector function by moving the keys vector; this is not possible to express as a simple function.

Another limitation is that when using the simple interface, authors do not have access to the encodings of the input vectors, nor control over the encoding of the result vector. Hence, do not have the power to optimize the code for specific input encodings or optimize it by generating specific output encodings. The array_sort function for instance does not need to re-order the elements and copy them during sorting; instead it can generate a dictionary vector as an output, which is something not expressible as a simple function.

In the ideal world we would like to add most of the optimization that someone can do in a vector function to the simple functions adapter, so it would be enabled automatically. We have identified a number of optimizations that apply to all functions and implemented these generically in the SimpleFunctionAdapter. In this way, we can achieve the best of the two worlds and gain Simplicity, Efficiency and Reliability for most functions.

In the past year, we have been working on several improvements to the simple function interface on both the expressivity and performance axes that we will discuss in this series of notes.

In this blog post, we will talk about some of the general optimizations that we have in the adapter, the optimizations discussed in this post make the performance of most simple functions that operates on primitive types matches their counter optimized vector function implementations. In the next blog post, we will discuss complex types in simple functions.

General Optimizations​

Vector Reuse​

If the output type matches one of the input types, and the input vector is to die after the function invocation, then it is possible to reuse it for the results instead of allocating a new vector. For example, in the expression plus(a, b), if a is stored in a flat vector that is not used after the invocation of the plus function, then that vevtor can be used to store the reults of the computation instead of allocating a new vevtor for the results.

Bulk Null Setting​

Nulls are represented in a bit vector, hence, writing each bit can be expensive specially for primitive operations (like plus and minus). One optimization is to optimize for the not null case, and bulk setting the nulls to not null. After that during the computation, only if the results are null, the null bit is set to null.

Null Setting Avoidance​

The adapter can statically infer if a function never generates null; In the simple function interface if the call function return's type is void, it means the output is never null, and if it's bool, then the function returns true for not null and false for null).

When the function does not generate nulls, then null setting is completely avoided during the computation (only the previous bulk setting is needed). The consequence of that is that the hot loop applying the function becomes simdizable triggering a huge boost in performance for primitive operations.

Worth to note also that if the simple function happens to be inlined in the adapter, then even if its return type is not void, but it always returns true then the compiler will be able to infer that setting nulls is never executed and would remove the null setting code.

Encoding Based Fast Path​

Vectors in Velox can have different encodings (flat, constant..etc). The generic way of reading a vector of arbitrary encoding is to use a decoded vector to guarantee correct data access. Even though decoded vectors provide a consistent API and make it easier to handle arbitrarily encoded input data, they translate into an overhead each time an input value is accessed (we need to check the encoding of the vector to know how to read the value for every row).

When the function is a primitive operation like plus or minus, such overhead is expensive! To avoid that, encoding based fast paths can be added, the code snippet below illustrates the idea.

In the code above, the overhead of checking the encoding is switched outside the loop that applies the functions (the plus operation here). And the inner loops are simple operations that are potentially simdizable and free of encoding checks. One issue with this optimization is that the core loop is replicated many times. In general, the numbers of times it will be replicated is n^m where n is the number of args, and m is the number of encodings.

To avoid code size blowing, we only apply this optimization when all input arguments are primitives and the number of input arguments is <=3. The figure below shows the effect of this optimization on the processing time of a query of primitive operations (the expression is a common pattern in ML use cases).

To compromise for both (performance and code size) when the conditions for specializing for all encodings are not met, we have a pseudo specialization mode that does not blow up the code size, but still reduce the overhead of decoding to a single multiplication per argument. This mode is enabled when all the primitive arguments are either flat or constant. The code below illustrates the idea:

When the input vector is constant we can read the value always from index 0 of the values buffer, and when it is flat we can read it from the index row; this can be achieved by assigning a factor to either 0 or 1 and reducing the decoding operation per row into a multiplication with that factor Note that such a multiplication does not prevent simd. The graph above shows that the psudeo specialization makes the program 1.6X fatser wi, while the complete specialization makes the program 2.5X faster.

ASCII Fast Path​

Functions with string inputs can be optimized when the inputs are known to be ascii. For example the length function for ascii strings is the size of the StringView O(1). But for non-ascii inputs the computation is a more complicated O(n) operation. Users can define a function callAscii() that will be called when all the string input arguments are ascii.

Zero-Copy Optimization​

When an input string (or portion of it, reaches the output as is) it does not need to be deep copied. Instead only a StringView needs to be set. Substring is an example of a function that benefits from this. This can be done in the simple function interface in two simple steps.

1. Using setNoCopy(); to set the output results without copying string vectors.
2. Inform the function to make the output vector share ownership of input string buffers, this can be by setting the field reuse_strings_from_arg.

The graph below shows the effect of the previous two optimizations on the performance of the substring function.

Constant Inputs Pre-processing​

Users can pre-process constant inputs of functions to avoid repeated computation by defining initialize function which is called once during query compilations and receives the constant inputs. For example, a regex function with constant pattern would only needs to compile the pattern expressions only once when its constant.

For more information about how to write simple functions check the documentation and the examples.