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// Copyright Materialize, Inc. and contributors. All rights reserved.
//
// Use of this software is governed by the Business Source License
// included in the LICENSE file.
//
// As of the Change Date specified in that file, in accordance with
// the Business Source License, use of this software will be governed
// by the Apache License, Version 2.0.
//! Renders a plan into a timely/differential dataflow computation.
//!
//! ## Error handling
//!
//! Timely and differential have no idioms for computations that can error. The
//! philosophy is, reasonably, to define the semantics of the computation such
//! that errors are unnecessary: e.g., by using wrap-around semantics for
//! integer overflow.
//!
//! Unfortunately, SQL semantics are not nearly so elegant, and require errors
//! in myriad cases. The classic example is a division by zero, but invalid
//! input for casts, overflowing integer operations, and dozens of other
//! functions need the ability to produce errors ar runtime.
//!
//! At the moment, only *scalar* expression evaluation can fail, so only
//! operators that evaluate scalar expressions can fail. At the time of writing,
//! that includes map, filter, reduce, and join operators. Constants are a bit
//! of a special case: they can be either a constant vector of rows *or* a
//! constant, singular error.
//!
//! The approach taken is to build two parallel trees of computation: one for
//! the rows that have been successfully evaluated (the "oks tree"), and one for
//! the errors that have been generated (the "errs tree"). For example:
//!
//! ```text
//! oks1 errs1 oks2 errs2
//! | | | |
//! | | | |
//! project | | |
//! | | | |
//! | | | |
//! map | | |
//! |\ | | |
//! | \ | | |
//! | \ | | |
//! | \ | | |
//! | \| | |
//! project + + +
//! | | / /
//! | | / /
//! join ------------+ /
//! | | /
//! | | +----------+
//! | |/
//! oks errs
//! ```
//!
//! The project operation cannot fail, so errors from errs1 are propagated
//! directly. Map operators are fallible and so can inject additional errors
//! into the stream. Join operators combine the errors from each of their
//! inputs.
//!
//! The semantics of the error stream are minimal. From the perspective of SQL,
//! a dataflow is considered to be in an error state if there is at least one
//! element in the final errs collection. The error value returned to the user
//! is selected arbitrarily; SQL only makes provisions to return one error to
//! the user at a time. There are plans to make the err collection accessible to
//! end users, so they can see all errors at once.
//!
//! To make errors transient, simply ensure that the operator can retract any
//! produced errors when corrected data arrives. To make errors permanent, write
//! the operator such that it never retracts the errors it produced. Future work
//! will likely want to introduce some sort of sort order for errors, so that
//! permanent errors are returned to the user ahead of transient errors—probably
//! by introducing a new error type a la:
//!
//! ```no_run
//! # struct EvalError;
//! # struct SourceError;
//! enum DataflowError {
//! Transient(EvalError),
//! Permanent(SourceError),
//! }
//! ```
//!
//! If the error stream is empty, the oks stream must be correct. If the error
//! stream is non-empty, then there are no semantics for the oks stream. This is
//! sufficient to support SQL in its current form, but is likely to be
//! unsatisfactory long term. We suspect that we can continue to imbue the oks
//! stream with semantics if we are very careful in describing what data should
//! and should not be produced upon encountering an error. Roughly speaking, the
//! oks stream could represent the correct result of the computation where all
//! rows that caused an error have been pruned from the stream. There are
//! strange and confusing questions here around foreign keys, though: what if
//! the optimizer proves that a particular key must exist in a collection, but
//! the key gets pruned away because its row participated in a scalar expression
//! evaluation that errored?
//!
//! In the meantime, it is probably wise for operators to keep the oks stream
//! roughly "as correct as possible" even when errors are present in the errs
//! stream. This reduces the amount of recomputation that must be performed
//! if/when the errors are retracted.
use std::any::Any;
use std::cell::RefCell;
use std::collections::{BTreeMap, HashMap, HashSet};
use std::rc::Rc;
use std::rc::Weak;
use std::time::Duration;
use differential_dataflow::AsCollection;
use timely::communication::Allocate;
use timely::dataflow::operators::capture::EventLink;
use timely::dataflow::operators::to_stream::ToStream;
use timely::dataflow::operators::Capture;
use timely::dataflow::scopes::Child;
use timely::dataflow::Scope;
use timely::progress::Antichain;
use timely::worker::Worker as TimelyWorker;
use crate::activator::RcActivator;
use dataflow_types::*;
use expr::{GlobalId, Id};
use itertools::Itertools;
use ore::collections::CollectionExt as _;
use ore::now::NowFn;
use persist::client::RuntimeClient;
use repr::{Row, Timestamp};
use crate::arrangement::manager::{TraceBundle, TraceManager};
use crate::event::ActivatedEventPusher;
use crate::metrics::Metrics;
use crate::render::context::CollectionBundle;
use crate::render::context::{ArrangementFlavor, Context};
use crate::render::sources::PersistedSourceManager;
use crate::replay::MzReplay;
use crate::server::LocalInput;
use crate::sink::SinkBaseMetrics;
use crate::source::metrics::SourceBaseMetrics;
use crate::source::timestamp::TimestampBindingRc;
use crate::source::SourceToken;
mod context;
mod debezium;
mod envelope_none;
mod flat_map;
mod join;
mod reduce;
pub mod sinks;
pub mod sources;
mod threshold;
mod top_k;
mod upsert;
/// Worker-local state that is maintained across dataflows.
///
/// This state is restricted to the COMPUTE state, the deterministic, idempotent work
/// done between data ingress and egress.
pub struct ComputeState {
/// The traces available for sharing across dataflows.
pub traces: TraceManager,
/// Tokens that should be dropped when a dataflow is dropped to clean up
/// associated state.
pub dataflow_tokens: HashMap<GlobalId, Box<dyn Any>>,
/// Shared buffer with TAIL operator instances by which they can respond.
///
/// The entries are pairs of sink identifier (to identify the tail instance)
/// and the response itself.
pub tail_response_buffer: Rc<RefCell<Vec<(GlobalId, TailResponse)>>>,
/// Frontier of sink writes (all subsequent writes will be at times at or
/// equal to this frontier)
pub sink_write_frontiers: HashMap<GlobalId, Rc<RefCell<Antichain<Timestamp>>>>,
}
/// Worker-local state related to the ingress or egress of collections of data.
pub struct StorageState {
/// Handles to local inputs, keyed by ID.
pub local_inputs: HashMap<GlobalId, LocalInput>,
/// Source descriptions that have been created and not yet dropped.
///
/// For the moment we retain all source descriptions, even those that have been
/// dropped, as this is used to check for rebinding of previous identifiers.
/// Once we have a better mechanism to avoid that, for example that identifiers
/// must strictly increase, we can clean up descriptions when sources are dropped.
pub source_descriptions: HashMap<GlobalId, dataflow_types::sources::SourceDesc>,
/// Handles to external sources, keyed by ID.
pub ts_source_mapping: HashMap<GlobalId, Vec<Weak<Option<SourceToken>>>>,
/// Timestamp data updates for each source.
pub ts_histories: HashMap<GlobalId, TimestampBindingRc>,
/// Handles that allow setting the compaction frontier for a persisted source. There can only
/// ever be one running (rendered) source of a persisted source, and if there is one, this map
/// will contain a handle to it.
pub persisted_sources: PersistedSourceManager,
/// Metrics reported by all dataflows.
pub metrics: Metrics,
/// Handle to the persistence runtime. None if disabled.
pub persist: Option<RuntimeClient>,
}
/// Information about each source that must be communicated between storage and compute layers.
pub struct SourceBoundary {
/// Captured `row` updates representing a differential collection.
pub ok:
ActivatedEventPusher<Rc<EventLink<repr::Timestamp, (Row, repr::Timestamp, repr::Diff)>>>,
/// Captured error updates representing a differential collection.
pub err: ActivatedEventPusher<
Rc<EventLink<repr::Timestamp, (DataflowError, repr::Timestamp, repr::Diff)>>,
>,
/// A token that should be dropped to terminate the source.
pub token: Rc<dyn std::any::Any>,
}
/// Assemble the "storage" side of a dataflow, i.e. the sources.
///
/// This method creates a new dataflow to host the implementations of sources for the `dataflow`
/// argument, and returns assets for each source that can import the results into a new dataflow.
pub fn build_storage_dataflow<A: Allocate>(
timely_worker: &mut TimelyWorker<A>,
storage_state: &mut StorageState,
dataflow: &DataflowDescription<plan::Plan>,
now: NowFn,
source_metrics: &SourceBaseMetrics,
) -> BTreeMap<GlobalId, SourceBoundary> {
let worker_logging = timely_worker.log_register().get("timely");
let name = format!("Dataflow: {}", &dataflow.debug_name);
let materialized_logging = timely_worker.log_register().get("materialized");
let mut results = BTreeMap::new();
timely_worker.dataflow_core(&name, worker_logging, Box::new(()), |_, scope| {
// The scope.clone() occurs to allow import in the region.
// We build a region here to establish a pattern of a scope inside the dataflow,
// so that other similar uses (e.g. with iterative scopes) do not require weird
// alternate type signatures.
scope.clone().region_named(&name, |region| {
let as_of = dataflow.as_of.clone().unwrap();
let dataflow_id = scope.addr().into_element();
let debug_name = format!("{}-sources", dataflow.debug_name);
assert!(
!dataflow
.source_imports
.iter()
.map(|(id, _src)| id)
.duplicates()
.next()
.is_some(),
"computation of unique IDs assumes a source appears no more than once per dataflow"
);
// Import declared sources into the rendering context.
for (src_id, source) in &dataflow.source_imports {
let ((ok, err), token) = crate::render::sources::import_source(
&debug_name,
dataflow_id,
&as_of,
source.clone(),
storage_state,
region,
materialized_logging.clone(),
src_id.clone(),
now.clone(),
source_metrics,
);
let ok_activator = RcActivator::new(format!("{debug_name}-ok"), 1);
let err_activator = RcActivator::new(format!("{debug_name}-err"), 1);
let ok_handle =
ActivatedEventPusher::new(Rc::new(EventLink::new()), ok_activator.clone());
let err_handle =
ActivatedEventPusher::new(Rc::new(EventLink::new()), err_activator.clone());
results.insert(
*src_id,
SourceBoundary {
ok: ActivatedEventPusher::<_>::clone(&ok_handle),
err: ActivatedEventPusher::<_>::clone(&err_handle),
token,
},
);
ok.inner.capture_into(ok_handle);
err.inner.capture_into(err_handle);
}
})
});
results
}
/// Assemble the "compute" side of a dataflow, i.e. all but the sources.
///
/// This method imports sources from provided assets, and then builds the remaining
/// dataflow using "compute-local" assets like shared arrangements, and producing
/// both arrangements and sinks.
pub fn build_compute_dataflow<A: Allocate>(
timely_worker: &mut TimelyWorker<A>,
compute_state: &mut ComputeState,
sources: BTreeMap<GlobalId, SourceBoundary>,
dataflow: DataflowDescription<plan::Plan>,
sink_metrics: &SinkBaseMetrics,
) {
let worker_logging = timely_worker.log_register().get("timely");
let name = format!("Dataflow: {}", &dataflow.debug_name);
timely_worker.dataflow_core(&name, worker_logging, Box::new(()), |_, scope| {
// The scope.clone() occurs to allow import in the region.
// We build a region here to establish a pattern of a scope inside the dataflow,
// so that other similar uses (e.g. with iterative scopes) do not require weird
// alternate type signatures.
scope.clone().region_named(&name, |region| {
let mut context = Context::for_dataflow(&dataflow, scope.addr().into_element());
let mut tokens = BTreeMap::new();
// Import declared sources into the rendering context.
for (source_id, source) in sources.into_iter() {
// Associate collection bundle with the source identifier.
let ok = Some(source.ok.inner)
.mz_replay(
region,
&format!("{name}-{source_id}"),
Duration::MAX,
source.ok.activator,
)
.as_collection();
let err = Some(source.err.inner)
.mz_replay(
region,
&format!("{name}-{source_id}-err"),
Duration::MAX,
source.err.activator,
)
.as_collection();
context.insert_id(
Id::Global(source_id),
CollectionBundle::from_collections(ok, err),
);
// Associate returned tokens with the source identifier.
let prior = tokens.insert(source_id, source.token);
assert!(prior.is_none());
}
// Import declared indexes into the rendering context.
for (idx_id, idx) in &dataflow.index_imports {
context.import_index(compute_state, &mut tokens, scope, region, *idx_id, &idx.0);
}
// We first determine indexes and sinks to export, then build the declared object, and
// finally export indexes and sinks. The reason for this is that we want to avoid
// cloning the dataflow plan for `build_object`, which can be expensive.
// Determine indexes to export
let indexes = dataflow
.index_exports
.iter()
.cloned()
.map(|(idx_id, idx, _typ)| (idx_id, dataflow.get_imports(&idx.on_id), idx))
.collect::<Vec<_>>();
// Determine sinks to export
let sinks = dataflow
.sink_exports
.iter()
.cloned()
.map(|(sink_id, sink)| (sink_id, dataflow.get_imports(&sink.from), sink))
.collect::<Vec<_>>();
// Build declared objects.
for object in dataflow.objects_to_build {
context.build_object(region, object);
}
// Export declared indexes.
for (idx_id, imports, idx) in indexes {
context.export_index(compute_state, &mut tokens, imports, idx_id, &idx);
}
// Export declared sinks.
for (sink_id, imports, sink) in sinks {
context.export_sink(
compute_state,
&mut tokens,
imports,
sink_id,
&sink,
sink_metrics,
);
}
});
})
}
impl<'g, G> Context<Child<'g, G, G::Timestamp>, Row, Timestamp>
where
G: Scope<Timestamp = Timestamp>,
{
fn import_index(
&mut self,
compute_state: &mut ComputeState,
tokens: &mut BTreeMap<GlobalId, Rc<dyn std::any::Any>>,
scope: &mut G,
region: &mut Child<'g, G, G::Timestamp>,
idx_id: GlobalId,
idx: &IndexDesc,
) {
if let Some(traces) = compute_state.traces.get_mut(&idx_id) {
let token = traces.to_drop().clone();
let (ok_arranged, ok_button) = traces.oks_mut().import_frontier_core(
scope,
&format!("Index({}, {:?})", idx.on_id, idx.key),
self.as_of_frontier.clone(),
);
let (err_arranged, err_button) = traces.errs_mut().import_frontier_core(
scope,
&format!("ErrIndex({}, {:?})", idx.on_id, idx.key),
self.as_of_frontier.clone(),
);
let ok_arranged = ok_arranged.enter(region);
let err_arranged = err_arranged.enter(region);
self.update_id(
Id::Global(idx.on_id),
CollectionBundle::from_expressions(
idx.key.clone(),
ArrangementFlavor::Trace(idx_id, ok_arranged, err_arranged),
),
);
tokens.insert(
idx_id,
Rc::new((ok_button.press_on_drop(), err_button.press_on_drop(), token)),
);
} else {
panic!(
"import of index {} failed while building dataflow {}",
idx_id, self.dataflow_id
);
}
}
fn build_object(
&mut self,
scope: &mut Child<'g, G, G::Timestamp>,
object: BuildDesc<plan::Plan>,
) {
// First, transform the relation expression into a render plan.
let bundle = self.render_plan(object.view, scope, scope.index());
self.insert_id(Id::Global(object.id), bundle);
}
fn export_index(
&mut self,
compute_state: &mut ComputeState,
tokens: &mut BTreeMap<GlobalId, Rc<dyn std::any::Any>>,
import_ids: HashSet<GlobalId>,
idx_id: GlobalId,
idx: &IndexDesc,
) {
// put together tokens that belong to the export
let mut needed_tokens = Vec::new();
for import_id in import_ids {
if let Some(token) = tokens.get(&import_id) {
needed_tokens.push(Rc::clone(&token));
}
}
let bundle = self.lookup_id(Id::Global(idx_id)).unwrap_or_else(|| {
panic!(
"Arrangement alarmingly absent! id: {:?}",
Id::Global(idx_id)
)
});
match bundle.arrangement(&idx.key) {
Some(ArrangementFlavor::Local(oks, errs)) => {
compute_state.traces.set(
idx_id,
TraceBundle::new(oks.trace, errs.trace).with_drop(needed_tokens),
);
}
Some(ArrangementFlavor::Trace(gid, _, _)) => {
// Duplicate of existing arrangement with id `gid`, so
// just create another handle to that arrangement.
let trace = compute_state.traces.get(&gid).unwrap().clone();
compute_state.traces.set(idx_id, trace);
}
None => {
println!("collection available: {:?}", bundle.collection.is_none());
println!(
"keys available: {:?}",
bundle.arranged.keys().collect::<Vec<_>>()
);
panic!(
"Arrangement alarmingly absent! id: {:?}, keys: {:?}",
Id::Global(idx_id),
&idx.key
);
}
};
}
}
impl<G> Context<G, Row, Timestamp>
where
G: Scope<Timestamp = Timestamp>,
{
/// Renders a plan to a differential dataflow, producing the collection of results.
///
/// The return type reflects the uncertainty about the data representation, perhaps
/// as a stream of data, perhaps as an arrangement, perhaps as a stream of batches.
pub fn render_plan(
&mut self,
plan: plan::Plan,
scope: &mut G,
worker_index: usize,
) -> CollectionBundle<G, Row, G::Timestamp> {
match plan {
Plan::Constant { rows } => {
// Produce both rows and errs to avoid conditional dataflow construction.
let (mut rows, errs) = match rows {
Ok(rows) => (rows, Vec::new()),
Err(e) => (Vec::new(), vec![e]),
};
// We should advance times in constant collections to start from `as_of`.
use differential_dataflow::lattice::Lattice;
for (_, time, _) in rows.iter_mut() {
time.advance_by(self.as_of_frontier.borrow());
}
let mut error_time: G::Timestamp = timely::progress::Timestamp::minimum();
error_time.advance_by(self.as_of_frontier.borrow());
let ok_collection = rows.into_iter().to_stream(scope).as_collection();
let err_collection = errs
.into_iter()
.map(move |e| (DataflowError::from(e), error_time, 1))
.to_stream(scope)
.as_collection();
CollectionBundle::from_collections(ok_collection, err_collection)
}
Plan::Get {
id,
keys,
mfp,
key_val,
} => {
// Recover the collection from `self` and then apply `mfp` to it.
// If `mfp` happens to be trivial, we can just return the collection.
let mut collection = self
.lookup_id(id)
.unwrap_or_else(|| panic!("Get({:?}) not found at render time", id));
if mfp.is_identity() {
// Assert that each of `keys` are present in `collection`.
assert!(keys
.arranged
.iter()
.all(|(key, _, _)| collection.arranged.contains_key(key)));
assert!(keys.raw <= collection.collection.is_some());
// Retain only those keys we want to import.
collection
.arranged
.retain(|key, _value| keys.arranged.iter().any(|(key2, _, _)| key2 == key));
collection
} else {
let (oks, errs) = collection.as_collection_core(mfp, key_val);
CollectionBundle::from_collections(oks, errs)
}
}
Plan::Let { id, value, body } => {
// Render `value` and bind it to `id`. Complain if this shadows an id.
let value = self.render_plan(*value, scope, worker_index);
let prebound = self.insert_id(Id::Local(id), value);
assert!(prebound.is_none());
let body = self.render_plan(*body, scope, worker_index);
self.remove_id(Id::Local(id));
body
}
Plan::Mfp {
input,
mfp,
input_key_val,
} => {
let input = self.render_plan(*input, scope, worker_index);
// If `mfp` is non-trivial, we should apply it and produce a collection.
if mfp.is_identity() {
input
} else {
let (oks, errs) = input.as_collection_core(mfp, input_key_val);
CollectionBundle::from_collections(oks, errs)
}
}
Plan::FlatMap {
input,
func,
exprs,
mfp,
input_key,
} => {
let input = self.render_plan(*input, scope, worker_index);
self.render_flat_map(input, func, exprs, mfp, input_key)
}
Plan::Join { inputs, plan } => {
let inputs = inputs
.into_iter()
.map(|input| self.render_plan(input, scope, worker_index))
.collect();
match plan {
dataflow_types::plan::join::JoinPlan::Linear(linear_plan) => {
self.render_join(inputs, linear_plan, scope)
}
dataflow_types::plan::join::JoinPlan::Delta(delta_plan) => {
self.render_delta_join(inputs, delta_plan, scope)
}
}
}
Plan::Reduce {
input,
key_val_plan,
plan,
input_key,
} => {
let input = self.render_plan(*input, scope, worker_index);
self.render_reduce(input, key_val_plan, plan, input_key)
}
Plan::TopK { input, top_k_plan } => {
let input = self.render_plan(*input, scope, worker_index);
self.render_topk(input, top_k_plan)
}
Plan::Negate { input } => {
let input = self.render_plan(*input, scope, worker_index);
let (oks, errs) = input.as_specific_collection(None);
CollectionBundle::from_collections(oks.negate(), errs)
}
Plan::Threshold {
input,
threshold_plan,
} => {
let input = self.render_plan(*input, scope, worker_index);
self.render_threshold(input, threshold_plan)
}
Plan::Union { inputs } => {
let mut oks = Vec::new();
let mut errs = Vec::new();
for input in inputs.into_iter() {
let (os, es) = self
.render_plan(input, scope, worker_index)
.as_specific_collection(None);
oks.push(os);
errs.push(es);
}
let oks = differential_dataflow::collection::concatenate(scope, oks);
let errs = differential_dataflow::collection::concatenate(scope, errs);
CollectionBundle::from_collections(oks, errs)
}
Plan::ArrangeBy {
input,
forms: keys,
input_key,
input_mfp,
} => {
let input = self.render_plan(*input, scope, worker_index);
input.ensure_collections(keys, input_key, input_mfp)
}
}
}
}