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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 ingestions and exports into timely dataflow
//!
//! ## Ingestions
//!
//! ### Overall structure
//!
//! Before describing any of the timely operators involved in ingesting a source it helps to
//! understand the high level structure of the timely scopes involved. The reason for this
//! structure is the fact that we ingest external sources with a source-specific, and source
//! implementation defined, timestamp type which tracks progress in a way that the source
//! implementation understands. Each source specific timestamp must be compatible with timely's
//! `timely::progress::Timestamp` trait and so it's suitable to represent timely streams and by
//! extension differential collections.
//!
//! On the other hand, Materialize expects a specific timestamp type for all its collections
//! (currently `mz_repr::Timestamp`) so at some point the dataflow's timestamp must change. More
//! generally, the ingestion dataflow starts with some timestamp type `FromTime` and ends with
//! another timestamp type `IntoTime`.
//!
//! Here we run into a problem though because we want to start with a timely stream of type
//! `Stream<G1: Scope<Timestamp=FromTime>, ..>` and end up using it in a scope `G2` whose timestamp
//! type is `IntoTime`. Timely dataflows are organized in scopes where each scope has an associated
//! timestamp type that must refine the timestamp type of its parent scope. What "refines" means is
//! defined by the [`timely::progress::timestamp::Refines`] trait in timely. `FromTime` however
//! does not refine `IntoTime` nor does `IntoTime` refine `FromTime`.
//!
//! In order to acomplish this we split ingestion dataflows in two scopes, both of which are
//! children of the root timely scope. The first scope is timestamped with `FromTime` and the
//! second one with `IntoTime`. To move timely streams from the one scope to the other we must do
//! so manually. Each stream that needs to be transferred between scopes is first captured using
//! [`timely::dataflow::operators::capture::capture::Capture`] into a tokio unbounded mpsc channel.
//! The data in the channel record in full detail the worker-local view of the original stream and
//! whoever controls the receiver can read in the events, in the standard way of consuming the
//! async channel, and work with it. How the receiver is turned back into a timely stream in the
//! destination scope is described in the next section.
//!
//! For now keep in mind the general structure of the dataflow:
//!
//!
//! ```text
//! +----------------RootScope(Timestamp=())------------------+
//! | |
//! | +---FromTime Scope---+ +---IntoTime Scope--+ | |
//! | | | | | |
//! | | *--+---------+--> | |
//! | | | | | |
//! | | <--+---------+--* | |
//! | +--------------------+ ^ +-------------------+ |
//! | | |
//! | | |
//! | data exchanged between |
//! | scopes with capture/reclock |
//! +---------------------------------------------------------+
//! ```
//!
//! ### Detailed dataflow
//!
//! We are now ready to describe the detailed structure of the ingestion dataflow. The dataflow
//! begins with the `source reader` dataflow fragment which is rendered in a `FromTime` timely
//! scope. This scope's timestamp is controlled by the [`crate::source::types::SourceRender::Time`]
//! associated type and can be anything the source implementation desires.
//!
//! Each source is free to render any arbitrary dataflow fragment in that scope as long as it
//! produces the collections expected by the rest of the framework. The rendering is handled by the
//! `[crate::source::types::SourceRender::render] method.
//!
//! When rendering a source dataflow we expect three outputs. First, a health output, which is how
//! the source communicates status updates about its health. Second, a data output, which is the
//! main output of a source and contains the data that will eventually be recorded in the persist
//! shard. Finally, an optional upper frontier output, which tracks the overall upstream upper
//! frontier. When a source doesn't provide a dedicated progress output the framework derives one
//! by observing the progress of the data output. This output (derived or not) is what drives
//! reclocking. When a source provides a dedicated upper output, it can manage it independently of
//! the data output frontier. For example, it's possible that a source implementation queries the
//! upstream system to learn what are the latest offsets for and set the upper output based on
//! that, even before having started the actual ingestion, which would be presented as data and
//! progress trickling in via the data output.
//!
//! ```text
//! resume upper
//! ,--------------------.
//! / |
//! health ,----+---. |
//! output | source | |
//! ,-----------| reader | |
//! / +--,---.-+ |
//! / / \ |
//! +-----/----+ data / \ upper |
//! | health | output/ \ output |
//! | operator | | \ |
//! +----------+ | | |
//! FromTime | | |
//! scope | | |
//! -------------------------------------|-----------|---------------|---
//! IntoTime | | |
//! scope | ,----+-----. |
//! | | remap | |
//! | | operator | |
//! | +---,------+ |
//! | / |
//! | / bindings |
//! | / |
//! ,-+-----+--. |
//! | reclock | |
//! | operator | |
//! +-,--,---.-+ |
//! ,----------´.-´ \ |
//! _.-´ .-´ \ |
//! _.-´ .-´ \ |
//! .-´ ,´ \ |
//! / / \ |
//! ,----------. ,----------. ,----------. |
//! | decode | | decode | .... | decode | |
//! | output 0 | | output 1 | | output N | |
//! +-----+----+ +-----+----+ +-----+----+ |
//! | | | |
//! | | | |
//! ,-----+----. ,-----+----. ,-----+----. |
//! | envelope | | envelope | .... | envelope | |
//! | output 0 | | output 1 | | output N | |
//! +----------+ +-----+----+ +-----+----+ |
//! | | | |
//! | | | |
//! ,-----+----. ,-----+----. ,-----+----. |
//! | persist | | persist | .... | persist | |
//! | sink 0 | | sink 1 | | sink N | |
//! +-----+----+ +-----+----+ +-----+----+ |
//! \ \ / |
//! `-. `, / |
//! `-._ `-. / |
//! `-._ `-. / |
//! `---------. `-. / |
//! +`---`---+---, |
//! | resume | |
//! | calculator | |
//! +------+-----+ |
//! \ |
//! `-------------------´
//! ```
//!
//! #### Reclocking
//!
//! Whenever a dataflow edge crosses the scope boundaries it must first be converted into a
//! captured stream via the `[mz_timely_util::capture::UnboundedTokioCapture`] utility. This
//! disassociates the stream and its progress information from the original timely scope and allows
//! it to be read from a different place. The downside of this mechanism is that it's invisible to
//! timely's progress tracking, but that seems like a necessary evil if we want to do reclocking.
//!
//! The two main ways these tokio-fied streams are turned back into normal timely streams in the
//! destination scope are by the `reclock operator` and the `remap operator` which process the
//! `data output` and `upper output` of the source reader respectively.
//!
//! The `remap operator` reads the `upper output`, which is composed only of frontiers, mints new
//! bindings, and writes them into the remap shard. The final durable timestamp bindings are
//! emitted as its output for consumption by the `reclock operator`.
//!
//! The `reclock operator` reads the `data output`, which contains both data and progress
//! statements, and uses the bindings it receives from the `remap operator` to reclock each piece
//! of data and each frontier statement into the target scope's timestamp and emit the reclocked
//! stream in its output.
//!
//! #### Partitioning
//!
//! At this point we have a timely stream with correctly timestamped data in the mz time domain
//! (`mz_repr::Timestamp`) which contains multiplexed messages for each of the potential subsources
//! of this source. Each message selects the output it belongs to by setting the output field in
//! [`crate::source::types::SourceMessage`]. By convention, the main source output is always output
//! zero and subsources get the outputs from one onwards.
//!
//! However, regardless of whether the output is the main source or a subsource it is treated
//! identically by the pipeline. Each output is demultiplexed into its own timely stream using
//! [`timely::dataflow::operators::partition::Partition`] and the rest of the ingestion pipeline is
//! rendered independently.
//!
//! #### Resumption frontier
//!
//! At the end of each per-output dataflow fragment is an instance of `persist_sink`, which is
//! responsible for writing the final `Row` data into the corresponding output shard. The durable
//! upper of each of the output shards is then recombined in a way that calculates the minimum
//! upper frontier between them. This is what we refer to as the "resumption frontier" or "resume
//! upper" and at this stage it is expressed in terms of `IntoTime` timestamps. As a final step,
//! this resumption frontier is converted back into a `FromTime` timestamped frontier using
//! `ReclockFollower::source_upper_at_frontier` and connected back to the source reader operator.
//! This frontier is what drives the `OffsetCommiter` which informs the upstream system to release
//! resources until the specified offsets.
//!
//! ## Exports
//!
//! Not yet documented
use std::collections::BTreeMap;
use std::rc::Rc;
use std::sync::Arc;
use mz_ore::error::ErrorExt;
use mz_repr::{GlobalId, Row};
use mz_storage_types::controller::CollectionMetadata;
use mz_storage_types::dyncfgs;
use mz_storage_types::sinks::{MetadataFilled, StorageSinkDesc};
use mz_storage_types::sources::{GenericSourceConnection, IngestionDescription, SourceConnection};
use mz_timely_util::antichain::AntichainExt;
use timely::communication::Allocate;
use timely::dataflow::operators::{Concatenate, ConnectLoop, Feedback, Leave, Map};
use timely::dataflow::Scope;
use timely::progress::Antichain;
use timely::worker::Worker as TimelyWorker;
use tokio::sync::Semaphore;
use crate::healthcheck::{HealthStatusMessage, HealthStatusUpdate, StatusNamespace};
use crate::source::RawSourceCreationConfig;
use crate::storage_state::StorageState;
mod persist_sink;
pub mod sinks;
pub mod sources;
/// Assemble the "ingestion" 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_ingestion_dataflow<A: Allocate>(
timely_worker: &mut TimelyWorker<A>,
storage_state: &mut StorageState,
primary_source_id: GlobalId,
description: IngestionDescription<CollectionMetadata>,
as_of: Antichain<mz_repr::Timestamp>,
resume_uppers: BTreeMap<GlobalId, Antichain<mz_repr::Timestamp>>,
source_resume_uppers: BTreeMap<GlobalId, Vec<Row>>,
) {
let worker_id = timely_worker.index();
let worker_logging = timely_worker.log_register().get("timely");
let debug_name = primary_source_id.to_string();
let name = format!("Source dataflow: {debug_name}");
timely_worker.dataflow_core(&name, worker_logging, Box::new(()), |_, root_scope| {
// Here we need to create two scopes. One timestamped with `()`, which is the root scope,
// and one timestamped with `mz_repr::Timestamp` which is the final scope of the dataflow.
// Refer to the module documentation for an explanation of this structure.
// The scope.clone() occurs to allow import in the region.
root_scope.clone().scoped(&name, |mz_scope| {
let debug_name = format!("{debug_name}-sources");
let mut tokens = vec![];
let (feedback_handle, feedback) = mz_scope.feedback(Default::default());
let connection = description.desc.connection.clone();
tracing::info!(
id = %primary_source_id,
as_of = %as_of.pretty(),
resume_uppers = ?resume_uppers,
source_resume_uppers = ?source_resume_uppers,
"timely-{worker_id} building {} source pipeline", connection.name(),
);
let busy_signal = if dyncfgs::SUSPENDABLE_SOURCES
.get(storage_state.storage_configuration.config_set())
{
Arc::new(Semaphore::new(1))
} else {
Arc::new(Semaphore::new(Semaphore::MAX_PERMITS))
};
let base_source_config = RawSourceCreationConfig {
name: format!("{}-{}", connection.name(), primary_source_id),
id: primary_source_id,
source_exports: description.indexed_source_exports(&primary_source_id),
timestamp_interval: description.desc.timestamp_interval,
worker_id: mz_scope.index(),
worker_count: mz_scope.peers(),
now_fn: storage_state.now.clone(),
metrics: storage_state.metrics.clone(),
as_of: as_of.clone(),
resume_uppers: resume_uppers.clone(),
source_resume_uppers,
storage_metadata: description.ingestion_metadata.clone(),
persist_clients: Arc::clone(&storage_state.persist_clients),
source_statistics: storage_state
.aggregated_statistics
.get_source(&primary_source_id)
.expect("statistics initialized")
.clone(),
shared_remap_upper: Rc::clone(
&storage_state.source_uppers[&description.remap_collection_id],
),
// This might quite a large clone, but its just during rendering
config: storage_state.storage_configuration.clone(),
remap_collection_id: description.remap_collection_id.clone(),
busy_signal: Arc::clone(&busy_signal),
};
let (mut outputs, source_health, source_tokens) = match connection {
GenericSourceConnection::Kafka(c) => crate::render::sources::render_source(
mz_scope,
&debug_name,
c,
description.clone(),
&feedback,
storage_state,
base_source_config,
),
GenericSourceConnection::Postgres(c) => crate::render::sources::render_source(
mz_scope,
&debug_name,
c,
description.clone(),
&feedback,
storage_state,
base_source_config,
),
GenericSourceConnection::MySql(c) => crate::render::sources::render_source(
mz_scope,
&debug_name,
c,
description.clone(),
&feedback,
storage_state,
base_source_config,
),
GenericSourceConnection::LoadGenerator(c) => crate::render::sources::render_source(
mz_scope,
&debug_name,
c,
description.clone(),
&feedback,
storage_state,
base_source_config,
),
};
tokens.extend(source_tokens);
let mut health_configs = BTreeMap::new();
let mut upper_streams = vec![];
let mut health_streams = vec![source_health];
let source_exports = description.indexed_source_exports(&primary_source_id);
for (export_id, export) in source_exports {
let (ok, err) = outputs
.get_mut(export.ingestion_output)
.expect("known to exist");
let source_data = ok.map(Ok).concat(&err.map(Err));
let metrics = storage_state.metrics.get_source_persist_sink_metrics(
export_id,
primary_source_id,
worker_id,
&export.export.storage_metadata.data_shard,
export.ingestion_output,
);
tracing::info!(
id = %primary_source_id,
"timely-{worker_id}: persisting export #{} of {} into {}",
export.ingestion_output,
primary_source_id,
export_id
);
let (upper_stream, errors, sink_tokens) = crate::render::persist_sink::render(
mz_scope,
export_id,
export.export.storage_metadata.clone(),
source_data,
storage_state,
metrics,
export.ingestion_output,
Arc::clone(&busy_signal),
);
upper_streams.push(upper_stream);
tokens.extend(sink_tokens);
let sink_health = errors.map(|err: Rc<anyhow::Error>| {
let halt_status =
HealthStatusUpdate::halting(err.display_with_causes().to_string(), None);
HealthStatusMessage {
index: 0,
namespace: StatusNamespace::Internal,
update: halt_status,
}
});
health_streams.push(sink_health.leave());
health_configs.insert(export.ingestion_output, export_id);
}
mz_scope
.concatenate(upper_streams)
.connect_loop(feedback_handle);
let health_stream = root_scope.concatenate(health_streams);
let health_token = crate::healthcheck::health_operator(
mz_scope,
storage_state.now.clone(),
resume_uppers
.iter()
.filter_map(|(id, frontier)| {
// If the collection isn't closed, then we will remark it as Starting as
// the dataflow comes up.
(!frontier.is_empty()).then_some(*id)
})
.collect(),
primary_source_id,
"source",
&health_stream,
health_configs,
crate::healthcheck::DefaultWriter {
command_tx: Rc::clone(&storage_state.internal_cmd_tx),
updates: Rc::clone(&storage_state.object_status_updates),
},
storage_state
.storage_configuration
.parameters
.record_namespaced_errors,
dyncfgs::STORAGE_SUSPEND_AND_RESTART_DELAY
.get(storage_state.storage_configuration.config_set()),
);
tokens.push(health_token);
storage_state
.source_tokens
.insert(primary_source_id, tokens);
})
});
}
/// do the export dataflow thing
pub fn build_export_dataflow<A: Allocate>(
timely_worker: &mut TimelyWorker<A>,
storage_state: &mut StorageState,
id: GlobalId,
description: StorageSinkDesc<MetadataFilled, mz_repr::Timestamp>,
) {
let worker_logging = timely_worker.log_register().get("timely");
let debug_name = id.to_string();
let name = format!("Source dataflow: {debug_name}");
timely_worker.dataflow_core(&name, worker_logging, Box::new(()), |_, root_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.
root_scope.clone().scoped(&name, |scope| {
let _debug_name = format!("{debug_name}-sinks");
let _: &mut timely::dataflow::scopes::Child<
timely::dataflow::scopes::Child<TimelyWorker<A>, _>,
mz_repr::Timestamp,
> = scope;
let mut tokens = vec![];
let (health_stream, sink_tokens) =
crate::render::sinks::render_sink(scope, storage_state, id, &description);
tokens.extend(sink_tokens);
let mut health_configs = BTreeMap::new();
health_configs.insert(
// There is only 1 sink (as opposed to many sub-sources), so we just use a single
// index.
0, id,
);
// Note that sinks also have only 1 active worker, which simplifies the work that
// `health_operator` has to do internally.
let health_token = crate::healthcheck::health_operator(
scope,
storage_state.now.clone(),
[id].into_iter().collect(),
id,
"sink",
&health_stream,
health_configs,
crate::healthcheck::DefaultWriter {
command_tx: Rc::clone(&storage_state.internal_cmd_tx),
updates: Rc::clone(&storage_state.object_status_updates),
},
storage_state
.storage_configuration
.parameters
.record_namespaced_errors,
dyncfgs::STORAGE_SUSPEND_AND_RESTART_DELAY
.get(storage_state.storage_configuration.config_set()),
);
tokens.push(health_token);
storage_state.sink_tokens.insert(id, tokens);
})
});
}