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v0.7.0

ArroyoSystems/arroyo

版本发布时间: 2023-10-18 00:36:00

ArroyoSystems/arroyo最新发布版本:v0.9.1(2024-02-03 05:33:18)

These release notes are also available on our blog.

The Arroyo team is excited to announce the release of Arroyo 0.7.0, the latest version of our open-source stream processing engine. This release includes a number of new features, including custom partitioning for the filesystem sink, message framing and unnest support, unions, state compaction, and more. Our focus on quality also continues, with a more sophisticated correctness test suite that can now test checkpointing and restoration.

Read on for more, and check out our docs for full details on existing and new features.

Thanks to all our contributors for this release:

What's next

With the 0.7 release out, we're already working on the next one. Arroyo 0.8 is targeted for mid-November, and will include a number of new features, including support for Avro, Delta Lake integration, a FileSystem source, saved UDFs, and more. We've also been working on a new distributed state backend, which will allow Arroyo to scale to multi-TB state sizes while maintaining fast restarts and frequently checkpoints.

Anything you'd like to see? Let us know on Discord!

Now on to the details.

Custom partitioning for FileSystem sink

Arroyo 0.5 added a high-performance, transactional filesystem sink which enables ingestion into data warehouses on S3. The initial version did not support custom partitioning of data, so records were written to a single file per subtask (with time and size-based rollovers).

In many cases, you will get better query performance if you partition your data by a field in your data (like an event type) or by time.

Arroyo 0.7 introduces support for field-based and time-based partitioning, allowing you to optimize your data layout according to your query patterns.

For example you can now create a table like this:

CREATE TABLE file_sink (
    time TIMESTAMP,
    event_type TEXT,
    user_id TEXT,
    count INT
) WITH (
    connector = 'filesystem',
    format = 'parquet',
    path = 's3://arroyo-events/realtime',
    rollover_seconds = '3600',
    time_partition_pattern = 'year=%Y/month=%m/day=%d/hour=%H',
    partition_fields = 'event_type'
);

This will write data to a path like s3://arroyo-events/realtime/year=2023/month=10/day=19/hour=10/event_type=login.

See all of the available options in the docs.

Message Framing

Arroyo now supports defining a framing strategy for messages. This allows users to customize how messages read off of a source are split into records for processing, where previously there was always a one-to-one mapping. This is particularly useful for sources which do not have a built-in framing strategy, such as HTTP APIs.

As an example, Arroyo can now directly consume metrics from a prometheus-compatible application—without using Prometheus! Here's a query that polls a prometheus endpoint (for a node exporter) and computes the CPU usage over a 1 minute sliding window:

create table raw_metrics (
    value TEXT,
    parsed TEXT generated always as (parse_prom(value))
) WITH (
    connector = 'polling_http',
    endpoint = 'http://localhost:9100/metrics',
    format = 'raw_string',
    framing = 'newline',
    emit_behavior = 'changed',
    poll_interval_ms = '1000'
);

create table metrics as
    select extract_json_string(parsed, '$.name') as name,
        cast(extract_json_string(parsed, '$.value') as float) as value,
        get_first_json_object(parsed, '$.labels') as labels
    from raw_metrics;

create table cpu as
select
    extract_json_string(labels, '$.cpu') as cpu,
    extract_json_string(labels, '$.mode') as mode,
    value
from metrics
where name = 'node_cpu_seconds_total';

select sum(usage) from (
    select rate(value) as usage, cpu, mode,
        hop(interval '2 seconds', '60 seconds') as window
    from cpu
    where mode = 'user' or mode = 'system'
    group by cpu, mode, window);

This relies on the following UDFs:

fn parse_prom(s: String) -> Option 
fn parse_prom(s: String) -> Option<String> {
    let regex = regex::Regex::new(r"(?P<metric_name>\w+)\{(?P<labels>[^}]+)\}\s+(?P<metric_value>[\d.]+)").unwrap();
    let label_regex = regex::Regex::new(r##"(?P<label>[^,]+)="(?P<value>[^"]+)""##).unwrap();

    let captures = regex.captures(&s)?;

    let name = captures.name("metric_name").unwrap().as_str();
    let labels = captures.name("labels").unwrap().as_str();
    let value = captures.name("metric_value").unwrap().as_str();

    let labels: std::collections::HashMap<String, String> = label_regex.captures_iter(&labels)
        .map(|capture| (
            capture.name("label").unwrap().as_str().to_string(),
            capture.name("value").unwrap().as_str().to_string()
        ))
        .collect();


    Some(serde_json::json!({
        "name": name,
        "labels": labels,
        "value": value
    }).to_string())
}
fn rate(values: Vec) -> Option 
fn rate(values: Vec<f32>) -> Option<f32> {
    let start = values.first()?;
    let end = values.last()?;

    Some((end - start) / 60.0)
}

Currently we support framing via newlines (specified as framing = 'newline' in SQL), although we plan to add support for other framing strategies in the future.

See the format docs for more.

SQL unnest

While framing allows you to split a single message into multiple records, this can only be applied at the source and for fairly simple framing rules. For other use cases you may want to do some computation or parsing that produces an array, and then unroll that array into multiple records.

This is what the new unnest operator does. It takes a column of type Array and produces a new record for each element in the array. This is often useful for dealing with JSON data, particularly from web APIs.

For example, the Github API doesn't provide a websocket feed of events, but it does provide a REST API endpoint. We can use the polling_http connector along with unnest to turn that into a stream:

CREATE TABLE raw_events (
    value TEXT
) WITH (
    connector = 'polling_http',
    endpoint = 'https://api.github.com/networks/arroyosystems/events',
    poll_interval_ms = '5000',
    emit_behavior = 'changed',
    headers = 'User-Agent:arroyo/0.7',
    format = 'json',
    'json.unstructured' = 'true'
);

create table events AS (
    select
        extract_json_string(event, '$.id') as id,
        extract_json_string(event, '$.type') as type,
        extract_json_string(event, '$.actor.login') as login,
        extract_json_string(event, '$.repo.name') as repo
    FROM
        (select unnest(extract_json(value, '$[*]'))
            as event from raw_events));

select concat(type, ' from ', login, ' in ', repo) FROM (
    select distinct(id), type, login, repo
    from events
);

SQL union

The union operator allows you to combine the results of multiple queries into a single stream. This is often useful for combining similar data from multiple sources (for example, two kafka streams); it can also be very useful for bootstrapping, which is the process of processing historical data and then switching to a live stream.

For example, we can use union to combine two Kafka topics like this:

create table topic1 (
    value TEXT
) WITH (
    connector = 'kafka',
    topic = 'topic1',
    type = 'source',
    bootstrap_servers = 'localhost:9092',
    format = 'raw_string'
);

create table topic2 (
    value TEXT
) WITH (
    connector = 'kafka',
    topic = 'topic2',
    type = 'source',
    bootstrap_servers = 'localhost:9092',
    format = 'raw_string'
);

select value from topic1
union all select value from topic2;

State compaction

Arroyo pipelines are stateful; operators like windows and joins need to remember things in order to aggregate across time, and sources and sinks need to remember what data they've already read or written to provide exactly-once semantics. While state is stored in memory for processing, it also needs to be written durably so that it can be recovered in the event of a failure. Today we write this state to local disk or to a remote object store like S3 as Parquet files.

This process of writing state to disk is called checkpointing (you can read more about how this works in our docs). Arroyo takes frequent checkpoints (by default every ten seconds) to minimize the amount of data that needs to be reprocessed in the case of failure. Arroyo's checkpointing is incremental, which means that each checkpoint only contains the changes since the last checkpoint, but still involves writing a new set of files.

Parquet files are immutable, which means that if a value is overwritten or deleted, it remains in previous versions of the file. So over time, as we take more checkpoints, we end up with more and more files on disk, and more and more data that needs to be read and processed when we restart a pipeline.

To solve this problem, Arroyo now supports compaction, which is the process of merging multiple Parquet files into a single file. By compacting our state files, we can actually delete data, and resolve multiples writes to the same key into a single value while reducing the total number of files.

Compaction is disabled by default for this release, but you can enable it by setting the COMPACTION_ENABLED env var to true. Some workloads will not benefit from compaction, but for others it can significantly reduce the storage costs of checkpoints and improve recovery times.

Arroyo CLI

Arroyo now includes a CLI tool, arroyo, which currently can be used to manage local Arroyo clusters. If you have a rust toolchain, you can install it with

$ cargo install arroyo
$ arroyo
Arroyo is a distributed stream processor that lets users ask complex
questions of high-volume real-time data by writing SQL.

This CLI can be used to run Arroyo clusters in Docker

Usage: arroyo <COMMAND>

Commands:
  start  Starts an Arroyo cluster in Docker
  stop   Stops a running Arroyo cluster
  help   Print this message or the help of the given subcommand(s)

Options:
  -h, --help     Print help
  -V, --version  Print version

Then you can start an Arroyo cluster with

$ arroyo start

We'll be adding more commands to the CLI in the future, including support for interacting with the API to manage pipelines and run queries.

Console onboarding and example queries

The console will now lead you through a short tour of the system when you first open it, and includes a set of example queries to get you started. The example queries can be found by clicking the "book" icon in the top right of the create pipeline screen.

UDF checking in the console

When writing Rust UDFs, it's easy to end up with compiler errors as you're developing. Arroyo now checks UDFs for errors before starting the pipeline, and will show you the errors in the console.

Array indexing

Arroyo now supports indexing into arrays with subscript notation:

select make_array(1, 2, 3)[1]
from source;

Custom consumer groups for Kafka

The Kafka source now supports setting a custom consumer group name, which can be useful when relying on Kafka's built-in offset tracking to start a new pipeline from where a previous pipeline left off. Note that running multiple concurrent pipelines with the same consumer group will result in each pipeline only receiving a subset of the data.

The consumer group for a Kafka source can be set in the console, or in SQL with the group_id option:

create table source (
    value TEXT
) WITH (
    connector = 'kafka',
    topic = 'topic',
    type = 'source',
    bootstrap_servers = 'localhost:9092',
    format = 'raw_string',
    group_id = 'my_consumer_group'
);

Restarting failed pipelines

It's now possible to restart a pipeline that has failed. Pipelines can fail for a number of reasons, including repeated problems talking to an external system, invalid data, or bugs in UDFs. When a pipeline fails the controller will attempt to restart it automatically, but if the problem persists it will eventually transition to the failed state and cease execution.

It's now possible to restart such pipelines from the console or via the new /api/v1/pipelines/{id}/restart endpoint.

Improvements

Fixes

Full Changelog: https://github.com/ArroyoSystems/arroyo/compare/v0.6.0...v0.7.0

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