Multi-stage distributed queries

This proposal is about extending Druid to support multi-stage distributed queries, something that I think will be really exciting and amp up what Druid is capable of.

Motivation

Today Druid’s distributed query stack is single-stage: the Broker receives a query, slices it up into pieces, and sends one piece to each Historical. The Historicals produce partial results for their piece of the query, send them back to the Broker, and the Broker merges them into the final result set.

This venerable design works well when the bulk of processing can be done at the leaf servers that store the actual data. It scales well: Druid can handle thousands of QPS of queries, very CPU and memory efficiently, so long as there are enough Historicals running and so long as the amount of data that makes it to the Broker is relatively small.

Druid has a lot of functionality that is designed to make this single-stage distributed query stack as useful as possible. Whenever possible, we push down projections, filters, aggregations, limits, and joins. We also have a suite of builtin approximations, like approximate topN and various sketches, that people can use to minimize the amount of state that must be tracked beyond the leaf servers.

But there are some cases where this query stack doesn’t work well, and which provide the motivation for multi-stage queries:

1. Queries with very large result sets. Imagine a GROUP BY query with no LIMIT that would return billions of rows, because it groups on something with very high cardinality. Today these result sets must flow through the Broker, which creates a bottleneck.
2. Additional SQL support. We’d like to keep expanding Druid’s ability to support SQL until we can do it all. Some operations that’d we like to support, like joining together two distributed tables, cannot be done in the single-stage design.
3. Complex query structures. Today, a query with lots of JOINs and subqueries will execute certain queries in a fully distributed manner – basically, anything that can be represented as a base distributed table joined with broadcasted tables – and then finish the remainder of query processing on the Broker. In some cases this works OK, because most of the work can be done as part of the distributed queries. But in other cases it results in a lot of data being collected on the Broker, which leads to an error like “Subquery generated results beyond maximum”.
4. Ingestion. Some databases use their query stack to handle ingestion. It makes sense if you think about an ingestion as a query that writes its results to a table. It would be nice if we could do this too, so we wouldn’t need to maintain an ingestion execution stack that is separate from the query execution stack.

Design

There are a lot of interesting pieces here, so I just want to touch on each one in this main proposal. Each one should then be fleshed out separately.

Query representation and SQL planning

Background

Druid models native queries as “fat” query types that represent a full distributed query. There are four query types used by the SQL engine: scan, timeseries, topN, and groupBy. Each query type represents an entire single-stage distributed computation: there is a first piece that can run distributed, and a second piece that must run on the Broker. They all internally handle filtering, projection, sorting, limiting, etc. They all have a “dataSource” field that describes where they should get their data from, which can be “table”, representing an actual Druid datasource; “query”, representing a subquery, and which can be any other query type; “join”, representing two other datasources joined together; or a handful of other less-common datasource types.

The SQL planner’s main job is to morph a tree of relational operators either a single native query or a tree of native queries. In the latter case, it uses “query” or “join” datasources to link the native queries together. It has limited flexibility in how it does this, because native queries have a rigid computational structure: they always do broadcast join first, then projection, then filter, then aggregation, then sort.

Proposal

To support ever-more complex query structures, we will need a way of representing data flow that is more flexible than native queries. The tried and true DAG approach will work well here. So I propose that the multi-stage engine should model queries as a DAG of “stages”. Each stage:

• Runs distributed across as many servers as makes sense.
• May consume partitioned data from some set of input stages. There will always be some stages with no input stages; these stages would be reading from Druid segments, external data, or something like that.
• Produces data either aligned with its input partitions, or reshuffled in some way.

The stages would be finer-grained than native queries. For example, a stage might do scan-filter-project, or aggregate, or sort, or limit. This is a common approach in relational databases.

As a first step, I suggest we keep native queries in the picture by translating SQL -> native query -> DAG-based query. This can be done with minimal changes to the SQL planner, and would enable classic native queries to exist at the same time as DAG-based queries without much code duplication. But at some point, we’ll want to go directly from SQL -> DAG-based query, since that will give the SQL planner more flexibility to reorder operations.

Here’s an example of how a query would look. This SQL query:

SELECT
  session,
  COUNT(*) AS cnt
FROM tbl
WHERE browser = 'Firefox'
GROUP BY session
ORDER BY cnt DESC
LIMIT 10

Would have five stages:

1. Scan: Read tbl, apply the filter browser = 'Firefox', and project out session. No shuffle.
2. Aggregate I: Locally group by session and compute count(*) for each. Shuffle by session.
3. Aggregate II: Continue grouping by session within each partition, and sum the partial counts to get full counts. No shuffle. This produces a fully grouped resultset, still partitioned by session.
4. Sort: Locally order by cnt DESC. Shuffle everything into a single partition.
5. Limit: Take the first 10 rows from the single partition generated by the prior stage.

When stages connect without shuffling, we can pipeline execution locally on the same servers, so there is no need for buffering or cross-server traffic. When stages connect with shuffling, we’ll need to exchange data across servers. Depending on the needs of the query, the producing stage could stream to the consuming stage, or the producing stage could buffer up all results before the consuming stage starts.

For the query above, if we know (or are willing to bet) that there are not too many distinct session values then we can run it just as efficiently as the current single-stage approach. First, we’ll make sure that Scan and Aggregate I are scheduled on the same set of workers, so the output of Scan can be pipelined into Aggregate I locally in memory. Then, we’ll configure the shuffle in Aggregate I to shuffle everything down to a single partition. Next, we’ll make sure that Aggregate II, Sort, and Limit all run on the same server. That server would be responsible for gathering all the partitioned Aggregate I outputs and preparing the final result. Finally, we’ll set things up so all stages run concurrently, and so Aggregate II streams from Aggregate I. Put together, this is exactly what the Historicals and Brokers do today.

Ingestion

In #11929 there is a proposal for adding a SQL INSERT statement. It could be implemented on top of our existing batch ingestion tasks: the SQL layer could convert relational operators to indexing tasks just as well as it can convert them to native queries. But I think it would be nicer to implement it on top of a multi-stage query stack.

We’d just need to do two things:

1. Provide a way for the query stack to read external data. There’s a natural way to do this through an “external” DataSource that maps onto RowBasedSegments. It would be similar to how we plug lookups into the query engines, except a little more complicated because we’ll need to split the external datasource before sending it down to various servers.
2. Provide a way for the query stack to generate segments. Once we have this multi-stage structure this is also natural: we can add a final stage to any query that shuffles data to match the target segment size and then generates and publishes segments.

So I propose that we work towards this instead of having the SQL planner generate batch indexing tasks. It has a nice side benefit: part (1) alone means that we’d be able to query external data in a regular query too. I don’t think that’ll be a core use case for Druid, but it has some usefulness, like previewing what an INSERT might do, or doing an ad-hoc join of Druid datasources with some external data.

We’ll also need to figure out what to do about streaming ingest at some point. I’m not sure what to do there but I think there are a few options that make sense. Even if the multi-stage query stack doesn’t have builtin support for streaming queries, we can layer streamingness on top in the same way that we do today with indexing tasks: there is a supervisor that manages a series of tasks, each of which reads a chunk of data from Kafka and then publishes it.

Server setup

There is an obvious question: where will this query engine run? Today we have Brokers, Historicals, and indexing stuff (MiddleManager or Indexers). I don’t want to add a net new service or process type in the long run, because we have a lot already. But I think in the short run we should add a new process type.

I think at first the Broker should remain in charge of SQL planning, and may route certain queries to these new multi-stage query processes if they are running in that particular cluster. It could do this based on user request (like a context parameter) or based on aspects of the query (like presence of INSERT, or external data, or a “complex enough” query).

However, like I said, in the long run I don’t think it’s a good plan to have an extra process type on top of all the ones we already have. So I think in the long run we should shoot for this process type actually being able to serve as a replacement for the Broker, Historicals, and MMs/Indexers. By that I mean that in the fullness of time, it should be able to:

• Receive and plan SQL queries, like a Broker.
• Cache segments locally, like a Historical.
• Run simple single-stage queries in an extremely efficient way, like the Historical + Broker combo.
• Run ingestions (INSERT statements) in a distributed and scalable manner, like MMs or Indexers.

At that point, a small Druid cluster would be pretty simple: just a Coordinator and this new process type (or a few of them for scale-out). And a large cluster could still specialize. Imagine setting up a tier of these new processes that cache segments but don’t perform ingestion, and another tier that don’t cache segments but do perform ingestion. That’s similar to setting up Historicals and MMs today, but more configuration-driven. It would allow simple clusters to be simple and complex clusters to be complex.

Code changes

A sketch of the code changes I think we’ll need to make the above work out:

1. Low-level mechanism for shuffling data between servers. This should include an efficient way to transfer large amounts of data over the network. Currently we use Smile-over-HTTP for this, which works well for relatively small amounts of data, but I think we can do better. It should also include both a pipelined implementation (producing stage streams to the consuming stage), and a buffered implementation (producing stage generates all data before consuming stage starts) for operations that cannot be pipelined.
2. Cluster-level execution coordinator for a multi-stage query.
3. Server-local execution coordinator for a single stage of a multi-stage query.
4. An actual process type for the above three things to run in.
5. Converter that generates multi-stage (DAG-based) queries from today’s native query types. (This enables hooking into the existing SQL layer, since it generates native queries.)
6. To make ingestion-through-query work: adapters from external data into the query stack, and from the query stack to the segment-generation code.

At Imply we’ve started to prototype a multi-stage query engine with the above design. We’ve implemented some of the pieces: not all of them, but enough to perform some basic queries. At this point I thought it would be a good time to open up a discussion with the wider community, since as we continue working on this stuff, we’d like to integrate it into Apache Druid.

Rationale

A.K.A. the “why not” section.

Why not integrate with an existing open-source query engine?

I wouldn’t want to stop anyone from integrating with another open-source query engine. Some people might prefer to deploy that way.

But I think we will want this kind of functionality as something native in Druid, for two reasons. First: I think the user experience will be nicer if we don’t require a dependency on another system. Second: I expect there will be opportunities for optimization that we can use if everything is built in to Druid, and that would be tough to implement with an external query stack.

Besides, the work we’ll need to do in order to build a multi-stage query engine will also benefit integrations with other engines.

Why not gradually add multi-stage capabilities to the existing query stack?

I think it’s a question of complexity.

The core query stack has three main hook points: individual segment scan, result merge on Historicals, and result merge on Broker. The implementations of the native query types, especially groupBy, have got quite complex over the years as new features have been added and needed to be mapped onto these hook points. For example: subtotals, ordering, limiting, having, are all typically done as part of “result merge on the Broker”. The hook points are doing more work than originally envisioned, which makes the code more difficult to follow and extend.

So I think it’s time to try a different approach with the query stack, rather than adding new capabilities. As we create a DAG-based query stack, we can split queries up into stages and move more of the “fancy stuff” to the framework, which will simplify the query-specific logic.

We can still share code, though. It won’t be 100% new. We can share all of the StorageAdapter stuff (cursors, etc), the ColumnSelectorFactory stuff, and the single-segment processing code for groupBy and topN. This lower-level code is super optimized and works well. It’s more the mid/high level stuff that IMO would benefit from a different approach.

[gianm]

An update: we’re planning to start doing PRs to set up the batch-query version of the feature in the near future. This batch-query version runs via the indexing service, and is suitable for ingestion and data management queries (INSERT, REPLACE as described in #11929). Each one is meant to explore one new area: they’re split up this way to aid review and discussion. Here’s the planned sequence.

1. A more reliable RPC client for task -> task and task -> Overlord communication. This one is important for ensuring the communication between servers involved in a query is really robust.

2. A binary format for transferring data between servers, and for spilling data to disk, that is more efficient than the Smile format we currently use. This one is important because of the high amount of data being transferred between servers. This would be in core. At first it would only be used by the query task (see next point 3), but I do think it would make sense to use it for more stuff over time, like Historical -> Broker communication and spilling for GroupBy queries.

3. An indexing service task that can run Druid queries (“query task”), that can accept a target datasource describing where to put the results, and that can handle “external” typed datasources. This would be able to run the kinds of queries generated by the functionality in #11929 (i.e., the kind currently being generated in CalciteInsertDmlTest and CalciteReplaceDmlTest). This would likely be in an extension.

4. SQL bindings for the query task. This would involve adding an endpoint that creates of a query task rather than running a query using the Broker-and-Historical-based stack. This change would enable an INSERT or REPLACE query to actually execute as a query task. This would likely be in that same extension.

5. Web console support for the new SQL syntax and endpoint.

After (3) the functionality will be usable for ingestion and data management, but kind of obscure, because you’ll need to embed a native Druid query into an indexing service task. After (4) it will be easier to use, since you can write SQL. After (5) it will be VERY easy to use! Then we can focus on making it more robust, more performant, adding features, and improving ease of use even further.

I mentioned that we’ve been prototyping some of the pieces of a multi-stage query at Imply. This sequence of PRs represents a contribution of everything we’ve prototyped so far. Of course, even after doing all of this, the functionality wouldn’t be well suited for low-latency queries. The indexing service task system is not really designed for that. But there is a path towards making low-latency queries happen in a way that shares substantial code with this initial, task-oriented functionality. We’ll be able to walk that path in the Apache Druid codebase using these contributions as a base.

Very much looking to feedback on this work, and integrating it into Druid!

[julianhyde]

@gianm When you are doing a big join with filters on both sides - e.g. orders from customers in california for red products - then you only want to read customers who may have bought a red product, and also only want to read orders placed by a customer in california. Thus you want a filter implied by the join to travel both ways, and you can handle a few false positives. A good way is to generate a bloom filter as you are scanning customers and pass it to the scan of products, and vice versa.

In short, approximate semi-joins pushed down to each sides of a join as pre-filters.

The paper “Sideways Information Passing for Push-Style Query Processing” from 2008 describes the idea pretty well. But we were doing it in Broadbase in around 1998.