Re: Batching in executor - Mailing list pgsql-hackers

On 10/27/25 08:24, Amit Langote wrote:
> Hi Tomas,
> 
> On Mon, Sep 29, 2025 at 8:01 PM Tomas Vondra <tomas@vondra.me> wrote:
>>
>> Hi Amit,
>>
>> Thanks for the patch. I took a look over the weekend, and done a couple
>> experiments / benchmarks, so let me share some initial feedback (or
>> rather a bunch of questions I came up with).
> 
> Thank you for reviewing the patch and taking the time to run those
> experiments. I appreciate the detailed feedback and questions.  I also
> apologize for my late reply, I spent perhaps way too much time going
> over your index prefetching thread trying to understand the notion of
> batching that it uses and getting sidelined by other things while
> writing this reply.
> 

Cool! Now you can do a review of the index prefetch patch ;-)

>> I'll start with some general thoughts, before going into some nitpicky
>> comments about patches / code and perf results.
>>
>> I think the general goal of the patch - reducing the per-tuple overhead
>> and making the executor more efficient for OLAP workloads - is very
>> desirable. I believe the limitations of per-row executor are one of the
>> reasons why attempts to implement a columnar TAM mostly failed. The
>> compression is nice, but it's hard to be competitive without an executor
>> that leverages that too. So starting with an executor, in a way that
>> helps even heap, seems like a good plan. So +1 to this.
> 
> I'm happy to hear that you find the overall direction worthwhile.
> 
>> While looking at the patch, I couldn't help but think about the index
>> prefetching stuff that I work on. It also introduces the concept of a
>> "batch", for passing data between an index AM and the executor. It's
>> interesting how different the designs are in some respects. I'm not
>> saying one of those designs is wrong, it's more due different goals.
>>
>> For example, the index prefetching patch establishes a "shared" batch
>> struct, and the index AM is expected to fill it with data. After that,
>> the batch is managed entirely by indexam.c, with no AM calls. The only
>> AM-specific bit in the batch is "position", but that's used only when
>> advancing to the next page, etc.
>>
>> This patch does things differently. IIUC, each TAM may produce it's own
>> "batch", which is then wrapped in a generic one. For example, heap
>> produces HeapBatch, and it gets wrapped in TupleBatch. But I think this
>> is fine. In the prefetching we chose to move all this code (walking the
>> batch items) from the AMs into the layer above, and make it AM agnostic.
> 
> ...
> 
>> But for the batching, we want to retain the custom format as long as
>> possible. Presumably, the various advantages of the TAMs are tied to the
>> custom/columnar storage format. Memory efficiency thanks to compression,
>> execution on compressed data, etc. Keeping the custom format as long as
>> possible is the whole point of "late materialization" (and materializing
>> as late as possible is one of the important details in column stores).
> 
> Exactly -- keeping the TAM-specific batch format as long as possible
> is a key goal here. As you noted, the benefits of a custom storage
> format (compression, operating on compressed data, etc.) are best
> realized when we delay materialization until absolutely necessary.  I
> want to design this patch that each TAM can produce and use its own
> batch representation internally, only wrapping it when interfacing
> with the executor in a generic way.  I admit that's not entirely true
> with the patch as it stands as I write above below.
> 

Understood. Makes sense in general.

>> How far ahead have you though about these capabilities? I was wondering
>> about two things in particular. First, at which point do we have to
>> "materialize" the TupleBatch into some generic format (e.g. TupleSlots).
>> I get it that you want to enable passing batches between nodes, but
>> would those use the same "format" as the underlying scan node, or some
>> generic one? Second, will it be possible to execute expressions on the
>> custom batches (i.e. on "compressed data")? Or is it necessary to
>> "materialize" the batch into regular tuple slots? I realize those may
>> not be there "now" but maybe it'd be nice to plan for the future.
> 
> I have been thinking about those future capabilities. Currently, the
> patch keeps tuples in the TAM-specific batch format up until they need
> to be consumed by a node that doesn’t understand that format or has
> not been modified to invoke the TAM callbacks to decode it.  In the
> current patch, that means we materialize to regular TupleTableSlots at
> nodes that require it (for example, the scan node reading from TAM
> needing to evaluate quals, etc.). However, the intention is to allow
> batches to be passed through as many nodes as possible without
> materialization, ideally using the same format produced by the scan
> node all the way up until reaching a node that can only work with
> tuples in TupleTableSlots.
> 
> As for executing expressions directly on the custom batch data: that’s
> something I would like to enable in the future. Right now, expressions
> (quals, projections, etc.) are evaluated after materializing into
> normal tuples in TupleTableSlots stored in TupleBatch, because the
> expression evaluation code isn’t yet totally batch-aware or is very
> from doing things like operate on compressed data in its native form.
> Patches 0004-0008 do try to add batch-aware expression evaluation but
> that's just a prototype.  In the long term, the goal is to allow
> expression evaluation on batch data (for example, applying a WHERE
> clause or aggregate transition directly on a columnar batch without
> converting it to heap tuples first). This will require significant new
> infrastructure (perhaps specialized batch-aware expression operators
> and functions), so it's not in the current patch, but I agree it's
> important to plan for it. The current design doesn’t preclude it, it
> lays some groundwork by introducing the batch abstraction -- but fully
> supporting that will be future work.
> 
> That said, one area I’d like to mention while at it, especially to
> enable native execution on compressed or columnar batches, is giving
> the table AM more control over how expression evaluation is performed
> on its batch data. In the current patch, the AM can provide a
> materialize function via TupleBatchOps, but that always produces an
> array of TupleTableSlots stored in the TupleBatch, not an opaque
> representation that remains under AM control. Maybe that's not bad for
> a v1 patch.

I think materializing into a batch of TupleTableSlots (and then doing
the regular expression evaluation) seems perfectly fine for v1. It's the
simplest fallback possible, and we'll need it anyway if overriding the
expression evaluation will be optional (which I assume it will be?).

> When evaluating expressions over a batch, a BatchVector
> is built by looping over these slots and invoking the standard
> per-tuple getsomeattrs() to "deform" a tuple into needed columns.
> While that enables batch-style EEOPs for qual evaluation and aggregate
> transition (and is already a gain over per-row evaluation), it misses
> the opportunity to leverage any batch-specific optimizations the AM
> could offer, such as vectorized decoding or filtering over compressed
> data, and other AM optimizations for getting only the necessary
> columns out possibly in a vector format.
> 

I'm not sure about this BatchVector thing. I haven't looked into that
very much, I'd expect the construction to be more expensive than the
benefits (compared to just doing the materialize + regular evaluation),
but maybe I'm completely wrong. Or maybe we could keep the vector
representation for multiple operations? No idea.

But it seems like a great area for experimenting ...

> I’m considering extending TupleTableSlotOps with a batch-aware variant
> of getsomeattrs(), something like slot_getsomeattrs_batch(), so that
> AMs can populate column vectors (e.g., BatchVector) directly from
> their native format. That would allow bypassing slot materialization
> entirely and plug AM-provided decoding logic directly into the
> executor’s batch expression paths. This isn’t implemented yet, but I
> see it as a necessary step toward supporting fully native expression
> evaluation over compressed or columnar formats. I’m not yet sure if
> TupleTableSlotOps is the right place for such a hook, it might belong
> elsewhere in the abstraction, but exposing a batch-aware interface for
> this purpose seems like the right direction.
> 

No opinion. I don't see it as a necessary prerequisite for the other
parts of the patch series, but maybe the BatchVector really helps, and
then this would make perfect sense. I'm not sure there's a single
"correct" sequence in which to do these improvements, it's always a
matter of opinion.

>> It might be worth exploring some columnar formats, and see if this
>> design would be a good fit. Let's say we want to process data read from
>> a parquet file. Would we be able to leverage the format, or would we
>> need to "materialize" into slots too early? Or maybe it'd be good to
>> look at the VCI extension [1], discussed in a nearby thread. AFAICS
>> that's still based on an index AM, but there were suggestions to use TAM
>> instead (and maybe that'd be a better choice).
> 
> Yeah, looking at columnar TAMs or FDWs is on my list. I do think the
> design should be able to accommodate true columnar formats like
> Parquet. If we had a table AM (or FDW) that reads Parquet files into a
> columnar batch structure, the executor batching framework should
> ideally allow us to pass that batch along without immediately
> materializing to tuples.  As mentioned before, we might have to adjust
> or extend the TupleBatch abstraction to handle a wider variety of
> batch formats, but conceptually it fits -- the goal is to avoid
> forcing early materialization. I will definitely keep the Parquet
> use-case in mind and perhaps do some experiments with a columnar
> source to ensure we aren’t baking in any unnecessary materialization.
> Also, thanks for the reference to the VCI extension thread; I'll take
> a look at that.
> 

+1 I think having a TAM/FDW reading those established and common formats
is a good way to validate the overall design.

>> The other option would be to "create batches" during execution, say by
>> having a new node that accumulates tuples, builds a batch and sends it
>> to the node above. This would help both in cases when either the lower
>> node does not produce batches at all, or the batches are too small (due
>> to filtering, aggregation, ...). Or course, it'd only win if this
>> increases efficiency of the upper part of the plan enough to pay for
>> building the batches. That can be a hard decision.
> 
> Yes, introducing a dedicated executor node to accumulate and form
> batches on the fly is an interesting idea, I have thought about it and
> even mentioned it in passing in the pgconf.dev unconference. This
> could indeed cover scenarios where the data source (a node) doesn't
> produce batches (e.g., a non-batching node feeding into a
> batching-aware upper node) or where batches coming from below are too
> small to be efficient. The current patch set doesn’t implement such a
> node; I focused on enabling batching at the scan/TAM level first. The
> cost/benefit decision for a batch-aggregator node is tricky, as you
> said. We’d need a way to decide when the overhead of gathering tuples
> into a batch is outweighed by the benefits to the upper node. This
> likely ties into costing or adaptive execution decisions. It's
> something I’m open to exploring in a future iteration, perhaps once we
> have more feedback on how the existing batching performs in various
> scenarios. It might also require some planner or executor smarts
> (maybe the executor can decide to batch on the fly if it sees a
> pattern of use, or the planner could insert such nodes when
> beneficial).
> 

Yeah, those are good questions. I don't have a clear idea how should we
decide when to do this batching. Costing during planning is the
"traditional" option, with all the issues (e.g. it requires a reasonably
good cost model). Another option would be some sort of execution-time
heuristics - buts then which node would be responsible for building the
batches (if we didn't create them during planning)?

I agree it makes sense to focus on batching at the TAM/scan level for
now. That's a pretty big project already.

>> You also mentioned we could make batches larger by letting them span
>> multiple pages, etc. I'm not sure that's worth it - wouldn't that
>> substantially complicate the TAM code, which would need to pin+track
>> multiple buffers for each batch, etc.? Possible, but is it worth it?
>>
>> I'm not sure allowing multi-page batches would actually solve the issue.
>> It'd help with batches at the "scan level", but presumably the batch
>> size in the upper nodes matters just as much. Large scan batches may
>> help, but hard to predict.
>>
>> In the index prefetching patch we chose to keep batches 1:1 with leaf
>> pages, at least for now. Instead we allowed having multiple batches at
>> once. I'm not sure that'd be necessary for TAMs, though.
> 
> I tend to agree with you here. Allowing a single batch to span
> multiple pages would add quite a bit of complexity to the table AM
> implementations (managing multiple buffer pins per batch, tracking
> page boundaries, etc.), and it's unclear if the benefit would justify
> that complexity. For now, I'm inclined not to pursue multi-page
> batches at the scan level in this patch. We can keep the batch
> page-local (e.g., for heap, one batch corresponds to max one page, as
> it does now). If we need larger batch sizes overall, we might address
> that by other means -- for example, by the above-mentioned idea of a
> higher-level batching node or by simply producing multiple batches in
> quick succession.
> 

+1

> You’re right that even if we made scan batches larger, it doesn’t
> necessarily solve everything, since the effective batch size at
> higher-level nodes could still be constrained by other factors. So
> rather than complicating the low-level TAM code with multi-page
> batches, I'd prefer to first see if the current approach (with
> one-page batches) yields good benefits and then consider alternatives.
> We could also consider letting a scan node produce multiple batches
> before yielding to the upper node (similar to how the index
> prefetching patch can have multiple leaf page batches in flight) if
> needed, but as you note, it might not be necessary for TAMs yet. So at
> this stage, I'll keep it simple.
> 

+1

>> This also reminds me of LIMIT queries. The way I imagine a "batchified"
>> executor to work is that batches are essentially "units of work". For
>> example, a nested loop would grab a batch of tuples from the outer
>> relation, lookup inner tuples for the whole batch, and only then pass
>> the result batch. (I'm ignoring the cases when the batch explodes due to
>> duplicates.)
>>
>> But what if there's a LIMIT 1 on top? Maybe it'd be enough to process
>> just the first tuple, and the rest of the batch is wasted work? Plenty
>> of (very expensive) OLAP have that, and many would likely benefit from
>> batching, so just disabling batching if there's LIMIT seems way too
>> heavy handed.
> 
> Yeah, LIMIT does complicate downstream batching decisions. If we
> always use a full-size batch (say 64 tuples) for every operation, a
> query with LIMIT 1 could end up doing a lot of unnecessary work
> fetching and processing 63 tuples that never get used. Disabling
> batching entirely for queries with LIMIT would indeed be overkill and
> lose benefits for cases where the limit is not extremely selective.
> 
>> Perhaps it'd be good to gradually ramp up the batch size? Start with
>> small batches, and then make them larger. The index prefetching does
>> that too, indirectly - it reads the whole leaf page as a batch, but then
>> gradually ramps up the prefetch distance (well, read_stream does that).
>> Maybe the batching should have similar thing ...
> 
> An adaptive batch size that ramps up makes a lot of sense as a
> solution. We could start with a very small batch (say 4 tuples) and if
> we detect that the query needs more (e.g., the LIMIT wasn’t satisfied
> yet or more output is still being consumed), then increase the batch
> size for subsequent operations. This way, a query that stops early
> doesn’t incur the full batching overhead, whereas a query that does
> process lots of tuples will gradually get to a larger batch size to
> gain efficiency. This is analogous to how the index prefetching ramps
> up prefetch distance, as you mentioned.
> 
> Implementing that will require some careful thought. It could be done
> either in the planner (choose initial batch sizes based on context
> like LIMIT) or more dynamically in the executor (adjust on the fly). I
> lean towards a runtime heuristic because it’s hard for the planner to
> predict exactly how a LIMIT will play out, especially in complex
> plans. In any case, I agree that a gradual ramp-up or other adaptive
> approach would make batching more robust in the presence of query
> execution variability. I will definitely consider adding such logic,
> perhaps as an improvement once the basic framework is in.
> 

I agree a runtime heuristics is probably the right approach. After all,
a lot of the issues with LIMIT queries is due to the planner not knowing
the real data distribution, etc.

>> In fact, how shall the optimizer decide whether to use batching? It's
>> one thing to decide whether a node can produce/consume batches, but
>> another thing is "should it"? With a node that "builds" a batch, this
>> decision would apply to even more plans, I guess.
>>
>> I don't have a great answer to this, it seems like an incredibly tricky
>> costing issue. I'm a bit worried we might end up with something too
>> coarse, like "jit=on" which we know is causing problems (admittedly,
>> mostly due to a lot of the LLVM work being unpredictable/external). But
>> having some "adaptive" heuristics (like the gradual ramp up) might make
>> it less risky.
> 
> I agree that deciding when to use batching is tricky. So far, the
> patch takes a fairly simplistic approach: if a node (particularly a
> scan node) supports batching, it just does it, and other parts of the
> plan will consume batches if they are capable. There isn’t yet a
> nuanced cost-based decision in the planner for enabling batching. This
> is indeed something we’ll have to refine. We don’t want to end up with
> a blunt on/off GUC that could cause regressions in some cases.
> 
> One idea is to introduce costing for batching: for example, estimate
> the per-tuple savings from batching vs the overhead of materialization
> or batch setup. However, developing a reliable cost model for that
> will take time and experimentation, especially with the possibility of
> variable batch sizes or adaptive behavior. Not to mention, that will
> be adding one more dimension to planner's costing model making the
> planning more expensive and unpredictable.  In the near term, I’m fine
> with relying on feedback and perhaps manual tuning (GUCs, etc.) to
> decide on batching, but that’s perhaps not a long-term solution.
> 

Yeah, the cost model is going to be hard, because this depends on so
much low-level plan/hardware details. Like, the TAM may allow execution
on compressed data / leverage vectorization, .... But maybe the CPU does
not do that efficiently? There's so many unknown unknowns ...

Considering we still haven't fixed the JIT cost model, maybe it's better
to not rely on it too much for this batching patch? Also, all those
details contradict the idea that cost models are a simplified model of
the reality.

> I share your inclination that adaptive heuristics might be the safer
> path initially. Perhaps the executor can decide to batch or not batch
> based on runtime conditions. The gradual ramp-up of batch size is one
> such adaptive approach. We could also consider things like monitoring
> how effective batching is (are we actually processing full batches or
> frequently getting cut off?) and adjust behavior. These are somewhat
> speculative ideas at the moment, but the bottom line is I’m aware we
> need a smarter strategy than a simple switch. This will likely evolve
> as we test the patch in more scenarios.
> 

I think the big question is how much can the batching change the
relative cost of two plans (I mean, actual cost, not just estimates).

Imagine plans P1 and P2, where

   cost(P1) < cost(P2) = cost(P1) + delta

where "delta" is small (so P1 is faster, but not much).  If we
"batchify" the plans into P1' and P2', can this happen?

  cost(P1') >> cost(P2')

That is, can the "slower" plan P2 benefit much more from the batching,
making it significantly faster?

If this is unlikely, we could entirely ignore batching during planning,
and only do that as post-processing on the selected plan, or perhaps
even just during execution.

OTOH that's what JIT does, and we know it's not perfect - but that's
mostly because JIT has rather unpredictable costs when enabling. Maybe
batching doesn't have that.

>> FWIW the current batch size limit (64 tuples) seems rather low, but it's
>> hard to say. It'd be good to be able to experiment with different
>> values, so I suggest we make this a GUC and not a hard-coded constant.
> 
> Yeah, I was thinking the same while testing -- the optimal batch size
> might vary by workload or hardware, and 64 was a somewhat arbitrary
> starting point. I will make the batch size limit configurable
> (probably as a GUC executor_batch_tuples, maybe only developer-focused
> at first). That will let us and others experiment easily with
> different batch sizes to see how it affects performance. It should
> also help with your earlier point: for example, on a machine where 64
> is too low or too high, we can adjust it without recompiling. So yes,
> I'll add a GUC for the batch size in the next version of the patch.
> 

+1 to have developer-only GUC for testing. But the goal should be to not
expect users to tune this.

>> As for what to add to explain, I'd start by adding info about which
>> nodes are "batched" (consuming/producing batches), and some info about
>> the batch sizes. An average size, maybe a histogram if you want to be a
>> bit fancy.
> 
> Adding more information to EXPLAIN is a good idea. In the current
> patch, EXPLAIN does not show anything about batching, but it would be
> very helpful for debugging and user transparency to indicate which
> nodes are operating in batch mode.  I will update EXPLAIN to mark
> nodes that produce or consume batches. Likely I’ll start with
> something simple like an extra line or tag for a node, e.g., "Batch:
> true (avg batch size 64)" or something along those lines. An average
> batch size could be computed if we have instrumentation, which would
> be useful to see if, say, the batch sizes ended up smaller due to
> LIMIT or other factors. A full histogram might be more detail than
> most users need, but I agree even just knowing average or maximum
> batch size per node could be useful for performance analysis. I'll
> implement at least the basics for now, and we can refine it (maybe add
> more stats) if needed.

+1 to start with something simple

> 
> (I had added a flag in the EXPLAIN output at one point, but removed it
> due to finding the regression output churn too noisy, though I
> understand I'll have to bite the bullet at some point.)
> 

Why would there be regression churn, if the option is disabled by default?

>> Now, numbers from some microbenchmarks:
>>
>> ...
>>>> Perhaps I did something wrong. It does not surprise me this is somewhat
>> CPU dependent. It's a bit sad the improvements are smaller for the newer
>> CPU, though.
> 
> Thanks for sharing your benchmark results -- that’s very useful data.
> I haven’t yet finished investigating why there's a regression relative
> to master when executor_batching is turned off. I re-ran my benchmarks
> to include comparisons with master and did observe some regressions in
> a few cases too, but I didn't see anything obvious in profiles that
> explained the slowdown. I initially assumed it might be noise, but now
> I suspect it could be related to structural changes in the scan code
> -- for example, I added a few new fields in the middle of
> HeapScanDescData, and even though the batching logic is bypassed when
> executor_batching is off, it’s possible that change alone affects
> memory layout or cache behavior in a way that penalizes the unbatched
> path. I haven’t confirmed that yet, but it’s on my list to look into
> more closely.
> 
> Your observation that newer CPUs like the Ryzen may see smaller
> improvements makes sense -- perhaps they handle the per-tuple overhead
> more efficiently to begin with. Still, I’d prefer not to see
> regressions at all, even in the unbatched case, so I’ll focus on
> understanding and fixing that part before drawing conclusions from the
> performance data.
> 
> Thanks again for the scripts -- those will help a lot in narrowing things down.
> 

If needed, I can rerun the tests and collect additional information
(e.g. maybe perf-stat or perf-diff would be interesting).

>> I also tried running TPC-H. I don't have useful numbers yet, but I ran
>> into a segfault - see the attached backtrace. It only happens with the
>> batching, and only on Q22 for some reason. I initially thought it's a
>> bug in clang, because I saw it with clang-22 built from git, and not
>> with clang-14 or gcc. But since then I reproduced it with clang-19 (on
>> debian 13). Still could be a clang bug, of course. I've seen ~20 of
>> those segfaults so far, and the backtraces look exactly the same.
> 
> The v3 I posted fixes a tricky bug in the new EEOPs for batched-agg
> evaluation that I suspect is also causing the crash you saw.
> 
> I'll try to post a v4 in a couple of weeks with some of the things I
> mentioned above.
> 

Sounds good. Thank you.


regards

-- 
Tomas Vondra