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environmentd consumes large amounts of memory during SELECTs #24724

Open def- opened 7 months ago

def- commented 7 months ago

What version of Materialize are you using?

2390836c89845788d375e661e3d4712c1de81f09

What is the issue?

Happened in CI: https://buildkite.com/materialize/nightlies/builds/6168#018d4332-de94-4221-80a0-7c315a105a79 OoM of environmentd. By running the test locally with bin/mzcompose --find limits down && bin/mzcompose --find limits run default --scenario=Indexes I am seeing normal memory consumption while the indexes are being created, and then excessive memory consumption while the 1000 selects are running. The memory consumption of environmentd spiked to over 3 GB. I'm not sure if this is expected when having many tables/indexes and selecting from all of them. I'll try to find a better repro.

jkosh44 commented 7 months ago

Can you give a brief description of what the test does? If there are 1000 concurrent sessions all running SELECTs then it is possible to OOM environmentd.

def- commented 7 months ago

It creates a table with 1000 columns, creates an index on each of these columns, then selects each of the columns sequentially, returning the 1 field. Nothing happens in parallel, it's all just a big testdrive fragment. So it is surprising that 3 GB of memory are used for results which constitute of 1000 integers (and should actually be cleared up?).

jkosh44 commented 7 months ago

Yeah, that is surprising. Can you send me a link to the testdrive file so that I can try and repro?

def- commented 7 months ago

https://gist.github.com/def-/ec99d7fac4a1d9c5572468c76015ef47

Edit: I also tried it with 10000 columns/indexes, and then I see an OoM with ~ 100 GB used while creating the indexes already, but that is not something we ever tried before, so maybe it has always been excessive for Mz: https://gist.github.com/def-/5593ddc8d439d2fbae0de4f867b9fab3

[Fri Jan 26 14:32:50 2024] Tasks state (memory values in pages):
[Fri Jan 26 14:32:50 2024] [  pid  ]   uid  tgid total_vm      rss pgtables_bytes swapents oom_score_adj name
[Fri Jan 26 14:32:50 2024] [ 704903]   999 704903      696      320    40960        0             0 tini
[Fri Jan 26 14:32:50 2024] [ 704981]   999 704981  8694005  1886074 64245760        0             0 environmentd
[Fri Jan 26 14:32:50 2024] [ 705030]   999 705030   532384   214704  2613248        0             0 cockroach
[Fri Jan 26 14:32:50 2024] [ 707864]   999 707864   129300    18501   983040        0             0 clusterd
[Fri Jan 26 14:32:50 2024] [ 707865]   999 707865   208744    43660  1654784        0             0 clusterd
[Fri Jan 26 14:32:50 2024] oom-kill:constraint=CONSTRAINT_MEMCG,nodemask=(null),cpuset=docker-742b0f3b5228b805bb8505b2dac10d0613fa28a56b2230b83edaab14f14ec792.scope,mems_allowed=0,oom_memcg=/system.slice/docker-742b0f3b5228b805bb8505b2dac10d0613fa28a56b2230b83edaab14f14ec792.scope,task_memcg=/system.slice/docker-742b0f3b5228b805bb8505b2dac10d0613fa28a56b2230b83edaab14f14ec792.scope,task=environmentd,pid=704981,uid=999
[Fri Jan 26 14:32:50 2024] Memory cgroup out of memory: Killed process 704981 (environmentd) total-vm:34776020kB, anon-rss:7452136kB, file-rss:92160kB, shmem-rss:0kB, UID:999 pgtables:62740kB oom_score_adj:0
jkosh44 commented 7 months ago

https://gist.github.com/def-/ec99d7fac4a1d9c5572468c76015ef47

Thanks, I think I may have found the mzcompose file too. I'm going to try and repro locally with this

import psycopg2

MATERIALIZED_URL = "postgresql://materialize@localhost:6875/materialize"

def main():
    with psycopg2.connect(MATERIALIZED_URL) as conn:
        index_count = 1000
        cur = conn.cursor()
        cur.execute("CREATE TABLE t (" + ", ".join(f"f{i} INTEGER" for i in range(index_count)) + ");")
        conn.commit()
        print("created table")
        cur.execute("INSERT INTO t VALUES (" + ", ".join(str(i) for i in range(index_count)) + ");")
        print("inserted values")
        conn.commit()
        for i in range(index_count):
            cur.execute(f"CREATE INDEX i{i} ON t(f{i})")
            conn.commit()
            print(f"created index {i}")
        for i in range(index_count):
            cur.execute(f"SELECT f{i} FROM t;")
            print(cur.fetchone())

if __name__ == "__main__":
    main()
def- commented 7 months ago

Here's a flamegraph visualization during the CREATE INDEX: https://gist.github.com/def-/6ef520eab70c1f3732f1b2a649f3fb80 Screenshot 2024-01-26 at 16 14 01 And here during the SELECT: https://gist.github.com/def-/925b2b6071655517080db4dbdba6490a Screenshot 2024-01-26 at 16 32 53 Unfortunately dumping the mzfg via bin/mzcompose --find limits exec materialized curl http://127.0.0.1:6878/prof/ --data-raw action=dump_sym_mzfg causes an OoM itself.

jkosh44 commented 7 months ago

I'm running it locally as well and the flamegraph visualization I have during CREATE INDEX look like this: https://gist.github.com/jkosh44/baa773b5e55a908845d499169bcaafd3, which seems to be much smaller than the 4GB that you're seeing.

def- commented 7 months ago

Strange, I'm using the Linux x86-64 docker images of the current main state.

jkosh44 commented 7 months ago

Here's a flamegraph of SELECT in my local run: https://gist.github.com/jkosh44/acda1b59b385df259f77b506d5c7d683, it also seems to use much less memory than what you're seeing.

I'm running it directly on my linux machine without docker, which may be the difference. I'll look through you're traces and try and see where all the memory is going.

def- commented 7 months ago

The way I run it through mzcompose also enables all these settings: https://github.com/MaterializeInc/materialize/blob/5ddf876485bf14aa44e33b331d007e1e0887a034/misc/python/materialize/mzcompose/__init__.py#L41-L93

jkosh44 commented 7 months ago

When comparing your flamegraphs to my flamegraphs, the big difference is that you have a DataflowDescription in create_index_finish() that is 1.5GiB while in my flamegraph that same DataflowDescription is 169MiB. I'll do some experimenting with docker and those system parameters.

def- commented 7 months ago

"compute_dataflow_max_inflight_bytes": "134217728", # 128 MiB sounds slightly suspicious

jkosh44 commented 7 months ago

FWIW, I enabled all those flags and still didn't see the memory spike. I'm going to try with Docker now.

jkosh44 commented 7 months ago

Just a couple of notes.

jkosh44 commented 7 months ago

When using mzcompose, after creating about 750 indexes my memory dump still sits at around 1GiB, https://gist.github.com/jkosh44/e18f89647b2146cecd9c192fb613c353. I'm not sure what's going on, but it seems clear from your dump that something weird is going on in create_index_finish() to create a 1.5GiB DataflowDescription. @MaterializeInc/compute is there any reason that would happen?

teskje commented 7 months ago

This is almost certainly not a single DataflowDescription but the sum of all that are kept in the ComputeCommandHistory. With 1000 indexes, we'll have at least 1000 dataflows in the history. 1.5 GiB still feels like a lot though.

Either the 1000 DataflowDescriptions are somehow very large, or we are keeping more dataflows around in the history than we expect. The number of dataflows in the history is exposed through a metric: mz_compute_controller_history_command_count.

ggevay commented 7 months ago

As Jan is saying, either we are keeping around a lot of DataflowDescriptions for some reason, or some of them get very big (MB size range).

For investigating the latter, could you do a

println!("{:?}", dataflow);

on these dataflow descriptions in create_index_finish, before the ship_dataflow calls? If some of them are really in the megabyte size range, then maybe the debug printout will make it obvious what is making them so big. (I tried this now on just a random dataflow, and it wasn't big at all, so it would be better to look at this in the above repro.)

def- commented 7 months ago

I assume you mean like this?

diff --git a/src/adapter/src/coord/sequencer/inner/create_index.rs b/src/adapter/src/coord/sequencer/inner/create_index.rs
index e681f266ea..b78fd2a574 100644
--- a/src/adapter/src/coord/sequencer/inner/create_index.rs
+++ b/src/adapter/src/coord/sequencer/inner/create_index.rs
@@ -415,10 +415,12 @@ impl Coordinator {
                         .execute(builtin_table_updates)
                         .await;

+                    println!("{:?}", df_desc);
                     let ship_dataflow_fut = coord.ship_dataflow(df_desc, cluster_id);

                     futures::future::join(builtin_updates_fut, ship_dataflow_fut).await;
                 } else {
+                    println!("{:?}", df_desc);
                     coord.ship_dataflow(df_desc, cluster_id).await;
                 }
ggevay commented 7 months ago

Yes.

def- commented 7 months ago

They look something like this for each index:

limits-materialized-1     | environmentd: 2024-01-29T12:44:45.175322Z  INFO mz_adapter::catalog::state: create index materialize.public.i35 (u36)
limits-materialized-1     | DataflowDescription { source_imports: {}, index_imports: {User(2): IndexImport { desc: IndexDesc { on_id: User(1), key: [Column(0)] }, typ: RelationType { column_types: [ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { 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true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true
limits-materialized-1     |  }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, 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limits-materialized-1     | mnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }]) }, input_key: Some([Column(0)]), input_mfp: 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scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }, ColumnType { scalar_type: Int32, nullable: true }], keys: [] })}, sink_exports: {}, as_of: Some(Antichain { elements: [1706532283000] }), until: Antichain { elements: [] }, debug_name: "materialize.public.i35" }
limits-materialized-1     | environmentd: 2024-01-29T12:44:45.745296Z  INFO mz_compute_client::controller::instance: creating dataflow name=materialize.public.i35 import_ids=[u2] export_ids=[u36] as_of=[1706532283000] until=[]
ggevay commented 7 months ago

Thanks!

Hmm, so the number of columns in these relations is around a 1000, right? We need to store the type of each column at least twice (once in the IndexDesc, and once in the MirRelationExpr::Get that is reading the index's input), and it turns out that

size_of::<ColumnType>()

is 48, so this would explain ~100 KB for each index. But we are still missing at least 1 order of magnitude of memory usage.

def- commented 7 months ago

Correct.

aalexandrov commented 7 months ago

I am seeing normal memory consumption while the indexes are being created, and then excessive memory consumption while the 1000 selects are running.

The below is most probably irrelevant to the root cause because of this statement, but I'm still keeping it there in case anybody wonders about the DataflowDescription managed by CatalogPlans.

As Jan is saying, either we are keeping around a lot of DataflowDescriptions for some reason

We are keeping the DataflowDescription of each CREATE INDEX and CREATE PHYSICAL PLAN in memory in order to serve EXPLAIN [OPTIMIZED|PHYSICAL] PLAN FOR CREATE statements.

https://github.com/MaterializeInc/materialize/blob/765b63511a2a96f89f00ec4dbc86a4d7c1c4dc49/src/adapter/src/catalog.rs#L153-L189

I think counting the number of nodes in optimized_plan_by_id and physical_plan_by_id might be a good proxy for the memory allocated by that. Also, this memory should remain there even if the SELECT workload is completely excluded from your test (as long as the indexes are not dropped).

aalexandrov commented 7 months ago

@def- Can you also re-try the test with "enable_mz_notices": "false" set in DEFAULT_SYSTEM_PARAMETERS?

ggevay commented 7 months ago

We are keeping the DataflowDescription of each CREATE INDEX and CREATE PHYSICAL PLAN in memory in order to serve EXPLAIN [OPTIMIZED|PHYSICAL] PLAN FOR CREATE statements.

Ok, so then the DataflowDescription is kept in memory by 1. the Compute controller, 2. optimized_plan_by_id, 3. physical_plan_by_id. And each DataflowDescription has the RelationType 2 times. This explains ~300 KB for each index, so we are getting closer to the megabyte range. Maybe DataflowDescription has the RelationType a few more times for some reason, and then we are there.

Now the next question would be: is this too much? I'd say users probably won't have a 1000 indexes on relation that have a 1000 columns for now. E.g., https://github.com/MaterializeInc/accounts/issues/3 has a few dozen indexes on relations that have at most a few dozen columns, so they are more than 2 orders of magnitude away from hitting this problem.

def- commented 7 months ago

Still about 3 GB used even with enable_mz_notices disabled:

diff --git a/misc/python/materialize/mzcompose/__init__.py b/misc/python/materialize/mzcompose/__init__.py
index 94fdaf62c3..c555f2cd16 100644
--- a/misc/python/materialize/mzcompose/__init__.py
+++ b/misc/python/materialize/mzcompose/__init__.py
@@ -91,6 +91,7 @@ DEFAULT_SYSTEM_PARAMETERS = {
     "storage_source_decode_fuel": "100000",
     "timestamp_oracle": "postgres",
     "wait_catalog_consolidation_on_startup": "true",
+    "enable_mz_notices": "false",
 }

 DEFAULT_CRDB_ENVIRONMENT = [

https://gist.github.com/def-/4c94d193e415a4d6b7eba2fb6525084c I noticed that the mzfg profile is showing only 800 MB allocated though. It seems like jemalloc is just very slow at returning the memory when we use it quickly:

Allocated: 813.1 MiB Bytes unused, but retained by allocator: 4.8 GiB

This causes OoOms in CI, which makes me wonder if it's also a problem for customers. I previously played around with jemalloc options, maybe something like this helps: https://jemalloc.net/jemalloc.3.html#opt.dirty_decay_ms (_RJEM_MALLOC_CONF="background_thread:true,retain:true,dirty_decay_ms:100,muzzy_decay_ms:100,metadata_thp:auto") Screenshot 2024-01-29 at 16 02 18

jkosh44 commented 7 months ago

https://gist.github.com/def-/4c94d193e415a4d6b7eba2fb6525084c I noticed that the mzfg profile is showing only 800 MB allocated though. It seems like jemalloc is just very slow at returning the memory when we use it quickly:

Allocated: 813.1 MiB Bytes unused, but retained by allocator: 4.8 GiB

This causes OoOms in CI, which makes me wonder if it's also a problem for customers.

You're previous flamegraphs did have a large number of bytes allocated though.

Here's a flamegraph visualization during the CREATE INDEX: https://gist.github.com/def-/6ef520eab70c1f3732f1b2a649f3fb80

Allocated: 4.1 GiB In active pages: 4.1 GiB Allocated for allocator metadata: 82.2 MiB Bytes unused, but retained by allocator: 16.8 GiB

And here during the SELECT: https://gist.github.com/def-/925b2b6071655517080db4dbdba6490a

Allocated: 3.9 GiB In active pages: 4.0 GiB Allocated for allocator metadata: 69.9 MiB Bytes unused, but retained by allocator: 5.7 GiB

jkosh44 commented 7 months ago

I was able to repro the 3GB allocation during index creation. The first difference is that I ran bin/mzcompose --find limits down && bin/mzcompose --find limits run default --scenario=Indexes (i.e. in release mode) instead of bin/mzcompose --find limits down && bin/mzcompose --dev --find limits run default --scenario=Indexes (i.e. in debug mode). The second thing I noticed is that when I take a memory dump, the first dump always has a couple hundred MiB allocated and the second dump always has around 3 GiB allocated.

First: https://gist.github.com/jkosh44/55ab7a323f866c9a12fdd21313eb094e Second: https://gist.github.com/jkosh44/b382a950fe8da81ddf052709c7150519

It doesn't seem to matter when the first and second dump are taken, the behavior is pretty consistent for me. Those 3 GiB stay allocated throughout the index creations and the SELECTs. That makes me think the allocated bytes are actually related to the memory dump itself and not CREATE INDEX.

jkosh44 commented 7 months ago

If I wait for the CREATE INDEXs to be completely finished and the SELECTs to have started before getting a memory dump, I see the same behavior. The first memory dump is relatively small and the second is huge.

First: https://gist.github.com/jkosh44/08b80ef48f95755059df26f8edcb5aa1 Second: https://gist.github.com/jkosh44/d69327c522ebc57e74295fa9ef8cc1f8

jkosh44 commented 7 months ago

@def- Does the nightly run collect memory dumps?

def- commented 7 months ago

Currently not. And when I tried enabling it I also saw that just taking memory dumps makes many tests OoM: https://github.com/MaterializeInc/materialize/pull/24731

jkosh44 commented 6 months ago

@def- it looks like this has stopped happening in Nightlies. Would it be possible to perform some type of core/memory dump on OOMs in CI to better analyze what is happening?

Also the /prof/heap endpoint generates memory dumps without actually increasing the amount of memory Materialize uses. We should switch to that one for debugging purposes, but I'm still figuring out how to use it properly.

def- commented 6 months ago

I want to do that in https://github.com/MaterializeInc/materialize/pull/24731, if you can point me to how to use prof/heap then I'd do that for all mzcompose-based tests and we could analyze OoMs much easier.

jkosh44 commented 6 months ago

I want to do that in #24731, if you can point me to how to use prof/heap then I'd do that for all mzcompose-based tests and we could analyze OoMs much easier.

I am in the process of figuring this out myself. Though, I've been told that you use pprof to analyze the generated heap file.

jkosh44 commented 6 months ago

I want to do that in #24731, if you can point me to how to use prof/heap then I'd do that for all mzcompose-based tests and we could analyze OoMs much easier.

I am in the process of figuring this out myself. Though, I've been told that you use pprof to analyze the generated heap file.

This is probably not the best way of doing this but I found that it works:

  1. Run bin/mzcompose --find limits down && bin/mzcompose --find limits run default --scenario=Indexes.
  2. In a different terminal run bin/mzcompose --find limits exec materialized curl http://127.0.0.1:6878/prof/heap > heap.
  3. Run go tool pprof heap.
  4. Run top inside of the pprof CLI.

I haven't figured out how to import debug symbols or get useful breakdowns, but top at least shows the total memory being used. I was not able to repro this issue and the total memory never even reached up to 1GiB.

def- commented 6 months ago

I am now under the impression that it may have been a fluke that environmentd got killed. Recently, both on main and v0.87.1 we have seen clusterd getting killed because of OoM in the same Indexes scenario: https://buildkite.com/materialize/nightlies/builds/6370#018d8f1e-a536-4c2c-a8a4-bd216a97c5d2 Edit: I created a new issue for that: https://github.com/MaterializeInc/materialize/issues/25153