Merge branch 'martin-seaweed-s3' into 'master'

notes on seaweedfs (s3 backend)

See merge request webgroup/sandcrawler!28

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+# Notes on seaweedfs
+
+> 2020-04-28, martin@archive.org
+
+Currently (04/2020) [minio](https://github.com/minio/minio) is used to store
+output from PDF analysis for [fatcat](https://fatcat.wiki) (e.g. from
+[grobid](https://grobid.readthedocs.io/en/latest/)). The file checksum (sha1)
+serves as key, values are blobs of XML or JSON.
+
+Problem: minio inserts slowed down after inserting 80M or more objects.
+
+Summary: I did four test runs, three failed, one (testrun-4) succeeded.
+
+* [testrun-4](https://git.archive.org/webgroup/sandcrawler/-/blob/martin-seaweed-s3/proposals/2020_seaweed_s3.md#testrun-4)
+
+So far, in a non-distributed mode, the project looks usable. Added 200M objects
+(about 550G) in 6 days. Full CPU load, 400M RAM usage, constant insert times.
+
+----
+
+Details (03/2020) / @bnewbold, slack
+
+> the sandcrawler XML data store (currently on aitio) is grinding to a halt, I
+> think because despite tuning minio+ext4+hdd just doesn't work. current at 2.6
+> TiB of data (each document compressed with snappy) and 87,403,183 objects.
+
+> this doesn't impact ingest processing (because content is queued and archived
+> in kafka), but does impact processing and analysis
+
+> it is possible that the other load on aitio is making this worse, but I did
+> an experiment with dumping to a 16 TB disk that slowed way down after about
+> 50 million files also. some people on the internet said to just not worry
+> about these huge file counts on modern filesystems, but i've debugged a bit
+> and I think it is a bad idea after all
+
+Possible solutions
+
+* putting content in fake WARCs and trying to do something like CDX
+* deploy CEPH object store (or swift, or any other off-the-shelf object store)
+* try putting the files in postgres tables, mongodb, cassandra, etc: these are
+  not designed for hundreds of millions of ~50 KByte XML documents (5 - 500
+  KByte range)
+* try to find or adapt an open source tool like Haystack, Facebook's solution
+  to this engineering problem. eg:
+  https://engineering.linkedin.com/blog/2016/05/introducing-and-open-sourcing-ambry---linkedins-new-distributed-
+
+----
+
+The following are notes gathered during a few test runs of seaweedfs in 04/2020
+on wbgrp-svc170.us.archive.org (4 core E5-2620 v4, 4GB RAM).
+
+----
+
+## Setup
+
+There are frequent [releases](https://github.com/chrislusf/seaweedfs/releases)
+but for the test, we used a build off the master branch.
+
+Directions from configuring AWS CLI for seaweedfs:
+[https://github.com/chrislusf/seaweedfs/wiki/AWS-CLI-with-SeaweedFS](https://github.com/chrislusf/seaweedfs/wiki/AWS-CLI-with-SeaweedFS).
+
+### Build the binary
+
+Using development version (requires a [Go installation](https://golang.org/dl/)).
+
+```
+$ git clone git@github.com:chrislusf/seaweedfs.git # 11f5a6d9
+$ cd seaweedfs
+$ make
+$ ls -lah weed/weed
+-rwxr-xr-x 1 tir tir 55M Apr 17 16:57 weed
+
+$ git rev-parse HEAD
+11f5a6d91346e5f3cbf3b46e0a660e231c5c2998
+
+$ sha1sum weed/weed
+a7f8f0b49e6183da06fc2d1411c7a0714a2cc96b
+```
+
+A single, 55M binary emerges after a few seconds. The binary contains
+subcommands to run different parts of seaweed, e.g. master or volume servers,
+filer and commands for maintenance tasks, like backup and compact.
+
+To *deploy*, just copy this binary to the destination.
+
+### Quickstart with S3
+
+Assuming `weed` binary is in PATH.
+
+Start a master and volume server (over /tmp, most likely) and the S3 API with a single command:
+
+```
+$ weed -server s3
+...
+Start Seaweed Master 30GB 1.74 at 0.0.0.0:9333
+...
+Store started on dir: /tmp with 0 volumes max 7
+Store started on dir: /tmp with 0 ec shards
+Volume server start with seed master nodes: [localhost:9333]
+...
+Start Seaweed S3 API Server 30GB 1.74 at http port 8333
+...
+```
+
+Install the [AWS
+CLI](https://github.com/chrislusf/seaweedfs/wiki/AWS-CLI-with-SeaweedFS).
+Create a bucket.
+
+```
+$ aws --endpoint-url http://localhost:8333 s3 mb s3://sandcrawler-dev
+make_bucket: sandcrawler-dev
+```
+
+List buckets.
+
+```
+$ aws --endpoint-url http://localhost:8333 s3 ls
+2020-04-17 17:44:39 sandcrawler-dev
+```
+
+Create a dummy file.
+
+```
+$ echo "blob" > 12340d9a4a4f710ecf03b127051814385e83ff08.tei.xml
+```
+
+Upload.
+
+```
+$ aws --endpoint-url http://localhost:8333 s3 cp 12340d9a4a4f710ecf03b127051814385e83ff08.tei.xml s3://sandcrawler-dev
+upload: ./12340d9a4a4f710ecf03b127051814385e83ff08.tei.xml to s3://sandcrawler-dev/12340d9a4a4f710ecf03b127051814385e83ff08.tei.xml
+```
+
+List.
+
+```
+$ aws --endpoint-url http://localhost:8333 s3 ls s3://sandcrawler-dev
+2020-04-17 17:50:35          5 12340d9a4a4f710ecf03b127051814385e83ff08.tei.xml
+```
+
+Stream to stdout.
+
+```
+$ aws --endpoint-url http://localhost:8333 s3 cp s3://sandcrawler-dev/12340d9a4a4f710ecf03b127051814385e83ff08.tei.xml -
+blob
+```
+
+Drop the bucket.
+
+```
+$ aws --endpoint-url http://localhost:8333 s3 rm --recursive s3://sandcrawler-dev
+```
+
+### Builtin benchmark
+
+The project comes with a builtin benchmark command.
+
+```
+$ weed benchmark
+```
+
+I encountered an error like
+[#181](https://github.com/chrislusf/seaweedfs/issues/181), "no free volume
+left" - when trying to start the benchmark after the S3 ops. A restart or a restart with `-volume.max 100` helped.
+
+```
+$ weed server -s3 -volume.max 100
+```
+
+### Listing volumes
+
+```
+$ weed shell
+> volume.list
+Topology volume:15/112757 active:8 free:112742 remote:0 volumeSizeLimit:100 MB
+  DataCenter DefaultDataCenter volume:15/112757 active:8 free:112742 remote:0
+    Rack DefaultRack volume:15/112757 active:8 free:112742 remote:0
+      DataNode localhost:8080 volume:15/112757 active:8 free:112742 remote:0
+        volume id:1 size:105328040 collection:"test" file_count:33933 version:3 modified_at_second:1587215730
+        volume id:2 size:106268552 collection:"test" file_count:34236 version:3 modified_at_second:1587215730
+        volume id:3 size:106290280 collection:"test" file_count:34243 version:3 modified_at_second:1587215730
+        volume id:4 size:105815368 collection:"test" file_count:34090 version:3 modified_at_second:1587215730
+        volume id:5 size:105660168 collection:"test" file_count:34040 version:3 modified_at_second:1587215730
+        volume id:6 size:106296488 collection:"test" file_count:34245 version:3 modified_at_second:1587215730
+        volume id:7 size:105753288 collection:"test" file_count:34070 version:3 modified_at_second:1587215730
+        volume id:8 size:7746408 file_count:12 version:3 modified_at_second:1587215764
+        volume id:9 size:10438760 collection:"test" file_count:3363 version:3 modified_at_second:1587215788
+        volume id:10 size:10240104 collection:"test" file_count:3299 version:3 modified_at_second:1587215788
+        volume id:11 size:10258728 collection:"test" file_count:3305 version:3 modified_at_second:1587215788
+        volume id:12 size:10240104 collection:"test" file_count:3299 version:3 modified_at_second:1587215788
+        volume id:13 size:10112840 collection:"test" file_count:3258 version:3 modified_at_second:1587215788
+        volume id:14 size:10190440 collection:"test" file_count:3283 version:3 modified_at_second:1587215788
+        volume id:15 size:10112840 collection:"test" file_count:3258 version:3 modified_at_second:1587215788
+      DataNode localhost:8080 total size:820752408 file_count:261934
+    Rack DefaultRack total size:820752408 file_count:261934
+  DataCenter DefaultDataCenter total size:820752408 file_count:261934
+total size:820752408 file_count:261934
+```
+
+### Custom S3 benchmark
+
+To simulate the use case of S3 use case for 100-500M small files (grobid xml,
+pdftotext, ...), I created a synthetic benchmark.
+
+* [https://gist.github.com/miku/6f3fee974ba82083325c2f24c912b47b](https://gist.github.com/miku/6f3fee974ba82083325c2f24c912b47b)
+
+We just try to fill up the datastore with millions of 5k blobs.
+
+----
+
+### testrun-1
+
+Small set, just to run. Status: done. Learned that the default in memory volume
+index grows too quickly for the 4GB machine.
+
+```
+$ weed server -dir /tmp/martin-seaweedfs-testrun-1 -s3 -volume.max 512 -master.volumeSizeLimitMB 100
+```
+
+* https://github.com/chrislusf/seaweedfs/issues/498 -- RAM
+* at 10M files, we already consume ~1G
+
+```
+-volume.index string
+        Choose [memory|leveldb|leveldbMedium|leveldbLarge] mode for memory~performance balance. (default "memory")
+```
+
+### testrun-2
+
+200M 5k objects, in-memory volume index. Status: done. Observed: After 18M
+objects the 512 100MB volumes are exhausted and seaweedfs will not accept any
+new data.
+
+```
+$ weed server -dir /tmp/martin-seaweedfs-testrun-2 -s3 -volume.max 512 -master.volumeSizeLimitMB 100
+...
+I0418 12:01:43  1622 volume_loading.go:104] loading index /tmp/martin-seaweedfs-testrun-2/test_511.idx to memory
+I0418 12:01:43  1622 store.go:122] add volume 511
+I0418 12:01:43  1622 volume_layout.go:243] Volume 511 becomes writable
+I0418 12:01:43  1622 volume_growth.go:224] Created Volume 511 on topo:DefaultDataCenter:DefaultRack:localhost:8080
+I0418 12:01:43  1622 master_grpc_server.go:158] master send to master@[::1]:45084: url:"localhost:8080" public_url:"localhost:8080" new_vids:511
+I0418 12:01:43  1622 master_grpc_server.go:158] master send to filer@::1:18888: url:"localhost:8080" public_url:"localhost:8080" new_vids:511
+I0418 12:01:43  1622 store.go:118] In dir /tmp/martin-seaweedfs-testrun-2 adds volume:512 collection:test replicaPlacement:000 ttl:
+I0418 12:01:43  1622 volume_loading.go:104] loading index /tmp/martin-seaweedfs-testrun-2/test_512.idx to memory
+I0418 12:01:43  1622 store.go:122] add volume 512
+I0418 12:01:43  1622 volume_layout.go:243] Volume 512 becomes writable
+I0418 12:01:43  1622 master_grpc_server.go:158] master send to master@[::1]:45084: url:"localhost:8080" public_url:"localhost:8080" new_vids:512
+I0418 12:01:43  1622 master_grpc_server.go:158] master send to filer@::1:18888: url:"localhost:8080" public_url:"localhost:8080" new_vids:512
+I0418 12:01:43  1622 volume_growth.go:224] Created Volume 512 on topo:DefaultDataCenter:DefaultRack:localhost:8080
+I0418 12:01:43  1622 node.go:82] topo failed to pick 1 from  0 node candidates
+I0418 12:01:43  1622 volume_growth.go:88] create 7 volume, created 2: No enough data node found!
+I0418 12:04:30  1622 volume_layout.go:231] Volume 511 becomes unwritable
+I0418 12:04:30  1622 volume_layout.go:231] Volume 512 becomes unwritable
+E0418 12:04:30  1622 filer_server_handlers_write.go:69] failing to assign a file id: rpc error: code = Unknown desc = No free volumes left!
+I0418 12:04:30  1622 filer_server_handlers_write.go:120] fail to allocate volume for /buckets/test/k43731970, collection:test, datacenter:
+E0418 12:04:30  1622 filer_server_handlers_write.go:69] failing to assign a file id: rpc error: code = Unknown desc = No free volumes left!
+E0418 12:04:30  1622 filer_server_handlers_write.go:69] failing to assign a file id: rpc error: code = Unknown desc = No free volumes left!
+E0418 12:04:30  1622 filer_server_handlers_write.go:69] failing to assign a file id: rpc error: code = Unknown desc = No free volumes left!
+E0418 12:04:30  1622 filer_server_handlers_write.go:69] failing to assign a file id: rpc error: code = Unknown desc = No free volumes left!
+I0418 12:04:30  1622 masterclient.go:88] filer failed to receive from localhost:9333: rpc error: code = Unavailable desc = transport is closing
+I0418 12:04:30  1622 master_grpc_server.go:276] - client filer@::1:18888
+```
+
+Inserted about 18M docs, then:
+
+```
+worker-0        @3720000        45475.13        81.80
+worker-1        @3730000        45525.00        81.93
+worker-3        @3720000        45525.76        81.71
+worker-4        @3720000        45527.22        81.71
+Process Process-1:
+Traceback (most recent call last):
+  File "/usr/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
+    self.run()
+  File "/usr/lib/python3.5/multiprocessing/process.py", line 93, in run
+    self._target(*self._args, **self._kwargs)
+  File "s3test.py", line 42, in insert_keys
+    s3.Bucket(bucket).put_object(Key=key, Body=data)
+  File "/home/martin/.virtualenvs/6f3fee974ba82083325c2f24c912b47b/lib/python3.5/site-packages/boto3/resources/factory.py", line 520, in do_action
+    response = action(self, *args, **kwargs)
+  File "/home/martin/.virtualenvs/6f3fee974ba82083325c2f24c912b47b/lib/python3.5/site-packages/boto3/resources/action.py", line 83, in __call__
+    response = getattr(parent.meta.client, operation_name)(**params)
+  File "/home/martin/.virtualenvs/6f3fee974ba82083325c2f24c912b47b/lib/python3.5/site-packages/botocore/client.py", line 316, in _api_call
+    return self._make_api_call(operation_name, kwargs)
+  File "/home/martin/.virtualenvs/6f3fee974ba82083325c2f24c912b47b/lib/python3.5/site-packages/botocore/client.py", line 626, in _make_api_call
+    raise error_class(parsed_response, operation_name)
+botocore.exceptions.ClientError: An error occurred (InternalError) when calling the PutObject operation (reached max retries: 4): We encountered an internal error, please try again.
+
+real    759m30.034s
+user    1962m47.487s
+sys     105m21.113s
+```
+
+Sustained 400 S3 puts/s, RAM usage 41% of a 4G machine. 56G on disk.
+
+> No free volumes left! Failed to allocate bucket for /buckets/test/k163721819
+
+### testrun-3
+
+* use leveldb, leveldbLarge
+* try "auto" volumes
+* Status: done. Observed: rapid memory usage.
+
+```
+$ weed server -dir /tmp/martin-seaweedfs-testrun-3 -s3 -volume.max 0 -volume.index=leveldbLarge -filer=false -master.volumeSizeLimitMB 100
+```
+
+Observations: memory usage grows rapidly, soon at 15%.
+
+Note-to-self: [https://github.com/chrislusf/seaweedfs/wiki/Optimization](https://github.com/chrislusf/seaweedfs/wiki/Optimization)
+
+### testrun-4
+
+The default volume size is 30G (and cannot be more at the moment), and RAM
+grows very much with the number of volumes. Therefore, keep default volume size
+and do not limit number of volumes `-volume.max 0` and do not use in-memory
+index (rather leveldb)
+
+Status: done, 200M object upload via Python script sucessfully in about 6 days,
+memory usage was at a moderate 400M (~10% of RAM). Relatively constant
+performance at about 400 `PutObject` requests/s (over 5 threads, each thread
+was around 80 requests/s; then testing with 4 threads, each thread got to
+around 100 requests/s).
+
+```
+$ weed server -dir /tmp/martin-seaweedfs-testrun-4 -s3 -volume.max 0 -volume.index=leveldb
+```
+
+The test script command was (40M files per worker, 5 workers).
+
+```
+$ time python s3test.py -n 40000000 -w 5 2> s3test.4.log
+...
+
+real    8454m33.695s
+user    21318m23.094s
+sys     1128m32.293s
+```
+
+The test script adds keys from `k0...k199999999`.
+
+```
+$ aws --endpoint-url http://localhost:8333 s3 ls s3://test | head -20
+2020-04-19 09:27:13       5000 k0
+2020-04-19 09:27:13       5000 k1
+2020-04-19 09:27:13       5000 k10
+2020-04-19 09:27:15       5000 k100
+2020-04-19 09:27:26       5000 k1000
+2020-04-19 09:29:15       5000 k10000
+2020-04-19 09:47:49       5000 k100000
+2020-04-19 12:54:03       5000 k1000000
+2020-04-20 20:14:10       5000 k10000000
+2020-04-22 07:33:46       5000 k100000000
+2020-04-22 07:33:46       5000 k100000001
+2020-04-22 07:33:46       5000 k100000002
+2020-04-22 07:33:46       5000 k100000003
+2020-04-22 07:33:46       5000 k100000004
+2020-04-22 07:33:46       5000 k100000005
+2020-04-22 07:33:46       5000 k100000006
+2020-04-22 07:33:46       5000 k100000007
+2020-04-22 07:33:46       5000 k100000008
+2020-04-22 07:33:46       5000 k100000009
+2020-04-20 20:14:10       5000 k10000001
+```
+
+Glance at stats.
+
+```
+$ du -hs /tmp/martin-seaweedfs-testrun-4
+596G    /tmp/martin-seaweedfs-testrun-4
+
+$ find . /tmp/martin-seaweedfs-testrun-4 | wc -l
+5104
+
+$ ps --pid $(pidof weed) -o pid,tid,class,stat,vsz,rss,comm
+  PID   TID CLS STAT    VSZ   RSS COMMAND
+32194 32194 TS  Sl+  1966964 491644 weed
+
+$ ls -1 /proc/$(pidof weed)/fd | wc -l
+192
+
+$ free -m
+              total        used        free      shared  buff/cache   available
+Mem:           3944         534         324          39        3086        3423
+Swap:          4094          27        4067
+```
+
+### Note on restart
+
+When stopping (CTRL-C) and restarting `weed` it will take about 10 seconds to
+get the S3 API server back up, but another minute or two, until seaweedfs
+inspects all existing volumes and indices.
+
+In that gap, requests to S3 will look like internal server errors.
+
+```
+$ aws --endpoint-url http://localhost:8333 s3 cp s3://test/k100 -
+download failed: s3://test/k100 to - An error occurred (500) when calling the
+GetObject operation (reached max retries: 4): Internal Server Error
+```
+
+### Read benchmark
+
+Reading via command line `aws` client is a bit slow at first sight (3-5s).
+
+```
+$ time aws --endpoint-url http://localhost:8333 s3 cp s3://test/k123456789 -
+ppbhjgzkrrgwagmjsuwhqcwqzmefybeopqz [...]
+
+real    0m5.839s
+user    0m0.898s
+sys     0m0.293s
+```
+
+#### Single process random reads
+
+Running 1000 random reads takes 49s.
+
+#### Concurrent random reads
+
+* 80000 request with 8 parallel processes: 7m41.973968488s, so about 170 objects/s)
+* seen up to 760 keys/s reads for 8 workers
+* weed will utilize all cores, so more cpus could result in higher read throughput
+* RAM usage can increase (seen up to 20% of 4G RAM), then descrease (GC) back to 5%, depending on query load