Janusgraph not using index in production - indexing
Problem
When performing queries in my production environment, the index is not being used and a full scan is performed, but my development environment works fine and uses the index.
After looking deeper at the problem in production, it also seems that the index information is being saved to the storage backend, but the data is not, and is being stored locally. I have no idea why this is...
I will explain the architecture now:
Environments
The following describe my two environments. Important to note, the index in question is a composite index, as such uses the storage backend, but I still included the index-backend in the architecture environment (aka Elasticsearch).
Both local and production environment versions are the same, i.e:
Janusgraph: 0.5.2
ScyllaDB: 0.5.2
Elasticsearch: 7.13.1
Local Environment
Services are running in docker-compose, consisting of a single Janusgraph instance, a single ScyllaDB instance, and a single Elasticsearch Instance.
Production Environment
Running on AWS, kubernetes cluster managed with EKS, I have multiple janusgraph deployments, which connect to a ScyllaDB cluster (in the same k8s cluster), which is done via Scylla For Kubernetes (https://operator.docs.scylladb.com/stable/), and an Elasticsearch cluster.
Setup
The following will give the simplest example I can that contains the problems I describe.
I pre-create the index's with the Janusgraph management system, such as:
# management.groovy
import org.janusgraph.graphdb.database.management.ManagementSystem
cluster = Cluster.open("/opt/janusgraph/my_scripts/gremlin.yaml")
client = cluster.connect()
graph = JanusGraphFactory.open("/opt/janusgraph/my_scripts/env.properties")
g = graph.traversal().withRemote(DriverRemoteConnection.using(client, "g"))
m = graph.openManagement()
uid_property = m.makePropertyKey("uid").dataType(String).make()
user_label = m.makeVertexLabel("User").make()
m.buildIndex("index::User::uid", Vertex.class).addKey(uid_property).indexOnly(user_label).buildCompositeIndex()
m.commit()
Upon inspection with m.printSchema() I can see that the index's are ENABLED, in both my local environment and production environment.
I proceed to import all the data that needs to exist on the graph, both local env and production env are OK.
Performing Queries
The following outline what happens when I run a query
Local Environment
What we see here is a simple lookup just to check that the query is using the index:
gremlin> g.V().has("User", "uid", "00003b90-dcc2-494d-a179-ac9009029501").profile()
==>Traversal Metrics
Step Count Traversers Time (ms) % Dur
=============================================================================================================
JanusGraphStep([],[~label.eq(User), uid.eq(... 1 1 1.837 100.00
\_condition=(~label = User AND uid = 00003b90-dcc2-494d-a179-ac9009029501)
\_isFitted=true
\_query=multiKSQ[1]#4000
\_index=index::User::uid
\_orders=[]
\_isOrdered=true
optimization 0.038
optimization 0.497
backend-query 1 0.901
\_query=index::User::uid:multiKSQ[1]#4000
\_limit=4000
>TOTAL - - 1.837 -
Production Environment
Again, we run the query to see if it using the index (which it is not)
g.V().has("User", "uid", "00003b90-dcc2-494d-a179-ac9009029501").profile()
==>Traversal Metrics
Step Count Traversers Time (ms) % Dur
=============================================================================================================
JanusGraphStep([],[~label.eq(User), uid.eq(... 1 1 11296.568 100.00
\_condition=(~label = User AND uid = 00003b90-dcc2-494d-a179-ac9009029501)
\_isFitted=false
\_query=[]
\_orders=[]
\_isOrdered=true
optimization 0.025
optimization 0.102
scan 0.000
\_condition=VERTEX
\_query=[]
\_fullscan=true
>TOTAL - - 11296.568 -
What Happened? So far my best guess:
The storage backend is NOT being used for storing data, but is being used for storing information about the indexes
Update: Aug 16 2021, after digging around some more I found out something interesting
It is now clear that the data is actually not being saved to the storage backend at all.
In my local environment I set the storage.directory environment variable to /var/lib/janusgraph/data, which mounts onto an empty directory, this directory remains empty. Any vertex/edge updates get's saved to the scyllaDB storage backend, and the data persists between janusgraph instance restarts.
In my production environment, this directory (/var/lib/janusgraph/data) is populated with files:
-rw-r--r-- 1 janusgraph janusgraph 0 Aug 16 05:46 je.lck
-rw-r--r-- 1 janusgraph janusgraph 9650 Aug 16 05:46 je.config.csv
-rw-r--r-- 1 janusgraph janusgraph 450 Aug 16 05:46 je.info.0
-rw-r--r-- 1 janusgraph janusgraph 0 Aug 16 05:46 je.info.0.lck
drwxr-xr-x 2 janusgraph janusgraph 118 Aug 16 05:46 .
-rw-r--r-- 1 janusgraph janusgraph 7533 Aug 16 05:46 00000000.jdb
drwx------ 1 janusgraph janusgraph 75 Aug 16 05:53 ..
-rw-r--r-- 1 janusgraph janusgraph 19951 Aug 16 06:09 je.stat.csv
and any subsequent updates on the graph seem to be reflected here, the update do not get put onto the storage backend, and other janusgraph instances on kubernetes cannot see any changes other instances make, leading me to come to the conclusion, the storage backend is not being used for storing data
The domain name used for the storage.hostname and index.hostname both resolve to IP address's, confirmed with using nslookup.
The endpoints must also work, as the keyspace janusgraph is created, and also has a different replication factor that I defined, and also retains the index information regardless of restarting the janusgraph instances.
Idea 1 (Index is not enabled)
This was disproved via running m.printSchema() showing that all the index's were ENABLED
Idea 2 (Storage backends have different data)
I looked at the data stored in scylladb, and got a summary with nodetool cfstats, this does show something different:
# Local
Keyspace : janusgraph
Read Count: 1688328
Read Latency: 2.5682805710738673E-5 ms
Write Count: 1055210
Write Latency: 1.702409946835227E-5 ms
...
Memtable cell count: 126411
Memtable data size: 345700491
Memtable off heap memory used: 480247808
# Production
Keyspace : janusgraph
Read Count: 6367
Read Latency: 2.1203078372860058E-5 ms
Write Count: 21
Write Latency: 0.0 ms
...
Memtable cell count: 4
Memtable data size: 10092
Memtable off heap memory used: 131072
Although I don't know how to explain the difference, it is clear that both backends contain all the data, verified with various count() queries over labels, such as g.V().hasLabel("User").count(), which both environments report the same result
Idea 3 (Elasticsearch Warnings)
When launching a gremlin console session, there is a difference in that the production environment shows:
07:27:09 WARN org.elasticsearch.client.RestClient - request [PUT http://*******<i_removed_the_domain>******:9200/_cluster/settings] returned 3 warnings: [299 Elasticsearch-7.13.4-c5f60e894ca0c61cdbae4f5a686d9f08bcefc942 "[node.data] setting was deprecated in Elasticsearch and will be removed in a future release! See the breaking changes documentation for the next major version."],[299 Elasticsearch-7.13.4-c5f60e894ca0c61cdbae4f5a686d9f08bcefc942 "[node.master] setting was deprecated in Elasticsearch and will be removed in a future release! See the breaking changes documentation for the next major version."],[299 Elasticsearch-7.13.4-c5f60e894ca0c61cdbae4f5a686d9f08bcefc942 "[node.ml] setting was deprecated in Elasticsearch and will be removed in a future release! See the breaking changes documentation for the next major version."]
but as my problem is using composite index's, I believe we can disregard elasticsearch warnings.
Idea 4 (ScyllaDB cluster node resources)
Another idea I had was increasing the node resources, even with 7gb RAM, the problem still persists.
Finally...
I don't know what to try next in order to solve this problem, this is my first time pushing Janusgraph into production and perhaps I have missed something important. I have been stuck on this problem for quite a while, hence now asking the community here for help.
Thank you very much for reading this for, and hopefully helping me to solve this problem
I solved the problem myself, I realised that my K8s Deployment .yaml file I use for deploying needed all environment variables to have the prefix janusgraph., as such the janusgraph server was starting with all default variables rather than my selected ones.
Every-time I was creating a gremlin shell session (which connected to it's localhost server), although I was specifying all the correct endpoints and configuration, it was still saving the data according to default janusgraph variables. Although, even in this case, I don't know why the index's were successfully created on my specified backend.
But none the less, the solution was to make sure environment variables have the prefix janusgraph.
Related
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Wrong balance between Aerospike instances in cluster
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Edit 1 All servers in the cluster have the same identical aerospike.conf file: Aerospike database configuration file. service { user root group root paxos-single-replica-limit 1 # Number of nodes where the replica count is automatically reduced to 1. paxos-recovery-policy auto-reset-master pidfile /var/run/aerospike/asd.pid service-threads 32 transaction-queues 32 transaction-threads-per-queue 32 batch-index-threads 32 proto-fd-max 15000 batch-max-requests 200000 } logging { # Log file must be an absolute path. file /var/log/aerospike/aerospike.log { context any info } } network { service { #address any port 3000 } heartbeat { mode mesh mesh-seed-address-port 10.240.0.6 3002 mesh-seed-address-port 10.240.0.5 3002 port 3002 interval 150 timeout 20 } fabric { port 3001 } info { port 3003 } } namespace test { replication-factor 3 memory-size 5G default-ttl 0 # 30 days, use 0 to never expire/evict. ldt-enabled true storage-engine device { file /data/aerospike.dat write-block-size 1M filesize 180G } } Edit 2: $ asinfo 1 : node BB90600F00A0142 2 : statistics cluster_size=14;cluster_key=E3C3672DCDD7F51;cluster_integrity=true;objects=3739898;sub-records=0;total-bytes-disk=193273528320;used-bytes-disk=26018492544;free-pct-disk=86;total-bytes-memory=5368709120;used-bytes-memory=239353472;data-used-bytes-memory=0;index-used-bytes-memory=239353472;sindex-used-bytes-memory=0;free-pct-memory=95;stat_read_reqs=2881465329;stat_read_reqs_xdr=0;stat_read_success=2878457632;stat_read_errs_notfound=3007093;stat_read_errs_other=0;stat_write_reqs=551398;stat_write_reqs_xdr=0;stat_write_success=549522;stat_write_errs=90;stat_xdr_pipe_writes=0;stat_xdr_pipe_miss=0;stat_delete_success=4;stat_rw_timeout=1862;udf_read_reqs=0;udf_read_success=0;udf_read_errs_other=0;udf_write_reqs=0;udf_write_success=0;udf_write_err_others=0;udf_delete_reqs=0;udf_delete_success=0;udf_delete_err_others=0;udf_lua_errs=0;udf_scan_rec_reqs=0;udf_query_rec_reqs=0;udf_replica_writes=0;stat_proxy_reqs=7021;stat_proxy_reqs_xdr=0;stat_proxy_success=2121;stat_proxy_errs=4739;stat_ldt_proxy=0;stat_cluster_key_err_ack_dup_trans_reenqueue=607;stat_expired_objects=0;stat_evicted_objects=0;stat_deleted_set_objects=0;stat_evicted_objects_time=0;stat_zero_bin_records=0;stat_nsup_deletes_not_shipped=0;stat_compressed_pkts_received=0;err_tsvc_requests=110;err_tsvc_requests_timeout=0;err_out_of_space=0;err_duplicate_proxy_request=0;err_rw_request_not_found=17;err_rw_pending_limit=19;err_rw_cant_put_unique=0;geo_region_query_count=0;geo_region_query_cells=0;geo_region_query_points=0;geo_region_query_falsepos=0;fabric_msgs_sent=58002818;fabric_msgs_rcvd=57998870;paxos_principal=BB92B00F00A0142;migrate_msgs_sent=55749290;migrate_msgs_recv=55759692;migrate_progress_send=0;migrate_progress_recv=0;migrate_num_incoming_accepted=7228;migrate_num_incoming_refused=0;queue=0;transactions=101978550;reaped_fds=6;scans_active=0;basic_scans_succeeded=0;basic_scans_failed=0;aggr_scans_succeeded=0;aggr_scans_failed=0;udf_bg_scans_succeeded=0;udf_bg_scans_failed=0;batch_index_initiate=40457778;batch_index_queue=0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0,0:0;batch_index_complete=40456708;batch_index_timeout=1037;batch_index_errors=33;batch_index_unused_buffers=256;batch_index_huge_buffers=217168717;batch_index_created_buffers=217583519;batch_index_destroyed_buffers=217583263;batch_initiate=0;batch_queue=0;batch_tree_count=0;batch_timeout=0;batch_errors=0;info_queue=0;delete_queue=0;proxy_in_progress=0;proxy_initiate=7021;proxy_action=5519;proxy_retry=0;proxy_retry_q_full=0;proxy_unproxy=0;proxy_retry_same_dest=0;proxy_retry_new_dest=0;write_master=551089;write_prole=1055431;read_dup_prole=14232;rw_err_dup_internal=0;rw_err_dup_cluster_key=1814;rw_err_dup_send=0;rw_err_write_internal=0;rw_err_write_cluster_key=0;rw_err_write_send=0;rw_err_ack_internal=0;rw_err_ack_nomatch=1767;rw_err_ack_badnode=0;client_connections=366;waiting_transactions=0;tree_count=0;record_refs=3739898;record_locks=0;migrate_tx_objs=0;migrate_rx_objs=0;ongoing_write_reqs=0;err_storage_queue_full=0;partition_actual=296;partition_replica=572;partition_desync=0;partition_absent=3228;partition_zombie=0;partition_object_count=3739898;partition_ref_count=4096;system_free_mem_pct=61;sindex_ucgarbage_found=0;sindex_gc_locktimedout=0;sindex_gc_inactivity_dur=0;sindex_gc_activity_dur=0;sindex_gc_list_creation_time=0;sindex_gc_list_deletion_time=0;sindex_gc_objects_validated=0;sindex_gc_garbage_found=0;sindex_gc_garbage_cleaned=0;system_swapping=false;err_replica_null_node=0;err_replica_non_null_node=0;err_sync_copy_null_master=0;storage_defrag_corrupt_record=0;err_write_fail_prole_unknown=0;err_write_fail_prole_generation=0;err_write_fail_unknown=0;err_write_fail_key_exists=0;err_write_fail_generation=0;err_write_fail_generation_xdr=0;err_write_fail_bin_exists=0;err_write_fail_parameter=0;err_write_fail_incompatible_type=0;err_write_fail_noxdr=0;err_write_fail_prole_delete=0;err_write_fail_not_found=0;err_write_fail_key_mismatch=0;err_write_fail_record_too_big=90;err_write_fail_bin_name=0;err_write_fail_bin_not_found=0;err_write_fail_forbidden=0;stat_duplicate_operation=53184;uptime=1001388;stat_write_errs_notfound=0;stat_write_errs_other=90;heartbeat_received_self=0;heartbeat_received_foreign=145137042;query_reqs=0;query_success=0;query_fail=0;query_abort=0;query_avg_rec_count=0;query_short_running=0;query_long_running=0;query_short_queue_full=0;query_long_queue_full=0;query_short_reqs=0;query_long_reqs=0;query_agg=0;query_agg_success=0;query_agg_err=0;query_agg_abort=0;query_agg_avg_rec_count=0;query_lookups=0;query_lookup_success=0;query_lookup_err=0;query_lookup_abort=0;query_lookup_avg_rec_count=0 3 : features cdt-list;pipelining;geo;float;batch-index;replicas-all;replicas-master;replicas-prole;udf 4 : cluster-generation 61 5 : partition-generation 11811 6 : edition Aerospike Community Edition 7 : version Aerospike Community Edition build 3.7.2 8 : build 3.7.2 9 : services 10.0.3.1:3000;10.240.0.14:3000;10.0.3.1:3000;10.240.0.27:3000;10.0.3.1:3000;10.240.0.5:3000;10.0.3.1:3000;10.240.0.43:3000;10.0.3.1:3000;10.240.0.30:3000;10.0.3.1:3000;10.240.0.18:3000;10.0.3.1:3000;10.240.0.42:3000;10.0.3.1:3000;10.240.0.33:3000;10.0.3.1:3000;10.240.0.24:3000;10.0.3.1:3000;10.240.0.37:3000;10.0.3.1:3000;10.240.0.41:3000;10.0.3.1:3000;10.240.0.13:3000;10.0.3.1:3000;10.240.0.23:3000 10 : services-alumni 10.0.3.1:3000;10.240.0.42:3000;10.0.3.1:3000;10.240.0.5:3000;10.0.3.1:3000;10.240.0.13:3000;10.0.3.1:3000;10.240.0.14:3000;10.0.3.1:3000;10.240.0.18:3000;10.0.3.1:3000;10.240.0.23:3000;10.0.3.1:3000;10.240.0.24:3000;10.0.3.1:3000;10.240.0.27:3000;10.0.3.1:3000;10.240.0.30:3000;10.0.3.1:3000;10.240.0.37:3000;10.0.3.1:3000;10.240.0.43:3000;10.0.3.1:3000;10.240.0.33:3000;10.0.3.1:3000;10.240.0.41:3000
I have a few comments about your configuration. First, transaction-threads-per-queue should be set to 3 or 4 (don't set it to the number of cores). The second has to do with your batch-read tuning. You're using the (default) batch-index protocol, and the config params you'll need to tune for batch-read performance are: You have batch-max-requests set very high. This is probably affecting both your CPU load and your memory consumption. It's enough that there's a slight imbalance in the number of keys you're accessing per-node, and that will reflect in the graphs you've shown. At least, this is possibly the issue. It's better that you iterate over smaller batches than try to fetch 200K records per-node at a time. batch-index-threads – by default its value is 4, and you set it to 32 (of a max of 64). You should do this incrementally by running the same test and benchmarking the performance. On each iteration adjust higher, then down if it's decreased in performance. For example: test with 32, +8 = 40 , +8 = 48, -4 = 44. There's no easy rule-of-thumb for the setting, you'll need to tune through iterations on the hardware you'll be using, and monitor the performance. batch-max-buffer-per-queue – this is more directly linked to the number of concurrent batch-read operations the node can support. Each batch-read request will consume at least one buffer (more if the data cannot fit in 128K). If you do not have enough of these allocated to support the number of concurrent batch-read requests you will get exceptions with error code 152 BATCH_QUEUES_FULL . Track and log such events clearly, because it means you need to raise this value. Note that this is the number of buffers per-queue. Each batch response worker thread has its own queue, so you'll have batch-index-threads x batch-max-buffer-per-queue buffers, each taking 128K of RAM. The batch-max-unused-buffers caps the memory usage of all these buffers combined, destroying unused buffers until their number is reduced. There's an overhead to allocating and destroying these buffers, so you do not want to set it too low compared to the total. Your current cost is 32 x 256 x 128KB = 1GB. Finally, you're storing your data on a filesystem. That's fine for development instances, but not recommended for production. In GCE you can provision either a SATA SSD or an NVMe SSD for your data storage, and those should be initialized, and used as block devices. Take a look at the GCE recommendations for more details. I suspect you have warnings in your log about the device not keeping up. It's likely that one of your nodes is an outlier with regards to the number of partitions it has (and therefore number of objects). You can confirm it with asadm -e 'asinfo -v "objects"'. If that's the case, you can terminate that node, and bring up a new one. This will force the partitions to be redistributed. This does trigger a migration, which takes quite longer in the CE server than in the EE one.
For anyone interested, Aerospike Enterpirse 4.3 introduced 'uniform-balance' which homogeneously balances data partitions. Read more here: https://www.aerospike.com/blog/aerospike-4-3-all-flash-uniform-balance/
Aerospike cluster not clean available blocks
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Authentication failures in cassandra when 1 of 16 nodes is down
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Re-posting this as the answer, as BrianC suggested above. So this is resolved... Here's the sequence of events that seems to have fixed it: Add 18 more nodes Run cleanup on original nodes (this was part of the original plan) Run a scrub on 1 table, since it was throwing exceptions on cleanup Run a repair on the system_auth KS on the original troubled node Wait for repair service to complete a full pass on all keyspaces Decom original 18 nodes. Honestly, I don't know what fixed it. The system_auth repair makes most sense, but what doesn't make sense is that it had run many passes before, so why work now, I don't know. I hope this at least helps someone.
Spark execution occasionally gets stuck at mapPartitions at Exchange.scala:44
I am running a Spark job on a two node standalone cluster (v 1.0.1). Spark execution often gets stuck at the task mapPartitions at Exchange.scala:44. This happens at the final stage of my job in a call to saveAsTextFile (as I expect from Spark's lazy execution). It is hard to diagnose the problem because I never experience it in local mode with local IO paths, and occasionally the job on the cluster does complete as expected with the correct output (same output as with local mode). This seems possibly related to reading from s3 (of a ~170MB file) immediately prior, as I see the following logging in the console: DEBUG NativeS3FileSystem - getFileStatus returning 'file' for key '[PATH_REMOVED].avro' INFO FileInputFormat - Total input paths to process : 1 DEBUG FileInputFormat - Total # of splits: 3 ... INFO DAGScheduler - Submitting 3 missing tasks from Stage 32 (MapPartitionsRDD[96] at mapPartitions at Exchange.scala:44) DEBUG DAGScheduler - New pending tasks: Set(ShuffleMapTask(32, 0), ShuffleMapTask(32, 1), ShuffleMapTask(32, 2)) The last logging I see before the task apparently hangs/gets stuck is: INFO NativeS3FileSystem: INFO NativeS3FileSystem: Opening key '[PATH_REMOVED].avro' for reading at position '67108864' Has anyone else experience non-deterministic problems related to reading from s3 in Spark?