Aerospike cluster not clean available blocks - aerospike

we use aerospike in our projects and caught strange problem.
We have a 3 node cluster and after some node restarting it stop working.
So, we make test to explain our problem
We make test cluster. 3 node, replication count = 2
Here is our namespace config
namespace test{
replication-factor 2
memory-size 100M
high-water-memory-pct 90
high-water-disk-pct 90
stop-writes-pct 95
single-bin true
default-ttl 0
storage-engine device {
cold-start-empty true
file /tmp/test.dat
write-block-size 1M
}
We write 100Mb test data after that we have that situation
available pct equal about 66% and Disk Usage about 34%
All good :slight_smile:
But we stopped one node. After migration we see that available pct = 49% and disk usage 50%
Return node to cluster and after migration we see that disk usage became previous about 32%, but available pct on old nodes stay 49%
Stop node one more time
available pct = 31%
Repeat one more time we get that situation
available pct = 0%
Our cluster crashed, Clients get AerospikeException: Error Code 8: Server memory error
So how we can clean available pct?

If your defrag-q is empty (and you can see whether it is from grepping the logs) then the issue is likely to be that your namespace is smaller than your post-write-queue. Blocks on the post-write-queue are not eligible for defragmentation and so you would see avail-pct trending down with no defragmentation to reclaim the space. By default the post-write-queue is 256 blocks and so in your case that would equate to 256Mb. If your namespace is smaller than that you will see avail-pct continue to drop until you hit stop-writes. You can reduce the size of the post-write-queue dynamically (i.e. no restart needed) using the following command, here I suggest 8 blocks:
asinfo -v 'set-config:context=namespace;id=<NAMESPACE>;post-write-queue=8'
If you are happy with this value you should amend your aerospike.conf to include it so that it persists after a node restart.

Related

JVM Runtime.availableProcessors() returns 2 when it should be 4

I'm running openjdk11 on alpine linux in a container in an AWS EKS cluster.
The application determines the size of a threadpool based on the number of CPUs as returned by Runtime.getRuntime().availableProcessors()
This call is returning 2 processors even though the container shows that 4 CPUs are available:
# cat /proc/cpuinfo | grep processor
processor : 0
processor : 1
processor : 2
processor : 3
Any idea why and how to solve the problem?
Update
Doing some more digging (prompted by some great questions from #gohm'c in the comments), I found a way to add some trace log prints to the JVM with -Xlog:os+container=trace
[0.001s][trace][os,container] CPU Shares is: 1536
[0.001s][trace][os,container] CPU Share count based on shares: 2
Now, I defined in resources.requests.cpu: "1500m".
I don't know why the slight discrepancy but I changed the value of the CPU request, and indeed the CPU Shares in the log trace changes accordingly.
I understand how the resources.limits.cpu value could affect the CPUs that the JVM sees. But why is the resources.requests.cpu value doing that! This seems like a bug to me? Any thoughts?

Janusgraph not using index in production

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.

Why flink container vcore size is always 1

I am running flink on yarn(more precisely in AWS EMR yarn cluster).
I read flink document and source code that by default for each task manager container, flink will request the number of slot per task manager as the number of vcores when request resource from yarn.
And I also confirmed from the source code:
// Resource requirements for worker containers
int taskManagerSlots = taskManagerParameters.numSlots();
int vcores = config.getInteger(ConfigConstants.YARN_VCORES,
Math.max(taskManagerSlots, 1));
Resource capability = Resource.newInstance(containerMemorySizeMB,
vcores);
resourceManagerClient.addContainerRequest(
new AMRMClient.ContainerRequest(capability, null, null,
priority));
When I use -yn 1 -ys 3 to start flink, I assume yarn will allocate 3 vcores for the only task manager container, but when I checked the number of vcores for each container from yarn resource manager web ui, I always see the number of vcores is 1. I also see vcore to be 1 from yarn resource manager logs.
I debugged the flink source code to the line I pasted below, and I saw value of vcores is 3.
This is really confuse me, can anyone help to clarify for me, thanks.
An answer from Kien Truong
Hi,
You have to enable CPU scheduling in YARN, otherwise, it always shows that only 1 CPU is allocated for each container,
regardless of how many Flink try to allocate. So you should add (edit) the following property in capacity-scheduler.xml:
<property>
<name>yarn.scheduler.capacity.resource-calculator</name>
<!-- <value>org.apache.hadoop.yarn.util.resource.DefaultResourceCalculator</value> -->
<value>org.apache.hadoop.yarn.util.resource.DominantResourceCalculator</value>
</property>
ALso, taskManager memory is, for example, 1400MB, but Flink reserves some amount for off-heap memory, so the actual heap size is smaller.
This is controlled by 2 settings:
containerized.heap-cutoff-min: default 600MB
containerized.heap-cutoff-ratio: default 15% of TM's memory
That's why your TM's heap size is limitted to ~800MB (1400 - 600)
#yinhua.
Use the command to start a session:./bin/yarn-session.sh, you need add -s arg.
-s,--slots Number of slots per TaskManager
details:
https://ci.apache.org/projects/flink/flink-docs-release-1.4/ops/deployment/yarn_setup.html
https://ci.apache.org/projects/flink/flink-docs-release-1.4/ops/cli.html#usage
I get the answer finally.
It's because yarn is use "DefaultResourceCalculator" allocation strategy, so only memory is counted for yarn RM, even if flink requested 3 vcores, but yarn simply ignore the cpu core number.

Wrong balance between Aerospike instances in cluster

I have an application with a high load for batch read operations. My Aerospike cluster (v 3.7.2) has 14 servers, each one with 7GB RAM and 2 CPUs in Google Cloud.
By looking at Google Cloud Monitoring Graphs, I noticed a very unbalanced load between servers: some servers have almost 100% CPU load, while others have less than 50% (image below). Even after hours of operation, the cluster unbalanced pattern doesn't change.
Is there any configuration that I could change to make this cluster more homogeneous? How to optimize node balancing?
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: Failed to store record. Error: (13L, 'AEROSPIKE_ERR_RECORD_TOO_BIG', 'src/main/client/put.c', 106)

I get the following error while storing the data to aerospike ( client.put ). I have enough space on the drive.
Aerospike: Failed to store record. Error: (13L, 'AEROSPIKE_ERR_RECORD_TOO_BIG', 'src/main/client/put.c', 106).
Here is my Aerospike server namespace configuration
namespace test {
replication-factor 1
memory-size 1G
default-ttl 30d # 30 days, use 0 to never expire/evict.
storage-engine device {
file /opt/aerospike/data/test.dat
filesize 2G
data-in-memory true # Store data in memory in addition to file.
}
}
By default namespaces have a write-block-size of 1 MiB. This is also the maximum configurable size and will limit the max object size the application is able to write to Aerospike.
If you need to go beyond 1 MiB see Large Data Types as a possible solution.
UPDATE 2019/09/06
Since Aerospike 3.16, the write-block-size limit has been increased from 1 MiB to 8 MiB.
Yes, but unfortunately, Aerospike has deprecated LDT (https://www.aerospike.com/blog/aerospike-ldt/). They now recommend to use Lists or Maps, but as stated in their post:
"the new implementation does not solve the problem of the 1MB Aerospike database row size limit. A future key feature of the product will be an enhanced implementation that transcends the 1MB limit for a number of types"
In other terms, it is still an unsolved problem when storing your data on SSD or HDD. However, you can store larger data on memory namespaces.