I have a rather busy RabbitMQ setup which at peak times becomes extremely slow accepting new connections (RabbitMQ 3.9.14)
I've tried fine tuning /etc/sysctl.conf as found on a guide on the RabbitMQ website
fs.file-max = 10000000
fs.nr_open = 10000000
fs.inotify.max_user_watches=524288
net.core.somaxconn = 4096
net.ipv4.tcp_fin_timeout = 30
net.ipv4.tcp_tw_reuse = 1
net.ipv4.tcp_keepalive_time=30
net.ipv4.tcp_keepalive_intvl=10
net.ipv4.tcp_keepalive_probes=4
net.ipv4.ip_local_port_range = 10000 64000
net.ipv6.conf.all.disable_ipv6=1
net.ipv6.conf.default.disable_ipv6=1
net.ipv6.conf.lo.disable_ipv6=1
net.netfilter.nf_conntrack_max=1048576
And also played around with the rabbitmq.conf options to see if anything would have an impact, however that is unfortunately not the case
num_acceptors.tcp = 32
channel_max = 4096
tcp_listen_options.backlog = 512
tcp_listen_options.nodelay = true
tcp_listen_options.linger.on = true
tcp_listen_options.linger.timeout = 0
tcp_listen_options.sndbuf = 196608
tcp_listen_options.recbuf = 196608
collect_statistics_interval = 60000
Due to the nature of my setup (PHP), every time messages are being published to RabbitMQ, a new connection is created, I wish I could do long-standing connections but that is beyond of what PHP is designed for
During peak activity, some connections take up to 7 seconds to open, once the connection is established however, the messages publishing performance is just fine.
I feel like I've exhausted all the logical options that I'm aware of. Is there any other tweaks that I can attempt to change in order to improve the connection performance of the node? The server load is low-ish, sitting at 15% peak. Disabling the management interface had negligible impact
Update: At first, when I've updated to RabbitMQ 3.10.5, I thought that the issue was solved, however that was not the case, it just gave us a bit more headroom.
The real cause was our high churn-rate (200/s+), during my conversation in the RabbitMQ slack channel it became apparent that a high churn rate would block the event loop and cause the spikes seen above.
The solution for us was to use a proxy to re-use connections instead of opening a new one every time we publish something:
https://github.com/cloudamqp/amqproxy
This has effectively resolved our issue.
Related
Good work everyone,
I am communicating with the STM32F746 processor using the lwip library. I got the ethernet buffers in the special area of the RAM and protected it with the MPU. Then I disabled the buffering feature. My code works as I want, but after 10-15 minutes communication is cut off. My grounding, my connections, everything looks good.
I left the mpu protection as default (0x30000000).
I wrote the following in the flash.ld file
. = ABSOLUTE(0x20010000);
*(.RxDecripSection)
. = ABSOLUTE(0x20010080);
*(.TxDecripSection)
. = ABSOLUTE(0x20010100);
*(.RxArraySection)
. = ABSOLUTE(0x200118D0);
*(.TxArraySection)
I set memory region from ethernetif.c file. But the result is still the same. My connection drops after 10 minutes.
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/
I have a job that periodically does some work involving ServerXmlHttpRquest to perform an HTTP POST. The job runs every 60 seconds.
And normally it runs without issue. But there's about a 1 in 50,000 chance (every two or three months) that it will hang:
IXMLHttpRequest http = new ServerXmlHttpRequest();
http.open("POST", deleteUrl, false, "", "");
http.send(stuffToDelete); <---hang
When it hangs, not even the Task Scheduler (with the option enabled to kill the job if it takes longer than 3 minutes to run) can end the task. I have to connect to the remote customer's network, get on the server, and use Task Manager to kill the process.
And then its good for another month or three.
Eventually i started using Task Manager to create a process dump,
so i could analyze where the hang is. After five crash dumps (over the last 11 months or so) i get a consistent picture:
ntdll.dll!_NtWaitForMultipleObjects#20()
KERNELBASE.dll!_WaitForMultipleObjectsEx#20()
user32.dll!MsgWaitForMultipleObjectsEx()
user32.dll!_MsgWaitForMultipleObjects#20()
urlmon.dll!CTransaction::CompleteOperation(int fNested) Line 2496
urlmon.dll!CTransaction::StartEx(IUri * pIUri, IInternetProtocolSink * pOInetProtSink, IInternetBindInfo * pOInetBindInfo, unsigned long grfOptions, unsigned long dwReserved) Line 4453 C++
urlmon.dll!CTransaction::Start(const wchar_t * pwzURL, IInternetProtocolSink * pOInetProtSink, IInternetBindInfo * pOInetBindInfo, unsigned long grfOptions, unsigned long dwReserved) Line 4515 C++
msxml3.dll!URLMONRequest::send()
msxml3.dll!XMLHttp::send()
Contoso.exe!FrobImporter.TFrobImporter.DeleteFrobs Line 971
Contoso.exe!FrobImporter.TFrobImporter.ImportCore Line 1583
Contoso.exe!FrobImporter.TFrobImporter.RunImport Line 1070
Contoso.exe!CommandLineProcessor.TCommandLineProcessor.HandleFrobImport Line 433
Contoso.exe!CommandLineProcessor.TCommandLineProcessor.CoreExecute Line 71
Contoso.exe!CommandLineProcessor.TCommandLineProcessor.Execute Line 84
Contoso.exe!Contoso.Contoso Line 167
kernel32.dll!#BaseThreadInitThunk#12()
ntdll.dll!__RtlUserThreadStart()
ntdll.dll!__RtlUserThreadStart#8()
So i do a ServerXmlHttpRequest.send, and it never returns. It will sit there for days (causing the system to miss financial transactions, until come Sunday night i get a call that it's broken).
It is of no help unless someone knows how to debug code, but the registers in the stalled thread at the time of the dump are:
EAX 00000030
EBX 00000000
ECX 00000000
EDX 00000000
ESI 002CAC08
EDI 00000001
EIP 732A08A7
ESP 0018F684
EBP 0018F6C8
EFL 00000000
Windows Server 2012 R2
Microsoft IIS/8.5
Default timeouts of ServerXmlHttpRequest
You can use serverXmlHttpRequest.setTimeouts(...) to configure the four classes of timeouts:
resolveTimeout: The value is applied to mapping host names (such as "www.microsoft.com") to IP addresses; the default value is infinite, meaning no timeout.
connectTimeout: A long integer. The value is applied to establishing a communication socket with the target server, with a default timeout value of 60 seconds.
sendTimeout: The value applies to sending an individual packet of request data (if any) on the communication socket to the target server. A large request sent to a server will normally be broken up into multiple packets; the send timeout applies to sending each packet individually. The default value is 30 seconds.
receiveTimeout: The value applies to receiving a packet of response data from the target server. Large responses will be broken up into multiple packets; the receive timeout applies to fetching each packet of data off the socket. The default value is 30 seconds.
The KB305053 (a server that decides to keep the connection open will cause serverXmlHttpRequest to wait for the connection to close) seems like it plausibly could be the issue. But the 30 second default timeout would have taken care of that.
Possible workaround - Add myself to a Job
The Windows Task Scheduler is unable to terminate the task; even though the option is enabled to do do.
I will look into using the Windows Job API to add my self process to a job, and use SetInformationJobObject to set a time limit on my process:
CreateJobObject
AssignProcessToJobObject
SetInformationJobObject
to limit my process to three minutes of execution time:
PerProcessUserTimeLimit
If LimitFlags specifies
JOB_OBJECT_LIMIT_PROCESS_TIME, this member is the per-process
user-mode execution time limit, in 100-nanosecond ticks. Otherwise,
this member is ignored.
The system periodically checks to determine
whether each process associated with the job has accumulated more
user-mode time than the set limit. If it has, the process is
terminated.
If the job is nested, the effective limit is the most
restrictive limit in the job chain.
Although since Task Scheduler uses Job objects to also limit a task's time, i'm not hopeful that the Job Object can limit a job either.
Edit: Job objects cannot limit a process by process time - only user time. And with a process idle waiting for an object, it will not accumulate any user time - certainly not three minutes worth.
Bonus Reading
How can a ServerXMLHTTP GET request hang? (GET, not POST)
KB305053: ServerXMLHTTP Stops Responding When You Send a POST Request (which says the timeout should expire; where mine does not)
MS Forums: oHttp.Send - Hangs (HEAD, not POST)
MS Forums: ASP to test SOAP WebService using MSXML2.ServerXMLHTTP Send hangs
CC to MS Support Forums
Consider switching to a newer, supported API.
msxml6.dll using MSXML2.ServerXMLHTTP.6.0
winhttpcom.dll using WinHttp.WinHttpRequest.5.1.
The msxml3.dll library is no longer supported and is only kept around for compatibility reasons. Plus, there were a number of security and stability improvements included with msxml4.dll (and newer) that you are missing out on.
I am trying to use sentinal redis to get/set keys from redis. I was trying to stress test my setup with about 2000 concurrent requests.
i used sentinel to put a single key on redis and then I executed 1000 concurrent get requests from redis.
But the underlying jedis used my sentinel is blocking call on getResource() (pool size is 500) and the overall average response time that I am achieving is around 500 ms, but my target was about 10 ms.
I am attaching sample of jvisualvm snapshot here
redis.clients.jedis.JedisSentinelPool.getResource() 98.02227 4.0845232601E7 ms 4779
redis.clients.jedis.BinaryJedis.get() 1.6894469 703981.381 ms 141
org.apache.catalina.core.ApplicationFilterChain.doFilter() 0.12820946 53424.035 ms 6875
org.springframework.core.serializer.support.DeserializingConverter.convert() 0.046286926 19287.457 ms 4
redis.clients.jedis.JedisSentinelPool.returnResource() 0.04444578 18520.263 ms 4
org.springframework.aop.framework.CglibAopProxy$DynamicAdvisedInterceptor.intercept() 0.035538 14808.45 ms 11430
May anyone help to debug further into the issue?
From JedisSentinelPool implementation of getResource() from Jedis sources (2.6.2):
#Override
public Jedis getResource() {
while (true) {
Jedis jedis = super.getResource();
jedis.setDataSource(this);
// get a reference because it can change concurrently
final HostAndPort master = currentHostMaster;
final HostAndPort connection = new HostAndPort(jedis.getClient().getHost(), jedis.getClient()
.getPort());
if (master.equals(connection)) {
// connected to the correct master
return jedis;
} else {
returnBrokenResource(jedis);
}
}
}
Note the while(true) and the returnBrokenResource(jedis), it means that it tries to get a jedis resource randomly from the pool that is indeed connected to the correct master and retries if it is not the good one. It is a dirty check and also a blocking call.
The super.getResource() call refers to JedisPool traditionnal implementation that is actually based on Apache Commons Pool (2.0). It does a lot to get an object from the pool, and I think it even repairs fail connections for instance. With a lot of contention on your pool, as probably in your stress test, it can probably take a lot of time to get a resource from the pool, just to see it is not connected to the correct master, so you end up calling it again, adding contention, slowing getting the resource etc...
You should check all the jedis instances in your pool to see if there's a lot of 'bad' connections.
Maybe you should give up using a common pool for your stress test (only create Jedis instances manually connected to the correct node, and close them nicely), or setting multiple ones to mitigate the cost of looking to "dirty" unchecked jedis resources.
Also with a pool of 500 jedis instances, you can't emulate 1000 concurrent queries, you need at least 1000.
I am calling 5 external servers to retrieve XML-based data for each request for a particular webpage on my IIS 6 server. Present volume is between 3-5 incoming requests per second, meaning 15-20 outgoing requests per second.
99% of the outgoing requests from my server (the client) to the external servers (the server) work OK but about 100-200 per day end up with a "The operation has timed out" exception.
This suggests I have a resource problem on my server - some shortage of sockets, ports etc or a thread lock but the problem with this theory is that the failures are entirely random - there are not a number of requests in a row that all fail - and two of the external servers account for the majority of the failures.
My question is how can I further diagnose these exceptions to determine if the problem is on my end (the client) or on the other end (the servers)?
The volume of requests precludes putting an analyzer on the wire - it would be very difficult to capture these few exceptions. I have reset CONNECTIONS and THREADS in my machine.config and the basic code looks like:
Dim hRequest As HttpWebRequest
Dim responseTime As String
Dim objWatch As New Stopwatch
Try
' calculate time it takes to process transaction
objWatch.Start()
hRequest = System.Net.WebRequest.Create(url)
' set some defaults
hRequest.Timeout = 5000
hRequest.ReadWriteTimeout = 10000
hRequest.KeepAlive = False ' to prevent open HTTP connection leak
hRequest.SendChunked = False
hRequest.AllowAutoRedirect = True
hRequest.MaximumAutomaticRedirections = 3
hRequest.Accept = "text/xml"
hRequest.Proxy = Nothing 'do not waste time searching for a proxy
hRequest.ServicePoint.Expect100Continue = False
Dim feed As New XDocument()
' use *Using* to auto close connections
Using hResponse As HttpWebResponse = DirectCast(hRequest.GetResponse(), HttpWebResponse)
Using reader As XmlReader = XmlReader.Create(hResponse.GetResponseStream())
feed = XDocument.Load(reader)
reader.Close()
End Using
hResponse.Close()
End Using
objWatch.Stop()
' Work here with returned contents in "feed" document
Return XXX' some results here
Catch ex As Exception
objWatch.Stop()
hRequest.Abort()
Return Nothing
End Try
Any suggestions?
By default, HttpWebRequest limits you to 2 connections per HTTP/1.1 server. So, if your requests take time to complete, and you have incoming requests queuing up on the server, you will run out of connection and thus get timeouts.
You should change the max outgoing connections on ServicePointManager.
ServicePointManager.DefaultConnectionLimit = 20 // or some big value.
You said that you are doing 5 outgoing request for each incoming request to the ASP page. Is that 5 different servers, or the same server?
DO you wait for the previous request to complete, before issuing the next one? Is the timeout happening while it is waiting for a connection, or during the request/response?
If the timeout is happening during the request/response then it means that the target server is under stress. The only way to find out if this is the case, is to run wireshark/netmon on one of the machines, and look at the network trace to see if the request from the app is even making it through to the server, and if it is, whether the target server is responding within the given timeout.
If this is a thread starvation issue, then one of the ways to diagnose it is to attach windbg.exe debugger to w3wp.exe process, when you start getting timeout. Then load the sos.dll debugging extension. And run the !threads command, followed by !threadpool command. It will show you how many Worker threads and completion port threads are utilized/remaining. If the #completionport threads or worker threads are low, then that will contribute to the timeout.
Alternatively, you can monitor ASP.NET and System.net perf counters. See if the ASP.NET request queue is increasing monotonically - this might indicate that your outgoing requests are not completing fast enough.
Sorry, there are no easy answers here. THere is a lot of avenues you will need to explore. If I were you, I would start off by attaching windbg.exe to w3wp when you start getting timeouts and do what I described earlier.