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• JVM GC demote object to eden space?

  - by Kevin

  I'm guessing this isn't possible...but here goes. My understanding is that eden space is cheaper to collect than old gen space, especially when you start getting into very large heaps. Large heaps tend to come up with long running applications (server apps) and server apps a lot of the time want to use some kind of caches. Caches with some kind of eviction (LRU) tend to defeat some assumptions that GC makes (temporary objects die quickly). So cache evictions end up filling up old gen faster than you'd like and you end up with a more costly old gen collection. Now, it seems like this sort of thing could be avoided if java provided a way to mark a reference as about to die (delete keyword)? The difference between this and c++ is that the use is optional. And calling delete does not actually delete the object, but rather is a hint to the GC that it should demote the object back to Eden space (where it will be more easily collected). I'm guessing this feature doesn't exist, but, why not (is there a reason it's a bad idea)?

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• tproxy squid bridge very slow when cache is full

  - by Roberto

  I have installed a bridge tproxy proxy in a fast server with 8GB ram. The traffic is around 60Mb/s. When I start for first time the proxy (with the cache empty) the proxy works very well but when the cache becomes full (few hours later) the bridge goes very slow, the traffic goes below 10Mb/s and the proxy server becomes unusable. Any hints of what may be happening? I'm using: linux-2.6.30.10 iptables-1.4.3.2 squid-3.1.1 compiled with these options: ./configure --prefix=/usr --mandir=/usr/share/man --infodir=/usr/share/info --datadir=/usr/share --localstatedir=/var/lib --sysconfdir=/etc/squid --libexecdir=/usr/libexec/squid --localstatedir=/var --datadir=/usr/share/squid --enable-removal-policies=lru,heap --enable-icmp --disable-ident-lookups --enable-cache-digests --enable-delay-pools --enable-arp-acl --with-pthreads --with-large-files --enable-htcp --enable-carp --enable-follow-x-forwarded-for --enable-snmp --enable-ssl --enable-async-io=32 --enable-linux-netfilter --enable-epoll --disable-poll --with-maxfd=16384 --enable-err-languages=Spanish --enable-default-err-language=Spanish My squid.conf: cache_mem 100 MB memory_pools off acl manager proto cache_object acl localhost src 127.0.0.1/32 acl localhost src ::1/128 acl to_localhost dst 127.0.0.0/8 0.0.0.0/32 acl to_localhost dst ::1/128 acl localnet src 10.0.0.0/8 # RFC1918 possible internal network acl localnet src 172.16.0.0/12 # RFC1918 possible internal network acl localnet src 192.168.0.0/16 # RFC1918 possible internal network acl localnet src fc00::/7 # RFC 4193 local private network range acl localnet src fe80::/10 # RFC 4291 link-local (directly plugged) machines acl net-g1 src xxx.xxx.xxx.xxx/24 acl SSL_ports port 443 acl Safe_ports port 80 # http acl Safe_ports port 21 # ftp acl Safe_ports port 443 # https acl Safe_ports port 70 # gopher acl Safe_ports port 210 # wais acl Safe_ports port 1025-65535 # unregistered ports acl Safe_ports port 280 # http-mgmt acl Safe_ports port 488 # gss-http acl Safe_ports port 591 # filemaker acl Safe_ports port 777 # multiling http acl CONNECT method CONNECT http_access allow manager localhost http_access deny manager http_access deny !Safe_ports http_access deny CONNECT !SSL_ports http_access allow net-g1 from where browsing should be allowed http_access allow localnet http_access allow localhost http_access deny all http_port 3128 http_port 3129 tproxy hierarchy_stoplist cgi-bin ? cache_dir ufs /var/spool/squid 8000 16 256 access_log none cache_log /var/log/squid/cache.log coredump_dir /var/spool/squid refresh_pattern ^ftp: 1440 20% 10080 refresh_pattern ^gopher: 1440 0% 1440 refresh_pattern -i (/cgi-bin/|\?) 0 0% 0 refresh_pattern . I have this issue when the cache is full, but do not really know if it is because of that. Thanks in advance and sorry my english. roberto

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• RHEL 6.x on Rackspace Cloud and Dedicated hardware experiencing Redis Timeouts

  - by zhallett

  I just recently set up a mixture of RHEL 6.1 Rackspace cloud hosts and RHEL 6.2 dedicated hosts using Rackconnect. I am experiencing intermittent Redis timeouts from within our Rails 3.2.8 app with Redis 2.4.16 running on the RHEL 6.2 dedicated hosts. There is no network latency or packet loss. Also there are no errors on any interfaces on our cloud or dedicated servers or on the managed firewall from Rackspace. When Redis timesout, there is nothing logged within redis even though it is set up to do debug logging. The only error we receive is from Airbrake saying there was a Redis timeout. Network topology: RHEL 6.1 cloud hosts <--> Alert logic IDS <--> Cisco ASA 5510 <--> RHEL 6.2 dedicated hosts (web nodes) (two way NAT) (db hosts running redis) Ping from db host to web host: 64 bytes from 10.181.230.180: icmp_seq=998 ttl=64 time=0.520 ms 64 bytes from 10.181.230.180: icmp_seq=999 ttl=64 time=0.579 ms 64 bytes from 10.181.230.180: icmp_seq=1000 ttl=64 time=0.482 ms --- web1.xxxxxx.com ping statistics --- 1000 packets transmitted, 1000 received, 0% packet loss, time 999007ms rtt min/avg/max/mdev = 0.359/0.535/5.684/0.200 ms Ping from web host to db host: 64 bytes from 192.168.100.26: icmp_seq=998 ttl=64 time=0.544 ms 64 bytes from 192.168.100.26: icmp_seq=999 ttl=64 time=0.452 ms 64 bytes from 192.168.100.26: icmp_seq=1000 ttl=64 time=0.529 ms --- data1.xxxxxx.com ping statistics --- 1000 packets transmitted, 1000 received, 0% packet loss, time 999017ms rtt min/avg/max/mdev = 0.358/0.499/6.120/0.201 ms Redis config: daemonize yes pidfile /var/run/redis/6379/redis_6379.pid port 6379 timeout 0 loglevel debug logfile /var/lib/redis/log syslog-enabled yes syslog-ident redis-6379 syslog-facility local0 databases 16 save 900 1 save 300 10 save 60 10000 rdbcompression yes dbfilename dump-6379.rdb dir /var/lib/redis maxclients 10000 maxmemory-policy volatile-lru maxmemory-samples 3 appendfilename appendonly-6379.aof appendfsync everysec no-appendfsync-on-rewrite no auto-aof-rewrite-percentage 100 auto-aof-rewrite-min-size 64mb slowlog-log-slower-than 10000 slowlog-max-len 1024 vm-enabled no vm-swap-file /tmp/redis.swap vm-max-memory 0 vm-page-size 32 vm-pages 134217728 vm-max-threads 4 hash-max-zipmap-entries 512 hash-max-zipmap-value 64 list-max-ziplist-entries 512 list-max-ziplist-value 64 set-max-intset-entries 512 zset-max-ziplist-entries 128 zset-max-ziplist-value 64 activerehashing yes Redis-cli info: redis-cli info redis_version:2.4.16 redis_git_sha1:00000000 redis_git_dirty:0 arch_bits:64 multiplexing_api:epoll gcc_version:4.4.6 process_id:4174 uptime_in_seconds:79346 uptime_in_days:0 lru_clock:1064644 used_cpu_sys:13.08 used_cpu_user:19.81 used_cpu_sys_children:1.56 used_cpu_user_children:7.69 connected_clients:167 connected_slaves:0 client_longest_output_list:0 client_biggest_input_buf:0 blocked_clients:6 used_memory:15060312 used_memory_human:14.36M used_memory_rss:22061056 used_memory_peak:15265928 used_memory_peak_human:14.56M mem_fragmentation_ratio:1.46 mem_allocator:jemalloc-3.0.0 loading:0 aof_enabled:0 changes_since_last_save:166 bgsave_in_progress:0 last_save_time:1352823542 bgrewriteaof_in_progress:0 total_connections_received:286 total_commands_processed:507254 expired_keys:0 evicted_keys:0 keyspace_hits:1509 keyspace_misses:65167 pubsub_channels:0 pubsub_patterns:0 latest_fork_usec:690 vm_enabled:0 role:master db0:keys=6,expires=0 edit 1: add redis-cli info output

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• Why MySQL sat for 2 minutes doing nothing?

  - by Alex R

  This was a one-time thing, not reproducible... But I saved the show innodb status output. Can anybody tell what's going on here? The simple insert took almost 3 minutes to complete. | InnoDB | | ===================================== 110201 15:58:10 INNODB MONITOR OUTPUT ===================================== Per second averages calculated from the last 34 seconds ---------- SEMAPHORES ---------- OS WAIT ARRAY INFO: reservation count 11963, signal count 11766 --Thread 1824 has waited at .\btr\btr0cur.c line 443 for 118.00 seconds the sema phore: S-lock on RW-latch at 09D6453C created in file .\buf\buf0buf.c line 550 a writer (thread id 1824) has reserved it in mode wait exclusive number of readers 1, waiters flag 1 Last time read locked in file .\buf\buf0flu.c line 599 Last time write locked in file .\btr\btr0cur.c line 443 Mutex spin waits 0, rounds 527817, OS waits 7133 RW-shared spins 2532, OS waits 1226; RW-excl spins 1652, OS waits 1118 ------------ TRANSACTIONS ------------ Trx id counter 0 95830 Purge done for trx's n:o < 0 95814 undo n:o < 0 0 History list length 11 LIST OF TRANSACTIONS FOR EACH SESSION: ---TRANSACTION 0 0, not started, OS thread id 3704 MySQL thread id 551, query id 2702112 localhost 127.0.0.1 root show innodb status ---TRANSACTION 0 95829, not started, OS thread id 3132 MySQL thread id 534, query id 2702020 localhost 127.0.0.1 root ---TRANSACTION 0 95828, not started, OS thread id 3152 MySQL thread id 527, query id 2701973 localhost 127.0.0.1 root ---TRANSACTION 0 95827, ACTIVE 118 sec, OS thread id 1824 inserting, thread decl ared inside InnoDB 500 mysql tables in use 1, locked 1 1 lock struct(s), heap size 320, 0 row lock(s) MySQL thread id 526, query id 2701972 localhost 127.0.0.1 root update INSERT INTO log_searchcriteria (userid,search_criteria,date,search_type) VALUES ( NAME_CONST('userid',NULL), NAME_CONST('search_criteria',_latin1' SELECT SQL_C ALC_FOUND_ROWS idx_search.CTCX_LATITUDE, idx_search.CTCX_LONGITUDE, idx_search.b uilding_id, idx_search.LN_LIST_NUMBER, idx_search.LP_LIST_PRICE, idx_search.HSN_ ADRESS_HOUSE_NUMBER, idx_search.STR_ADDRESS_STREET, idx_search.CP_ADDRESS_COMPAS S_POINT, idx_search.UN_UNIT, idx_search.CIT_CITY, idx_search.ZP_ZIP_CODE, idx_se arch.AR_AREA_NAME, idx_search.BR_BEDROOMS, idx_search.BTH_BATHS, idx_search.ST_S TATUS, idx_search.CTCX_STYLE_TYPE, idx_s -------- FILE I/O -------- I/O thread 0 state: wait Windows aio (insert buffer thread) I/O thread 1 state: wait Windows aio (log thread) I/O thread 2 state: wait Windows aio (read thread) I/O thread 3 state: wait Windows aio (write thread) Pending normal aio reads: 0, aio writes: 1, ibuf aio reads: 0, log i/o's: 0, sync i/o's: 0 Pending flushes (fsync) log: 0; buffer pool: 0 151006 OS file reads, 120758 OS file writes, 6844 OS fsyncs 0.00 reads/s, 0 avg bytes/read, 0.00 writes/s, 0.00 fsyncs/s ------------------------------------- INSERT BUFFER AND ADAPTIVE HASH INDEX ------------------------------------- Ibuf: size 1, free list len 5, seg size 7, 24664 inserts, 24664 merged recs, 4612 merges Hash table size 553253, node heap has 629 buffer(s) 0.00 hash searches/s, 0.00 non-hash searches/s --- LOG --- Log sequence number 5 2318193115 Log flushed up to 5 2318193115 Last checkpoint at 5 2318129891 0 pending log writes, 0 pending chkp writes 3036 log i/o's done, 0.00 log i/o's/second ---------------------- BUFFER POOL AND MEMORY ---------------------- Total memory allocated 213459462; in additional pool allocated 1720192 Dictionary memory allocated 240416 Buffer pool size 8192 Free buffers 0 Database pages 7563 Modified db pages 18 Pending reads 0 Pending writes: LRU 0, flush list 18, single page 0 Pages read 150973, created 28788, written 115137 0.00 reads/s, 0.00 creates/s, 0.00 writes/s No buffer pool page gets since the last printout -------------- ROW OPERATIONS -------------- 1 queries inside InnoDB, 0 queries in queue 1 read views open inside InnoDB Main thread id 2992, state: flushing buffer pool pages Number of rows inserted 794294, updated 89203, deleted 13698, read 1453084305 0.00 inserts/s, 0.00 updates/s, 0.00 deletes/s, 0.00 reads/s ---------------------------- END OF INNODB MONITOR OUTPUT ============================ Thanks

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• Innodb Queries Slow

  - by user105196

  I have redHat 5.3 (Tikanga) with Mysql 5.0.86 configued with RIAD 10 HW, I run an application inquiries from Mysql/InnoDB and MyIsam tables, the queries are super fast,but some quires on Innodb tables sometime slow down and took more than 1-3 seconds to run and these queries are simple and optimized, this problem occurred just on innodb tables in different time with random queries. Why is this happening only to Innodb tables? the below is the Innodb status and some Mysql variables: show innodb status\G ************* 1. row ************* Status: 120325 10:54:08 INNODB MONITOR OUTPUT Per second averages calculated from the last 19 seconds SEMAPHORES OS WAIT ARRAY INFO: reservation count 22943, signal count 22947 Mutex spin waits 0, rounds 561745, OS waits 7664 RW-shared spins 24427, OS waits 12201; RW-excl spins 1461, OS waits 1277 TRANSACTIONS Trx id counter 0 119069326 Purge done for trx's n:o < 0 119069326 undo n:o < 0 0 History list length 41 Total number of lock structs in row lock hash table 0 LIST OF TRANSACTIONS FOR EACH SESSION: ---TRANSACTION 0 0, not started, process no 29093, OS thread id 1166043456 MySQL thread id 703985, query id 5807220 localhost root show innodb status FILE I/O I/O thread 0 state: waiting for i/o request (insert buffer thread) I/O thread 1 state: waiting for i/o request (log thread) I/O thread 2 state: waiting for i/o request (read thread) I/O thread 3 state: waiting for i/o request (write thread) Pending normal aio reads: 0, aio writes: 0, ibuf aio reads: 0, log i/o's: 0, sync i/o's: 0 Pending flushes (fsync) log: 0; buffer pool: 0 132777 OS file reads, 689086 OS file writes, 252010 OS fsyncs 0.00 reads/s, 0 avg bytes/read, 0.00 writes/s, 0.00 fsyncs/s INSERT BUFFER AND ADAPTIVE HASH INDEX Ibuf: size 1, free list len 366, seg size 368, 62237 inserts, 62237 merged recs, 52881 merges Hash table size 8850487, used cells 3698960, node heap has 7061 buffer(s) 0.00 hash searches/s, 0.00 non-hash searches/s LOG Log sequence number 15 3415398745 Log flushed up to 15 3415398745 Last checkpoint at 15 3415398745 0 pending log writes, 0 pending chkp writes 218214 log i/o's done, 0.00 log i/o's/second BUFFER POOL AND MEMORY Total memory allocated 4798817080; in additional pool allocated 12342784 Buffer pool size 262144 Free buffers 101603 Database pages 153480 Modified db pages 0 Pending reads 0 Pending writes: LRU 0, flush list 0, single page 0 Pages read 151954, created 1526, written 494505 0.00 reads/s, 0.00 creates/s, 0.00 writes/s No buffer pool page gets since the last printout ROW OPERATIONS 0 queries inside InnoDB, 0 queries in queue 1 read views open inside InnoDB Main thread process no. 29093, id 1162049856, state: waiting for server activity Number of rows inserted 77675, updated 85439, deleted 0, read 14377072495 0.00 inserts/s, 0.00 updates/s, 0.00 deletes/s, 0.00 reads/s END OF INNODB MONITOR OUTPUT 1 row in set, 1 warning (0.02 sec) read_buffer_size = 128M sort_buffer_size = 256M tmp_table_size = 1024M innodb_additional_mem_pool_size = 20M innodb_log_file_size=10M innodb_lock_wait_timeout=100 innodb_buffer_pool_size=4G join_buffer_size = 128M key_buffer_size = 1G can any one help me ?

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• The clock hands of the buffer cache

  - by Tony Davis

  Over a leisurely beer at our local pub, the Waggon and Horses, Phil Factor was holding forth on the esoteric, but strangely poetic, language of SQL Server internals, riddled as it is with 'sleeping threads', 'stolen pages', and 'memory sweeps'. Generally, I remain immune to any twinge of interest in the bowels of SQL Server, reasoning that there are certain things that I don't and shouldn't need to know about SQL Server in order to use it successfully. Suddenly, however, my attention was grabbed by his mention of the 'clock hands of the buffer cache'. Back at the office, I succumbed to a moment of weakness and opened up Google. He wasn't lying. SQL Server maintains various memory buffers, or caches. For example, the plan cache stores recently-used execution plans. The data cache in the buffer pool stores frequently-used pages, ensuring that they may be read from memory rather than via expensive physical disk reads. These memory stores are classic LRU (Least Recently Updated) buffers, meaning that, for example, the least frequently used pages in the data cache become candidates for eviction (after first writing the page to disk if it has changed since being read into the cache). SQL Server clearly needs some mechanism to track which pages are candidates for being cleared out of a given cache, when it is getting too large, and it is this mechanism that is somewhat more labyrinthine than I previously imagined. Each page that is loaded into the cache has a counter, a miniature "wristwatch", which records how recently it was last used. This wristwatch gets reset to "present time", each time a page gets updated and then as the page 'ages' it clicks down towards zero, at which point the page can be removed from the cache. But what is SQL Server is suffering memory pressure and urgently needs to free up more space than is represented by zero-counter pages (or plans etc.)? This is where our 'clock hands' come in. Each cache has associated with it a "memory clock". Like most conventional clocks, it has two hands; one "external" clock hand, and one "internal". Slava Oks is very particular in stressing that these names have "nothing to do with the equivalent types of memory pressure". He's right, but the names do, in that peculiar Microsoft tradition, seem designed to confuse. The hands do relate to memory pressure; the cache "eviction policy" is determined by both global and local memory pressures on SQL Server. The "external" clock hand responds to global memory pressure, in other words pressure on SQL Server to reduce the size of its memory caches as a whole. Global memory pressure – which just to confuse things further seems sometimes to be referred to as physical memory pressure – can be either external (from the OS) or internal (from the process itself, e.g. due to limited virtual address space). The internal clock hand responds to local memory pressure, in other words the need to reduce the size of a single, specific cache. So, for example, if a particular cache, such as the plan cache, reaches a defined "pressure limit" the internal clock hand will start to turn and a memory sweep will be performed on that cache in order to remove plans from the memory store. During each sweep of the hands, the usage counter on the cache entry is reduced in value, effectively moving its "last used" time to further in the past (in effect, setting back the wrist watch on the page a couple of hours) and increasing the likelihood that it can be aged out of the cache. There is even a special Dynamic Management View, sys.dm_os_memory_cache_clock_hands, which allows you to interrogate the passage of the clock hands. Frequently turning hands equates to excessive memory pressure, which will lead to performance problems. Two hours later, I emerged from this rather frightening journey into the heart of SQL Server memory management, fascinated but still unsure if I'd learned anything that I'd put to any practical use. However, I certainly began to agree that there is something almost Tolkeinian in the language of the deep recesses of SQL Server. Cheers, Tony.

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• Data Source Security Part 4

  - by Steve Felts

  So far, I have covered Client Identity and Oracle Proxy Session features, with WLS or database credentials.  This article will cover one more feature, Identify-based pooling.  Then, there is one more topic to cover - how these options play with transactions.Identity-based Connection Pooling An identity based pool creates a heterogeneous pool of connections.  This allows applications to use a JDBC connection with a specific DBMS credential by pooling physical connections with different DBMS credentials.  The DBMS credential is based on either the WebLogic user mapped to a database user or the database user directly, based on the “use database credentials” setting as described earlier. Using this feature enabled with “use database credentials” enabled seems to be what is proposed in the JDBC standard, basically a heterogeneous pool with users specified by getConnection(user, password). The allocation of connections is more complex if Enable Identity Based Connection Pooling attribute is enabled on the data source.  When an application requests a database connection, the WebLogic Server instance selects an existing physical connection or creates a new physical connection with requested DBMS identity. The following section provides information on how heterogeneous connections are created:1. At connection pool initialization, the physical JDBC connections based on the configured or default “initial capacity” are created with the configured default DBMS credential of the data source.2. An application tries to get a connection from a data source.3a. If “use database credentials” is not enabled, the user specified in getConnection is mapped to a DBMS credential, as described earlier.  If the credential map doesn’t have a matching user, the default DBMS credential is used from the datasource descriptor.3b. If “use database credentials” is enabled, the user and password specified in getConnection are used directly.4. The connection pool is searched for a connection with a matching DBMS credential.5. If a match is found, the connection is reserved and returned to the application.6. If no match is found, a connection is created or reused based on the maximum capacity of the pool: - If the maximum capacity has not been reached, a new connection is created with the DBMS credential, reserved, and returned to the application.- If the pool has reached maximum capacity, based on the least recently used (LRU) algorithm, a physical connection is selected from the pool and destroyed. A new connection is created with the DBMS credential, reserved, and returned to the application. It should be clear that finding a matching connection is more expensive than a homogeneous pool.  Destroying a connection and getting a new one is very expensive.  If you can use a normal homogeneous pool or one of the light-weight options (client identity or an Oracle proxy connection), those should be used instead of identity based pooling. Regardless of how physical connections are created, each physical connection in the pool has its own DBMS credential information maintained by the pool. Once a physical connection is reserved by the pool, it does not change its DBMS credential even if the current thread changes its WebLogic user credential and continues to use the same connection. To configure this feature, select Enable Identity Based Connection Pooling.  See http://docs.oracle.com/cd/E24329_01/apirefs.1211/e24401/taskhelp/jdbc/jdbc_datasources/EnableIdentityBasedConnectionPooling.html  "Enable identity-based connection pooling for a JDBC data source" in Oracle WebLogic Server Administration Console Help. You must make the following changes to use Logging Last Resource (LLR) transaction optimization with Identity-based Pooling to get around the problem that multiple users will be accessing the associated transaction table.- You must configure a custom schema for LLR using a fully qualified LLR table name. All LLR connections will then use the named schema rather than the default schema when accessing the LLR transaction table.  - Use database specific administration tools to grant permission to access the named LLR table to all users that could access this table via a global transaction. By default, the LLR table is created during boot by the user configured for the connection in the data source. In most cases, the database will only allow access to this user and not allow access to mapped users. Connections within Transactions Now that we have covered the behavior of all of these various options, it’s time to discuss the exception to all of the rules.  When you get a connection within a transaction, it is associated with the transaction context on a particular WLS instance. When getting a connection with a data source configured with non-XA LLR or 1PC (using the JTS driver) with global transactions, the first connection obtained within the transaction is returned on subsequent connection requests regardless of the values of username/password specified and independent of the associated proxy user session, if any. The connection must be shared among all users of the connection when using LLR or 1PC. For XA data sources, the first connection obtained within the global transaction is returned on subsequent connection requests within the application server, regardless of the values of username/password specified and independent of the associated proxy user session, if any.  The connection must be shared among all users of the connection within a global transaction within the application server/JVM.

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• JBOSS 7.1 started hanging after 6 months of deployment

  - by PVR

  My application is been live from 6 months. The application is host on jboss 7.1 server. From last few days I am finding numerous problem of hanging of jboss server. Though I restart the jboss server again, it does not invoke. I need to restart the server machine itself. Can anyone please let me know what could be the cause of these problems and the workable resolutions or any suggestion ? Kindly dont degrade the question as I am facing a lot problems due to this hanging issue. Also for the information, the application is based on Java, GWT, Hibernate 3. Please find the standalone.xml file in case if it helps. <extensions> <extension module="org.jboss.as.clustering.infinispan"/> <extension module="org.jboss.as.configadmin"/> <extension module="org.jboss.as.connector"/> <extension module="org.jboss.as.deployment-scanner"/> <extension module="org.jboss.as.ee"/> <extension module="org.jboss.as.ejb3"/> <extension module="org.jboss.as.jaxrs"/> <extension module="org.jboss.as.jdr"/> <extension module="org.jboss.as.jmx"/> <extension module="org.jboss.as.jpa"/> <extension module="org.jboss.as.logging"/> <extension module="org.jboss.as.mail"/> <extension module="org.jboss.as.naming"/> <extension module="org.jboss.as.osgi"/> <extension module="org.jboss.as.pojo"/> <extension module="org.jboss.as.remoting"/> <extension module="org.jboss.as.sar"/> <extension module="org.jboss.as.security"/> <extension module="org.jboss.as.threads"/> <extension module="org.jboss.as.transactions"/> <extension module="org.jboss.as.web"/> <extension module="org.jboss.as.webservices"/> <extension module="org.jboss.as.weld"/> </extensions> <system-properties> <property name="org.apache.coyote.http11.Http11Protocol.COMPRESSION" value="on"/> <property name="org.apache.coyote.http11.Http11Protocol.COMPRESSION_MIME_TYPES" value="text/javascript,text/css,text/html,text/xml,text/json"/> </system-properties> <management> <security-realms> <security-realm name="ManagementRealm"> <authentication> <properties path="mgmt-users.properties" relative-to="jboss.server.config.dir"/> </authentication> </security-realm> <security-realm name="ApplicationRealm"> <authentication> <properties path="application-users.properties" relative-to="jboss.server.config.dir"/> </authentication> </security-realm> </security-realms> <management-interfaces> <native-interface security-realm="ManagementRealm"> <socket-binding native="management-native"/> </native-interface> <http-interface security-realm="ManagementRealm"> <socket-binding http="management-http"/> </http-interface> </management-interfaces> </management> <profile> <subsystem xmlns="urn:jboss:domain:logging:1.1"> <console-handler name="CONSOLE"> <level name="INFO"/> <formatter> <pattern-formatter pattern="%d{HH:mm:ss,SSS} %-5p [%c] (%t) %s%E%n"/> </formatter> </console-handler> <periodic-rotating-file-handler name="FILE"> <formatter> <pattern-formatter pattern="%d{HH:mm:ss,SSS} %-5p [%c] (%t) %s%E%n"/> </formatter> <file relative-to="jboss.server.log.dir" path="server.log"/> <suffix value=".yyyy-MM-dd"/> <append value="true"/> </periodic-rotating-file-handler> <logger category="com.arjuna"> <level name="WARN"/> </logger> <logger category="org.apache.tomcat.util.modeler"> <level name="WARN"/> </logger> <logger category="sun.rmi"> <level name="WARN"/> </logger> <logger category="jacorb"> <level name="WARN"/> </logger> <logger category="jacorb.config"> <level name="ERROR"/> </logger> <root-logger> <level name="INFO"/> <handlers> <handler name="CONSOLE"/> <handler name="FILE"/> </handlers> </root-logger> </subsystem> <subsystem xmlns="urn:jboss:domain:configadmin:1.0"/> <subsystem xmlns="urn:jboss:domain:datasources:1.0"> <datasources> <datasource jndi-name="java:jboss/datasources/ExampleDS" pool-name="ExampleDS" enabled="true" use-java-context="true"> <connection-url>jdbc:h2:mem:test;DB_CLOSE_DELAY=-1</connection-url> <driver>h2</driver> <security> <user-name>sa</user-name> <password>sa</password> </security> </datasource> <drivers> <driver name="h2" module="com.h2database.h2"> <xa-datasource-class>org.h2.jdbcx.JdbcDataSource</xa-datasource-class> </driver> </drivers> </datasources> </subsystem> <subsystem xmlns="urn:jboss:domain:deployment-scanner:1.1"> <deployment-scanner path="deployments" relative-to="jboss.server.base.dir" scan-interval="5000"/> </subsystem> <subsystem xmlns="urn:jboss:domain:ee:1.0"/> <subsystem xmlns="urn:jboss:domain:ejb3:1.2"> <session-bean> <stateless> <bean-instance-pool-ref pool-name="slsb-strict-max-pool"/> </stateless> <stateful default-access-timeout="5000" cache-ref="simple"/> <singleton default-access-timeout="5000"/> </session-bean> <pools> <bean-instance-pools> <strict-max-pool name="slsb-strict-max-pool" max-pool-size="20" instance-acquisition-timeout="5" instance-acquisition-timeout-unit="MINUTES"/> <strict-max-pool name="mdb-strict-max-pool" max-pool-size="20" instance-acquisition-timeout="5" instance-acquisition-timeout-unit="MINUTES"/> </bean-instance-pools> </pools> <caches> <cache name="simple" aliases="NoPassivationCache"/> <cache name="passivating" passivation-store-ref="file" aliases="SimpleStatefulCache"/> </caches> <passivation-stores> <file-passivation-store name="file"/> </passivation-stores> <async thread-pool-name="default"/> <timer-service thread-pool-name="default"> <data-store path="timer-service-data" relative-to="jboss.server.data.dir"/> </timer-service> <remote connector-ref="remoting-connector" thread-pool-name="default"/> <thread-pools> <thread-pool name="default"> <max-threads count="10"/> <keepalive-time time="100" unit="milliseconds"/> </thread-pool> </thread-pools> </subsystem> <subsystem xmlns="urn:jboss:domain:infinispan:1.2" default-cache-container="hibernate"> <cache-container name="hibernate" default-cache="local-query"> <local-cache name="entity"> <transaction mode="NON_XA"/> <eviction strategy="LRU" max-entries="10000"/> <expiration max-idle="100000"/> </local-cache> <local-cache name="local-query"> <transaction mode="NONE"/> <eviction strategy="LRU" max-entries="10000"/> <expiration max-idle="100000"/> </local-cache> <local-cache name="timestamps"> <transaction mode="NONE"/> <eviction strategy="NONE"/> </local-cache> </cache-container> </subsystem> <subsystem xmlns="urn:jboss:domain:jaxrs:1.0"/> <subsystem xmlns="urn:jboss:domain:jca:1.1"> <archive-validation enabled="true" fail-on-error="true" fail-on-warn="false"/> <bean-validation enabled="true"/> <default-workmanager> <short-running-threads> <core-threads count="50"/> <queue-length count="50"/> <max-threads count="50"/> <keepalive-time time="10" unit="seconds"/> </short-running-threads> <long-running-threads> <core-threads count="50"/> <queue-length count="50"/> <max-threads count="50"/> <keepalive-time time="100" unit="seconds"/> </long-running-threads> </default-workmanager> <cached-connection-manager/> </subsystem> <subsystem xmlns="urn:jboss:domain:jdr:1.0"/> <subsystem xmlns="urn:jboss:domain:jmx:1.1"> <show-model value="true"/> <remoting-connector/> </subsystem> <subsystem xmlns="urn:jboss:domain:jpa:1.0"> <jpa default-datasource=""/> </subsystem> <subsystem xmlns="urn:jboss:domain:mail:1.0"> <mail-session jndi-name="java:jboss/mail/Default"> <smtp-server outbound-socket-binding-ref="mail-smtp"/> </mail-session> </subsystem> <subsystem xmlns="urn:jboss:domain:naming:1.1"/> <subsystem xmlns="urn:jboss:domain:osgi:1.2" activation="lazy"> <properties> <property name="org.osgi.framework.startlevel.beginning"> 1 </property> </properties> <capabilities> <capability name="javax.servlet.api:v25"/> <capability name="javax.transaction.api"/> <capability name="org.apache.felix.log" startlevel="1"/> <capability name="org.jboss.osgi.logging" startlevel="1"/> <capability name="org.apache.felix.configadmin" startlevel="1"/> <capability name="org.jboss.as.osgi.configadmin" startlevel="1"/> </capabilities> </subsystem> <subsystem xmlns="urn:jboss:domain:pojo:1.0"/> <subsystem xmlns="urn:jboss:domain:remoting:1.1"> <connector name="remoting-connector" socket-binding="remoting" security-realm="ApplicationRealm"/> </subsystem> <subsystem xmlns="urn:jboss:domain:resource-adapters:1.0"/> <subsystem xmlns="urn:jboss:domain:sar:1.0"/> <subsystem xmlns="urn:jboss:domain:security:1.1"> <security-domains> <security-domain name="other" cache-type="default"> <authentication> <login-module code="Remoting" flag="optional"> <module-option name="password-stacking" value="useFirstPass"/> </login-module> <login-module code="RealmUsersRoles" flag="required"> <module-option name="usersProperties" value="${jboss.server.config.dir}/application-users.properties"/> <module-option name="rolesProperties" value="${jboss.server.config.dir}/application-roles.properties"/> <module-option name="realm" value="ApplicationRealm"/> <module-option name="password-stacking" value="useFirstPass"/> </login-module> </authentication> </security-domain> <security-domain name="jboss-web-policy" cache-type="default"> <authorization> <policy-module code="Delegating" flag="required"/> </authorization> </security-domain> <security-domain name="jboss-ejb-policy" cache-type="default"> <authorization> <policy-module code="Delegating" flag="required"/> </authorization> </security-domain> </security-domains> </subsystem> <subsystem xmlns="urn:jboss:domain:threads:1.1"/> <subsystem xmlns="urn:jboss:domain:transactions:1.1"> <core-environment> <process-id> <uuid/> </process-id> </core-environment> <recovery-environment socket-binding="txn-recovery-environment" status-socket-binding="txn-status-manager"/> <coordinator-environment default-timeout="300"/> </subsystem> <subsystem xmlns="urn:jboss:domain:web:1.1" default-virtual-server="default-host" native="false"> <connector name="http" protocol="HTTP/1.1" scheme="http" socket-binding="http"/> <virtual-server name="default-host" enable-welcome-root="false"> <alias name="localhost"/> <alias name="nextenders.com"/> </virtual-server> </subsystem> <subsystem xmlns="urn:jboss:domain:webservices:1.1"> <modify-wsdl-address>true</modify-wsdl-address> <wsdl-host>${jboss.bind.address:127.0.0.1}</wsdl-host> <endpoint-config name="Standard-Endpoint-Config"/> <endpoint-config name="Recording-Endpoint-Config"> <pre-handler-chain name="recording-handlers" protocol-bindings="##SOAP11_HTTP ##SOAP11_HTTP_MTOM ##SOAP12_HTTP ##SOAP12_HTTP_MTOM"> <handler name="RecordingHandler" class="org.jboss.ws.common.invocation.RecordingServerHandler"/> </pre-handler-chain> </endpoint-config> </subsystem> <subsystem xmlns="urn:jboss:domain:weld:1.0"/> </profile> <interfaces> <interface name="management"> <inet-address value="${jboss.bind.address.management:127.0.0.1}"/> </interface> <interface name="public"> <inet-address value="${jboss.bind.address:127.0.0.1}"/> </interface> <interface name="unsecure"> <inet-address value="${jboss.bind.address.unsecure:127.0.0.1}"/> </interface> </interfaces> <socket-binding-group name="standard-sockets" default-interface="public" port-offset="${jboss.socket.binding.port-offset:0}"> <socket-binding name="management-native" interface="management" port="${jboss.management.native.port:9999}"/> <socket-binding name="management-http" interface="management" port="${jboss.management.http.port:9990}"/> <socket-binding name="management-https" interface="management" port="${jboss.management.https.port:9443}"/> <socket-binding name="ajp" port="8009"/> <socket-binding name="http" port="80"/> <socket-binding name="https" port="443"/> <socket-binding name="osgi-http" interface="management" port="8090"/> <socket-binding name="remoting" port="4447"/> <socket-binding name="txn-recovery-environment" port="4712"/> <socket-binding name="txn-status-manager" port="4713"/> <outbound-socket-binding name="mail-smtp"> <remote-destination host="localhost" port="25"/> </outbound-socket-binding> </socket-binding-group>

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• spring jboss ehcache

  - by boyd4715

  I am trying to configure my application to make use of ehCache. I am using Spring 2.5.6, Jboss 5.1.0 GA and its embedded version of Hibernate along with ehCache-core V2.3.1. I have done the following configuration: <property name="hibernateProperties"> <props> <prop key="hibernate.dialect">org.hibernate.dialect.MySQLDialect</prop> <prop key="hibernate.hbm2ddl.auto">update</prop> <prop key="hibernate.show_sql">true</prop> <prop key="hibernate.jdbc.batch_size">20</prop> <prop key="hibernate.cache.provider_class">net.sf.ehcache.hibernate.SingletonEhCacheProvider</prop> <prop key="net.sf.ehcache.configurationResourceName">ehcache.xml</prop> <prop key="hibernate.cache.use_second_level_cache">true</prop> <prop key="hibernate.cache.use_structured_entries">true</prop> <prop key="hibernate.cache.use_query_cache">true</prop> <prop key="hibernate.generate_statistics">true</prop> <!-- prop key="hibernate.cache.use_second_level_cache">true</prop> <prop key="hibernate.cache.region.factory_class">net.sf.ehcache.hibernate.EhCacheRegionFactory</prop> <prop key="hibernate.cache.provider_class">net.sf.ehcache.hibernate.SingletonEhCacheProvider</prop> <prop key="hibernate.cache.use_second_level_cache">true</prop> <prop key="hibernate.cache.use_query_cache">true</prop--> </props> </property> This is my ehcache.xml <defaultCache eternal="false" overflowToDisk="false" maxElementsInMemory="50000" timeToIdleSeconds="30" timeToLiveSeconds="6000" memoryStoreEvictionPolicy="LRU" /> <cache name="com.model.SystemProperty" maxElementsInMemory="5000" eternal="true" overflowToDisk="false" memoryStoreEvictionPolicy="LFU" /> This file is located in my class path. I have added the following to my domain object: @Cache(usage = CacheConcurrencyStrategy.READ_WRITE, region="vsg.ecotrak.admin.store.domain.Store", include="non-lazy") When I start up the server, it gets stuck. Here is the output: 13:17:09,000 INFO [SettingsFactory] Second-level cache: enabled 13:17:09,000 INFO [SettingsFactory] Query cache: enabled 13:17:09,016 INFO [SettingsFactory] Cache region factory : org.hibernate.cache.impl.bridge.RegionFactoryCacheProviderBridge 13:17:09,017 INFO [RegionFactoryCacheProviderBridge] Cache provider: net.sf.ehcache.hibernate.SingletonEhCacheProvider Any Ideal as to why this is happening? I am running on a Windows 7 64 bit if that matters. I downgraded the ehcache jar to V 1.2.3 and the server now starts.

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• Akka framework support for finding duplicate messages

  - by scala_is_awesome

  I'm trying to build a high-performance distributed system with Akka and Scala. If a message requesting an expensive (and side-effect-free) computation arrives, and the exact same computation has already been requested before, I want to avoid computing the result again. If the computation requested previously has already completed and the result is available, I can cache it and re-use it. However, the time window in which duplicate computation can be requested may be arbitrarily small. e.g. I could get a thousand or a million messages requesting the same expensive computation at the same instant for all practical purposes. There is a commercial product called Gigaspaces that supposedly handles this situation. However there seems to be no framework support for dealing with duplicate work requests in Akka at the moment. Given that the Akka framework already has access to all the messages being routed through the framework, it seems that a framework solution could make a lot of sense here. Here is what I am proposing for the Akka framework to do: 1. Create a trait to indicate a type of messages (say, "ExpensiveComputation" or something similar) that are to be subject to the following caching approach. 2. Smartly (hashing etc.) identify identical messages received by (the same or different) actors within a user-configurable time window. Other options: select a maximum buffer size of memory to be used for this purpose, subject to (say LRU) replacement etc. Akka can also choose to cache only the results of messages that were expensive to process; the messages that took very little time to process can be re-processed again if needed; no need to waste precious buffer space caching them and their results. 3. When identical messages (received within that time window, possibly "at the same time instant") are identified, avoid unnecessary duplicate computations. The framework would do this automatically, and essentially, the duplicate messages would never get received by a new actor for processing; they would silently vanish and the result from processing it once (whether that computation was already done in the past, or ongoing right then) would get sent to all appropriate recipients (immediately if already available, and upon completion of the computation if not). Note that messages should be considered identical even if the "reply" fields are different, as long as the semantics/computations they represent are identical in every other respect. Also note that the computation should be purely functional, i.e. free from side-effects, for the caching optimization suggested to work and not change the program semantics at all. If what I am suggesting is not compatible with the Akka way of doing things, and/or if you see some strong reasons why this is a very bad idea, please let me know. Thanks, Is Awesome, Scala

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• Organization &amp; Architecture UNISA Studies &ndash; Chap 4

  - by MarkPearl

  Learning Outcomes Explain the characteristics of memory systems Describe the memory hierarchy Discuss cache memory principles Discuss issues relevant to cache design Describe the cache organization of the Pentium Computer Memory Systems There are key characteristics of memory… Location – internal or external Capacity – expressed in terms of bytes Unit of Transfer – the number of bits read out of or written into memory at a time Access Method – sequential, direct, random or associative From a users perspective the two most important characteristics of memory are… Capacity Performance – access time, memory cycle time, transfer rate The trade off for memory happens along three axis… Faster access time, greater cost per bit Greater capacity, smaller cost per bit Greater capacity, slower access time This leads to people using a tiered approach in their use of memory   As one goes down the hierarchy, the following occurs… Decreasing cost per bit Increasing capacity Increasing access time Decreasing frequency of access of the memory by the processor The use of two levels of memory to reduce average access time works in principle, but only if conditions 1 to 4 apply. A variety of technologies exist that allow us to accomplish this. Thus it is possible to organize data across the hierarchy such that the percentage of accesses to each successively lower level is substantially less than that of the level above. A portion of main memory can be used as a buffer to hold data temporarily that is to be read out to disk. This is sometimes referred to as a disk cache and improves performance in two ways… Disk writes are clustered. Instead of many small transfers of data, we have a few large transfers of data. This improves disk performance and minimizes processor involvement. Some data designed for write-out may be referenced by a program before the next dump to disk. In that case the data is retrieved rapidly from the software cache rather than slowly from disk. Cache Memory Principles Cache memory is substantially faster than main memory. A caching system works as follows.. When a processor attempts to read a word of memory, a check is made to see if this in in cache memory… If it is, the data is supplied, If it is not in the cache, a block of main memory, consisting of a fixed number of words is loaded to the cache. Because of the phenomenon of locality of references, when a block of data is fetched into the cache, it is likely that there will be future references to that same memory location or to other words in the block. Elements of Cache Design While there are a large number of cache implementations, there are a few basic design elements that serve to classify and differentiate cache architectures… Cache Addresses Cache Size Mapping Function Replacement Algorithm Write Policy Line Size Number of Caches Cache Addresses Almost all non-embedded processors support virtual memory. Virtual memory in essence allows a program to address memory from a logical point of view without needing to worry about the amount of physical memory available. When virtual addresses are used the designer may choose to place the cache between the MMU (memory management unit) and the processor or between the MMU and main memory. The disadvantage of virtual memory is that most virtual memory systems supply each application with the same virtual memory address space (each application sees virtual memory starting at memory address 0), which means the cache memory must be completely flushed with each application context switch or extra bits must be added to each line of the cache to identify which virtual address space the address refers to. Cache Size We would like the size of the cache to be small enough so that the overall average cost per bit is close to that of main memory alone and large enough so that the overall average access time is close to that of the cache alone. Also, larger caches are slightly slower than smaller ones. Mapping Function Because there are fewer cache lines than main memory blocks, an algorithm is needed for mapping main memory blocks into cache lines. The choice of mapping function dictates how the cache is organized. Three techniques can be used… Direct – simplest technique, maps each block of main memory into only one possible cache line Associative – Each main memory block to be loaded into any line of the cache Set Associative – exhibits the strengths of both the direct and associative approaches while reducing their disadvantages For detailed explanations of each approach – read the text book (page 148 – 154) Replacement Algorithm For associative and set associating mapping a replacement algorithm is needed to determine which of the existing blocks in the cache must be replaced by a new block. There are four common approaches… LRU (Least recently used) FIFO (First in first out) LFU (Least frequently used) Random selection Write Policy When a block resident in the cache is to be replaced, there are two cases to consider If no writes to that block have happened in the cache – discard it If a write has occurred, a process needs to be initiated where the changes in the cache are propagated back to the main memory. There are several approaches to achieve this including… Write Through – all writes to the cache are done to the main memory as well at the point of the change Write Back – when a block is replaced, all dirty bits are written back to main memory The problem is complicated when we have multiple caches, there are techniques to accommodate for this but I have not summarized them. Line Size When a block of data is retrieved and placed in the cache, not only the desired word but also some number of adjacent words are retrieved. As the block size increases from very small to larger sizes, the hit ratio will at first increase because of the principle of locality, which states that the data in the vicinity of a referenced word are likely to be referenced in the near future. As the block size increases, more useful data are brought into cache. The hit ratio will begin to decrease as the block becomes even bigger and the probability of using the newly fetched information becomes less than the probability of using the newly fetched information that has to be replaced. Two specific effects come into play… Larger blocks reduce the number of blocks that fit into a cache. Because each block fetch overwrites older cache contents, a small number of blocks results in data being overwritten shortly after they are fetched. As a block becomes larger, each additional word is farther from the requested word and therefore less likely to be needed in the near future. The relationship between block size and hit ratio is complex, and no set approach is judged to be the best in all circumstances.   Pentium 4 and ARM cache organizations The processor core consists of four major components: Fetch/decode unit – fetches program instruction in order from the L2 cache, decodes these into a series of micro-operations, and stores the results in the L2 instruction cache Out-of-order execution logic – Schedules execution of the micro-operations subject to data dependencies and resource availability – thus micro-operations may be scheduled for execution in a different order than they were fetched from the instruction stream. As time permits, this unit schedules speculative execution of micro-operations that may be required in the future Execution units – These units execute micro-operations, fetching the required data from the L1 data cache and temporarily storing results in registers Memory subsystem – This unit includes the L2 and L3 caches and the system bus, which is used to access main memory when the L1 and L2 caches have a cache miss and to access the system I/O resources

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• Improving TCP performance over a gigabit network lots of connections and high traffic for storage and streaming services

  - by Linux Guy

  I have two servers, Both servers hardware Specification are Processor : Dual Processor RAM : over 128 G.B Hard disk : SSD Hard disk Outging Traffic bandwidth : 3 Gbps network cards speed : 10 Gbps Server A : for Encoding videos Server B : for storage videos andstream videos over web interface like youtube The inbound bandwidth between two servers is 10Gbps , the outbound bandwidth internet bandwidth is 500Mpbs Both servers using public ip addresses in public and private network Both servers transfer and connection on nginx port , and the server B used for streaming media , like youtube stream videos Both servers in same network , when i do ping from Server A to Server B i got high time latency above 1.0ms , the time range time=52.7 ms to time=215.7 ms - This is the output of iftop utility 353Mb 707Mb 1.04Gb 1.38Gb 1.73Gb mqqqqqqqqqqqqqqqqqqqqqqqqqqqvqqqqqqqqqqqqqqqqqqqqqqqqqqqvqqqqqqqqqqqqqqqqqqqqqqqqqqqvqqqqqqqqqqqqqqqqqqqqqqqqqqqvqqqqqqqqqqqqqqqqqqqqqqqqqqq server.example.com => ip.address 6.36Mb 4.31Mb 1.66Mb <= 158Kb 94.8Kb 35.1Kb server.example.com => ip.address 1.23Mb 4.28Mb 1.12Mb <= 17.1Kb 83.5Kb 21.9Kb server.example.com => ip.address 395Kb 3.89Mb 1.07Mb <= 6.09Kb 109Kb 28.6Kb server.example.com => ip.address 4.55Mb 3.83Mb 1.04Mb <= 55.6Kb 45.4Kb 13.0Kb server.example.com => ip.address 649Kb 3.38Mb 1.47Mb <= 9.00Kb 38.7Kb 16.7Kb server.example.com => ip.address 5.00Mb 3.32Mb 1.80Mb <= 65.7Kb 55.1Kb 29.4Kb server.example.com => ip.address 387Kb 3.13Mb 1.06Mb <= 18.4Kb 39.9Kb 15.0Kb server.example.com => ip.address 3.27Mb 3.11Mb 1.01Mb <= 81.2Kb 64.5Kb 20.9Kb server.example.com => ip.address 1.75Mb 3.08Mb 2.72Mb <= 16.6Kb 35.6Kb 32.5Kb server.example.com => ip.address 1.75Mb 2.90Mb 2.79Mb <= 22.4Kb 32.6Kb 35.6Kb server.example.com => ip.address 3.03Mb 2.78Mb 1.82Mb <= 26.6Kb 27.4Kb 20.2Kb server.example.com => ip.address 2.26Mb 2.66Mb 1.36Mb <= 51.7Kb 49.1Kb 24.4Kb server.example.com => ip.address 586Kb 2.50Mb 1.03Mb <= 4.17Kb 26.1Kb 10.7Kb server.example.com => ip.address 2.42Mb 2.49Mb 2.44Mb <= 31.6Kb 29.7Kb 29.9Kb server.example.com => ip.address 2.41Mb 2.46Mb 2.41Mb <= 26.4Kb 24.5Kb 23.8Kb server.example.com => ip.address 2.37Mb 2.39Mb 2.40Mb <= 28.9Kb 27.0Kb 28.5Kb server.example.com => ip.address 525Kb 2.20Mb 1.05Mb <= 7.03Kb 26.0Kb 12.8Kb qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq TX: cum: 102GB peak: 1.65Gb rates: 1.46Gb 1.44Gb 1.48Gb RX: 1.31GB 24.3Mb 19.5Mb 18.9Mb 20.0Mb TOTAL: 103GB 1.67Gb 1.48Gb 1.46Gb 1.50Gb I check the transfer speed using iperf utility From Server A to Server B # iperf -c 0.0.0.2 -p 8777 ------------------------------------------------------------ Client connecting to 0.0.0.2, TCP port 8777 TCP window size: 85.3 KByte (default) ------------------------------------------------------------ [ 3] local 0.0.0.1 port 38895 connected with 0.0.0.2 port 8777 [ ID] Interval Transfer Bandwidth [ 3] 0.0-10.8 sec 528 KBytes 399 Kbits/sec My Current Connections in Server B # netstat -an|grep ":8777"|awk '/tcp/ {print $6}'|sort -nr| uniq -c 2072 TIME_WAIT 28 SYN_RECV 1 LISTEN 189 LAST_ACK 139 FIN_WAIT2 373 FIN_WAIT1 3381 ESTABLISHED 34 CLOSING Server A Network Card Information Settings for eth0: Supported ports: [ TP ] Supported link modes: 100baseT/Full 1000baseT/Full 10000baseT/Full Supported pause frame use: No Supports auto-negotiation: Yes Advertised link modes: 10000baseT/Full Advertised pause frame use: No Advertised auto-negotiation: Yes Speed: 10000Mb/s Duplex: Full Port: Twisted Pair PHYAD: 0 Transceiver: external Auto-negotiation: on MDI-X: Unknown Supports Wake-on: d Wake-on: d Current message level: 0x00000007 (7) drv probe link Link detected: yes Server B Network Card Information Settings for eth2: Supported ports: [ FIBRE ] Supported link modes: 10000baseT/Full Supported pause frame use: No Supports auto-negotiation: No Advertised link modes: 10000baseT/Full Advertised pause frame use: No Advertised auto-negotiation: No Speed: 10000Mb/s Duplex: Full Port: Direct Attach Copper PHYAD: 0 Transceiver: external Auto-negotiation: off Supports Wake-on: d Wake-on: d Current message level: 0x00000007 (7) drv probe link Link detected: yes ifconfig server A eth0 Link encap:Ethernet HWaddr 00:25:90:ED:9E:AA inet addr:0.0.0.1 Bcast:0.0.0.255 Mask:255.255.255.0 UP BROADCAST RUNNING MULTICAST MTU:1500 Metric:1 RX packets:1202795665 errors:0 dropped:64334 overruns:0 frame:0 TX packets:2313161968 errors:0 dropped:0 overruns:0 carrier:0 collisions:0 txqueuelen:1000 RX bytes:893413096188 (832.0 GiB) TX bytes:3360949570454 (3.0 TiB) lo Link encap:Local Loopback inet addr:127.0.0.1 Mask:255.0.0.0 inet6 addr: ::1/128 Scope:Host UP LOOPBACK RUNNING MTU:65536 Metric:1 RX packets:2207544 errors:0 dropped:0 overruns:0 frame:0 TX packets:2207544 errors:0 dropped:0 overruns:0 carrier:0 collisions:0 txqueuelen:0 RX bytes:247769175 (236.2 MiB) TX bytes:247769175 (236.2 MiB) ifconfig Server B eth2 Link encap:Ethernet HWaddr 00:25:90:82:C4:FE inet addr:0.0.0.2 Bcast:0.0.0.2 Mask:255.255.255.0 UP BROADCAST RUNNING MULTICAST MTU:1500 Metric:1 RX packets:39973046980 errors:0 dropped:1828387600 overruns:0 frame:0 TX packets:69618752480 errors:0 dropped:0 overruns:0 carrier:0 collisions:0 txqueuelen:1000 RX bytes:3013976063688 (2.7 TiB) TX bytes:102250230803933 (92.9 TiB) lo Link encap:Local Loopback inet addr:127.0.0.1 Mask:255.0.0.0 inet6 addr: ::1/128 Scope:Host UP LOOPBACK RUNNING MTU:65536 Metric:1 RX packets:1049495 errors:0 dropped:0 overruns:0 frame:0 TX packets:1049495 errors:0 dropped:0 overruns:0 carrier:0 collisions:0 txqueuelen:0 RX bytes:129012422 (123.0 MiB) TX bytes:129012422 (123.0 MiB) Netstat -i on Server B # netstat -i Kernel Interface table Iface MTU Met RX-OK RX-ERR RX-DRP RX-OVR TX-OK TX-ERR TX-DRP TX-OVR Flg eth2 9000 0 42098629968 0 2131223717 0 73698797854 0 0 0 BMRU lo 65536 0 1077908 0 0 0 1077908 0 0 0 LRU I Turn up send/receive buffers on the network card to 2048 and problem still persist I increase the MTU for server A and problem still persist and i increase the MTU for server B for better connectivity and transfer speed but it couldn't transfer at all The problem is : as you can see from iperf utility, the transfer speed from server A to server B slow when i restart network service in server B the transfer in server A at full speed, after 2 minutes , it's getting slow How could i troubleshoot slow speed issue and fix it in server B ? Notice : if there any other commands i should execute in servers for more information, so it might help resolve the problem , let me know in comments

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• Spring hibernate ehcache setup

  - by Johan Sjöberg

  I have some problems getting the hibernate second level cache to work for caching domain objects. According to the ehcache documentation it shouldn't be too complicated to add caching to my existing working application. I have the following setup (only relevant snippets are outlined): @Entity @Cache(usage = CacheConcurrencyStrategy.NONSTRICT_READ_WRITE public void Entity { // ... } ehcache-entity.xml <cache name="com.company.Entity" eternal="false" maxElementsInMemory="10000" overflowToDisk="true" diskPersistent="false" timeToIdleSeconds="0" timeToLiveSeconds="300" memoryStoreEvictionPolicy="LRU" /> ApplicationContext.xml <bean class="org.springframework.orm.hibernate3.annotation.AnnotationSessionFactoryBean"> <property name="dataSource" ref="ds" /> <property name="annotatedClasses"> <list> <value>com.company.Entity</value> </list> </property> <property name="hibernateProperties"> <props> <prop key="hibernate.generate_statistics">true</prop> <prop key="hibernate.cache.use_second_level_cache">true</prop> <prop key="net.sf.ehcache.configurationResourceName">/ehcache-entity.xml</prop> <prop key="hibernate.cache.region.factory_class">net.sf.ehcache.hibernate.SingletonEhCacheRegionFactory</prop> .... </property> </bean> Maven dependencies <dependency> <groupId>org.hibernate</groupId> <artifactId>hibernate-annotations</artifactId> <version>3.4.0.GA</version> </dependency> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-hibernate3</artifactId> <version>2.0.8</version> <exclusions> <exclusion> <artifactId>hibernate</artifactId> <groupId>org.hibernate</groupId> </exclusion> </exclusions> </dependency> <dependency> <groupId>net.sf.ehcache</groupId> <artifactId>ehcache-core</artifactId> <version>2.3.2</version> </dependency> A test class is used which enables cache statistics: Cache cache = cacheManager.getCache("com.company.Entity"); cache.setStatisticsAccuracy(Statistics.STATISTICS_ACCURACY_GUARANTEED); cache.setStatisticsEnabled(true); // store, read etc ... cache.getStatistics().getMemoryStoreObjectCount(); // returns 0 No operation seems to trigger any cache changes. What am I missing? Currently I'm using HibernateTemplate in the DAO, perhaps that has some impact. [EDIT] The only ehcache log output when set to DEBUG is: SettingsFactory: Cache region factory : net.sf.ehcache.hibernate.SingletonEhCacheRegionFactory

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• Scala n00b: Critique my code

  - by Peter

  G'day everyone, I'm a Scala n00b (but am experienced with other languages) and am learning the language as I find time - very much enjoying it so far! Usually when learning a new language the first thing I do is implement Conway's Game of Life, since it's just complex enough to give a good sense of the language, but small enough in scope to be able to whip up in a couple of hours (most of which is spent wrestling with syntax). Anyhoo, having gone through this exercise with Scala I was hoping the Scala gurus out there might take a look at the code I've ended up with and provide feedback on it. I'm after anything - algorithmic improvements (particularly concurrent solutions!), stylistic improvements, alternative APIs or language constructs, disgust at the length of my function names - whatever feedback you've got, I'm keen to hear it! You should be able to run the following script via "scala GameOfLife.scala" - by default it will run a 20x20 board with a single glider on it - please feel free to experiment. // CONWAY'S GAME OF LIFE (SCALA) abstract class GameOfLifeBoard(val aliveCells : Set[Tuple2[Int, Int]]) { // Executes a "time tick" - returns a new board containing the next generation def tick : GameOfLifeBoard // Is the board empty? def empty : Boolean = aliveCells.size == 0 // Is the given cell alive? protected def alive(cell : Tuple2[Int, Int]) : Boolean = aliveCells contains cell // Is the given cell dead? protected def dead(cell : Tuple2[Int, Int]) : Boolean = !alive(cell) } class InfiniteGameOfLifeBoard(aliveCells : Set[Tuple2[Int, Int]]) extends GameOfLifeBoard(aliveCells) { // Executes a "time tick" - returns a new board containing the next generation override def tick : GameOfLifeBoard = new InfiniteGameOfLifeBoard(nextGeneration) // The next generation of this board protected def nextGeneration : Set[Tuple2[Int, Int]] = aliveCells flatMap neighbours filter shouldCellLiveInNextGeneration // Should the given cell should live in the next generation? protected def shouldCellLiveInNextGeneration(cell : Tuple2[Int, Int]) : Boolean = (alive(cell) && (numberOfAliveNeighbours(cell) == 2 || numberOfAliveNeighbours(cell) == 3)) || (dead(cell) && numberOfAliveNeighbours(cell) == 3) // The number of alive neighbours for the given cell protected def numberOfAliveNeighbours(cell : Tuple2[Int, Int]) : Int = aliveNeighbours(cell) size // Returns the alive neighbours for the given cell protected def aliveNeighbours(cell : Tuple2[Int, Int]) : Set[Tuple2[Int, Int]] = aliveCells intersect neighbours(cell) // Returns all neighbours (whether dead or alive) for the given cell protected def neighbours(cell : Tuple2[Int, Int]) : Set[Tuple2[Int, Int]] = Set((cell._1-1, cell._2-1), (cell._1, cell._2-1), (cell._1+1, cell._2-1), (cell._1-1, cell._2), (cell._1+1, cell._2), (cell._1-1, cell._2+1), (cell._1, cell._2+1), (cell._1+1, cell._2+1)) // Information on where the currently live cells are protected def xVals = aliveCells map { cell => cell._1 } protected def xMin = (xVals reduceLeft (_ min _)) - 1 protected def xMax = (xVals reduceLeft (_ max _)) + 1 protected def xRange = xMin until xMax + 1 protected def yVals = aliveCells map { cell => cell._2 } protected def yMin = (yVals reduceLeft (_ min _)) - 1 protected def yMax = (yVals reduceLeft (_ max _)) + 1 protected def yRange = yMin until yMax + 1 // Returns a simple graphical representation of this board override def toString : String = { var result = "" for (y <- yRange) { for (x <- xRange) { if (alive (x,y)) result += "# " else result += ". " } result += "\n" } result } // Equality stuff override def equals(other : Any) : Boolean = { other match { case that : InfiniteGameOfLifeBoard => (that canEqual this) && that.aliveCells == this.aliveCells case _ => false } } def canEqual(other : Any) : Boolean = other.isInstanceOf[InfiniteGameOfLifeBoard] override def hashCode = aliveCells.hashCode } class FiniteGameOfLifeBoard(val boardWidth : Int, val boardHeight : Int, aliveCells : Set[Tuple2[Int, Int]]) extends InfiniteGameOfLifeBoard(aliveCells) { override def tick : GameOfLifeBoard = new FiniteGameOfLifeBoard(boardWidth, boardHeight, nextGeneration) // Determines the coordinates of all of the neighbours of the given cell override protected def neighbours(cell : Tuple2[Int, Int]) : Set[Tuple2[Int, Int]] = super.neighbours(cell) filter { cell => cell._1 >= 0 && cell._1 < boardWidth && cell._2 >= 0 && cell._2 < boardHeight } // Information on where the currently live cells are override protected def xRange = 0 until boardWidth override protected def yRange = 0 until boardHeight // Equality stuff override def equals(other : Any) : Boolean = { other match { case that : FiniteGameOfLifeBoard => (that canEqual this) && that.boardWidth == this.boardWidth && that.boardHeight == this.boardHeight && that.aliveCells == this.aliveCells case _ => false } } override def canEqual(other : Any) : Boolean = other.isInstanceOf[FiniteGameOfLifeBoard] override def hashCode : Int = { 41 * ( 41 * ( 41 + super.hashCode ) + boardHeight.hashCode ) + boardWidth.hashCode } } class GameOfLife(initialBoard: GameOfLifeBoard) { // Run the game of life until the board is empty or the exact same board is seen twice // Important note: this method does NOT necessarily terminate!! def go : Unit = { var currentBoard = initialBoard var previousBoards = List[GameOfLifeBoard]() while (!currentBoard.empty && !(previousBoards contains currentBoard)) { print(27.toChar + "[2J") // ANSI: clear screen print(27.toChar + "[;H") // ANSI: move cursor to top left corner of screen println(currentBoard.toString) Thread.sleep(75) // Warning: unbounded list concatenation can result in OutOfMemoryExceptions ####TODO: replace with LRU bounded list previousBoards = List(currentBoard) ::: previousBoards currentBoard = currentBoard tick } // Print the final board print(27.toChar + "[2J") // ANSI: clear screen print(27.toChar + "[;H") // ANSI: move cursor to top left corner of screen println(currentBoard.toString) } } // Script starts here val simple = Set((1,1)) val square = Set((4,4), (4,5), (5,4), (5,5)) val glider = Set((2,1), (3,2), (1,3), (2,3), (3,3)) val initialBoard = glider (new GameOfLife(new FiniteGameOfLifeBoard(20, 20, initialBoard))).go //(new GameOfLife(new InfiniteGameOfLifeBoard(initialBoard))).go // COPYRIGHT PETER MONKS 2010 Thanks! Peter

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• How to tune down the Hyperic built-in postgresql database for a small setup

  - by Svish

  We are testing out Hyperic 4.5.1 in a quite small environment for now. Currently there are just 1-5 agents and there probably won't be any more than 10-15. When I run ps ax there are 20(!) postgres processes running. For a small setup like this, that can't be necessary, can it? I'm a software developer and don't have much experience with setting up servers and such though, so don't really know. Either way, what settings are appropriate for a small Hyperic setup like this? Current, default and untouched configuration file, hqdb/data/postgresql.conf: # ----------------------------- # PostgreSQL configuration file # ----------------------------- # # This file consists of lines of the form: # # name = value # # (The '=' is optional.) White space may be used. Comments are introduced # with '#' anywhere on a line. The complete list of option names and # allowed values can be found in the PostgreSQL documentation. The # commented-out settings shown in this file represent the default values. # # Please note that re-commenting a setting is NOT sufficient to revert it # to the default value, unless you restart the server. # # Any option can also be given as a command line switch to the server, # e.g., 'postgres -c log_connections=on'. Some options can be changed at # run-time with the 'SET' SQL command. # # This file is read on server startup and when the server receives a # SIGHUP. If you edit the file on a running system, you have to SIGHUP the # server for the changes to take effect, or use "pg_ctl reload". Some # settings, which are marked below, require a server shutdown and restart # to take effect. # # Memory units: kB = kilobytes MB = megabytes GB = gigabytes # Time units: ms = milliseconds s = seconds min = minutes h = hours d = days #--------------------------------------------------------------------------- # FILE LOCATIONS #--------------------------------------------------------------------------- # The default values of these variables are driven from the -D command line # switch or PGDATA environment variable, represented here as ConfigDir. #data_directory = 'ConfigDir' # use data in another directory # (change requires restart) #hba_file = 'ConfigDir/pg_hba.conf' # host-based authentication file # (change requires restart) #ident_file = 'ConfigDir/pg_ident.conf' # ident configuration file # (change requires restart) # If external_pid_file is not explicitly set, no extra PID file is written. #external_pid_file = '(none)' # write an extra PID file # (change requires restart) #--------------------------------------------------------------------------- # CONNECTIONS AND AUTHENTICATION #--------------------------------------------------------------------------- # - Connection Settings - #listen_addresses = 'localhost' # what IP address(es) to listen on; # comma-separated list of addresses; # defaults to 'localhost', '*' = all # (change requires restart) port = 9432 # (change requires restart) max_connections = 100 # (change requires restart) # Note: increasing max_connections costs ~400 bytes of shared memory per # connection slot, plus lock space (see max_locks_per_transaction). You # might also need to raise shared_buffers to support more connections. #superuser_reserved_connections = 3 # (change requires restart) #unix_socket_directory = '' # (change requires restart) #unix_socket_group = '' # (change requires restart) #unix_socket_permissions = 0777 # octal # (change requires restart) #bonjour_name = '' # defaults to the computer name # (change requires restart) # - Security & Authentication - #authentication_timeout = 1min # 1s-600s #ssl = off # (change requires restart) #password_encryption = on #db_user_namespace = off # Kerberos #krb_server_keyfile = '' # (change requires restart) #krb_srvname = 'postgres' # (change requires restart) #krb_server_hostname = '' # empty string matches any keytab entry # (change requires restart) #krb_caseins_users = off # (change requires restart) # - TCP Keepalives - # see 'man 7 tcp' for details #tcp_keepalives_idle = 0 # TCP_KEEPIDLE, in seconds; # 0 selects the system default #tcp_keepalives_interval = 0 # TCP_KEEPINTVL, in seconds; # 0 selects the system default #tcp_keepalives_count = 0 # TCP_KEEPCNT; # 0 selects the system default #--------------------------------------------------------------------------- # RESOURCE USAGE (except WAL) #--------------------------------------------------------------------------- # - Memory - shared_buffers = 64MB # min 128kB or max_connections*16kB # (change requires restart) #temp_buffers = 8MB # min 800kB #max_prepared_transactions = 5 # can be 0 or more # (change requires restart) # Note: increasing max_prepared_transactions costs ~600 bytes of shared memory # per transaction slot, plus lock space (see max_locks_per_transaction). work_mem = 2MB # min 64kB maintenance_work_mem = 32MB # min 1MB #max_stack_depth = 2MB # min 100kB # - Free Space Map - max_fsm_pages = 204800 # min max_fsm_relations*16, 6 bytes each # (change requires restart) #max_fsm_relations = 1000 # min 100, ~70 bytes each # (change requires restart) # - Kernel Resource Usage - #max_files_per_process = 1000 # min 25 # (change requires restart) #shared_preload_libraries = '' # (change requires restart) # - Cost-Based Vacuum Delay - #vacuum_cost_delay = 0 # 0-1000 milliseconds #vacuum_cost_page_hit = 1 # 0-10000 credits #vacuum_cost_page_miss = 10 # 0-10000 credits #vacuum_cost_page_dirty = 20 # 0-10000 credits #vacuum_cost_limit = 200 # 0-10000 credits # - Background writer - #bgwriter_delay = 200ms # 10-10000ms between rounds #bgwriter_lru_percent = 1.0 # 0-100% of LRU buffers scanned/round #bgwriter_lru_maxpages = 5 # 0-1000 buffers max written/round #bgwriter_all_percent = 0.333 # 0-100% of all buffers scanned/round #bgwriter_all_maxpages = 5 # 0-1000 buffers max written/round #--------------------------------------------------------------------------- # WRITE AHEAD LOG #--------------------------------------------------------------------------- # - Settings - fsync = on # turns forced synchronization on or off #wal_sync_method = fsync # the default is the first option # supported by the operating system: # open_datasync # fdatasync # fsync # fsync_writethrough # open_sync #full_page_writes = on # recover from partial page writes #wal_buffers = 64kB # min 32kB # (change requires restart) commit_delay = 100000 # range 0-100000, in microseconds #commit_siblings = 5 # range 1-1000 # - Checkpoints - checkpoint_segments = 10 # in logfile segments, min 1, 16MB each #checkpoint_timeout = 5min # range 30s-1h #checkpoint_warning = 30s # 0 is off # - Archiving - #archive_command = '' # command to use to archive a logfile segment #archive_timeout = 0 # force a logfile segment switch after this # many seconds; 0 is off #--------------------------------------------------------------------------- # QUERY TUNING #--------------------------------------------------------------------------- # - Planner Method Configuration - #enable_bitmapscan = on #enable_hashagg = on #enable_hashjoin = on #enable_indexscan = on #enable_mergejoin = on #enable_nestloop = on #enable_seqscan = on #enable_sort = on #enable_tidscan = on # - Planner Cost Constants - #seq_page_cost = 1.0 # measured on an arbitrary scale #random_page_cost = 4.0 # same scale as above #cpu_tuple_cost = 0.01 # same scale as above #cpu_index_tuple_cost = 0.005 # same scale as above #cpu_operator_cost = 0.0025 # same scale as above #effective_cache_size = 128MB # - Genetic Query Optimizer - #geqo = on #geqo_threshold = 12 #geqo_effort = 5 # range 1-10 #geqo_pool_size = 0 # selects default based on effort #geqo_generations = 0 # selects default based on effort #geqo_selection_bias = 2.0 # range 1.5-2.0 # - Other Planner Options - #default_statistics_target = 10 # range 1-1000 #constraint_exclusion = off #from_collapse_limit = 8 #join_collapse_limit = 8 # 1 disables collapsing of explicit # JOINs #--------------------------------------------------------------------------- # ERROR REPORTING AND LOGGING #--------------------------------------------------------------------------- # - Where to Log - log_destination = 'stderr' # Valid values are combinations of # stderr, syslog and eventlog, # depending on platform. # This is used when logging to stderr: redirect_stderr = on # Enable capturing of stderr into log # files # (change requires restart) # These are only used if redirect_stderr is on: log_directory = '../../logs' # Directory where log files are written # Can be absolute or relative to PGDATA log_filename = 'hqdb-%Y-%m-%d.log' # Log file name pattern. # Can include strftime() escapes #log_truncate_on_rotation = off # If on, any existing log file of the same # name as the new log file will be # truncated rather than appended to. But # such truncation only occurs on # time-driven rotation, not on restarts # or size-driven rotation. Default is # off, meaning append to existing files # in all cases. log_rotation_age = 1d # Automatic rotation of logfiles will # happen after that time. 0 to # disable. #log_rotation_size = 10MB # Automatic rotation of logfiles will # happen after that much log # output. 0 to disable. # These are relevant when logging to syslog: #syslog_facility = 'LOCAL0' #syslog_ident = 'postgres' # - When to Log - #client_min_messages = notice # Values, in order of decreasing detail: # debug5 # debug4 # debug3 # debug2 # debug1 # log # notice # warning # error #log_min_messages = notice # Values, in order of decreasing detail: # debug5 # debug4 # debug3 # debug2 # debug1 # info # notice # warning # error # log # fatal # panic #log_error_verbosity = default # terse, default, or verbose messages #log_min_error_statement = error # Values in order of increasing severity: # debug5 # debug4 # debug3 # debug2 # debug1 # info # notice # warning # error # fatal # panic (effectively off) log_min_duration_statement = 10000 # -1 is disabled, 0 logs all statements # and their durations. #silent_mode = off # DO NOT USE without syslog or # redirect_stderr # (change requires restart) # - What to Log - #debug_print_parse = off #debug_print_rewritten = off #debug_print_plan = off #debug_pretty_print = off #log_connections = off #log_disconnections = off #log_duration = off #log_line_prefix = '' # Special values: # %u = user name # %d = database name # %r = remote host and port # %h = remote host # %p = PID # %t = timestamp (no milliseconds) # %m = timestamp with milliseconds # %i = command tag # %c = session id # %l = session line number # %s = session start timestamp # %x = transaction id # %q = stop here in non-session # processes # %% = '%' # e.g. '<%u%%%d> ' #log_statement = 'none' # none, ddl, mod, all #log_hostname = off #--------------------------------------------------------------------------- # RUNTIME STATISTICS #--------------------------------------------------------------------------- # - Query/Index Statistics Collector - #stats_command_string = on #update_process_title = on stats_start_collector = on # needed for block or row stats # (change requires restart) stats_block_level = on stats_row_level = on stats_reset_on_server_start = off # (change requires restart) # - Statistics Monitoring - #log_parser_stats = off #log_planner_stats = off #log_executor_stats = off #log_statement_stats = off #--------------------------------------------------------------------------- # AUTOVACUUM PARAMETERS #--------------------------------------------------------------------------- #autovacuum = off # enable autovacuum subprocess? # 'on' requires stats_start_collector # and stats_row_level to also be on #autovacuum_naptime = 1min # time between autovacuum runs #autovacuum_vacuum_threshold = 500 # min # of tuple updates before # vacuum #autovacuum_analyze_threshold = 250 # min # of tuple updates before # analyze #autovacuum_vacuum_scale_factor = 0.2 # fraction of rel size before # vacuum #autovacuum_analyze_scale_factor = 0.1 # fraction of rel size before # analyze #autovacuum_freeze_max_age = 200000000 # maximum XID age before forced vacuum # (change requires restart) #autovacuum_vacuum_cost_delay = -1 # default vacuum cost delay for # autovacuum, -1 means use # vacuum_cost_delay #autovacuum_vacuum_cost_limit = -1 # default vacuum cost limit for # autovacuum, -1 means use # vacuum_cost_limit #--------------------------------------------------------------------------- # CLIENT CONNECTION DEFAULTS #--------------------------------------------------------------------------- # - Statement Behavior - #search_path = '"$user",public' # schema names #default_tablespace = '' # a tablespace name, '' uses # the default #check_function_bodies = on #default_transaction_isolation = 'read committed' #default_transaction_read_only = off #statement_timeout = 0 # 0 is disabled #vacuum_freeze_min_age = 100000000 # - Locale and Formatting - datestyle = 'iso, mdy' #timezone = unknown # actually, defaults to TZ # environment setting #timezone_abbreviations = 'Default' # select the set of available timezone # abbreviations. Currently, there are # Default # Australia # India # However you can also create your own # file in share/timezonesets/. #extra_float_digits = 0 # min -15, max 2 #client_encoding = sql_ascii # actually, defaults to database # encoding # These settings are initialized by initdb -- they might be changed lc_messages = 'C' # locale for system error message # strings lc_monetary = 'C' # locale for monetary formatting lc_numeric = 'C' # locale for number formatting lc_time = 'C' # locale for time formatting # - Other Defaults - #explain_pretty_print = on #dynamic_library_path = '$libdir' #local_preload_libraries = '' #--------------------------------------------------------------------------- # LOCK MANAGEMENT #--------------------------------------------------------------------------- #deadlock_timeout = 1s #max_locks_per_transaction = 64 # min 10 # (change requires restart) # Note: each lock table slot uses ~270 bytes of shared memory, and there are # max_locks_per_transaction * (max_connections + max_prepared_transactions) # lock table slots. #--------------------------------------------------------------------------- # VERSION/PLATFORM COMPATIBILITY #--------------------------------------------------------------------------- # - Previous Postgres Versions - #add_missing_from = off #array_nulls = on #backslash_quote = safe_encoding # on, off, or safe_encoding #default_with_oids = off #escape_string_warning = on #standard_conforming_strings = off #regex_flavor = advanced # advanced, extended, or basic #sql_inheritance = on # - Other Platforms & Clients - #transform_null_equals = off #--------------------------------------------------------------------------- # CUSTOMIZED OPTIONS #--------------------------------------------------------------------------- #custom_variable_classes = '' # list of custom variable class names SELECT * FROM pg_stat_activity; datid | datname | procpid | usesysid | usename | current_query | waiting | query_start | backend_start | client_addr | client_port -------+---------+---------+----------+---------+---------------------------------+---------+-------------------------------+-------------------------------+-------------+------------- 16384 | hqdb | 3267 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.036781+01 | 2011-02-08 15:51:20.02413+01 | 127.0.0.1 | 47892 16384 | hqdb | 3268 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.050994+01 | 2011-02-08 15:51:20.047393+01 | 127.0.0.1 | 47893 16384 | hqdb | 3269 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.056661+01 | 2011-02-08 15:51:20.053201+01 | 127.0.0.1 | 47894 16384 | hqdb | 3271 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.062351+01 | 2011-02-08 15:51:20.058822+01 | 127.0.0.1 | 47895 16384 | hqdb | 3272 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.068328+01 | 2011-02-08 15:51:20.064517+01 | 127.0.0.1 | 47896 16384 | hqdb | 3273 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.07444+01 | 2011-02-08 15:51:20.070755+01 | 127.0.0.1 | 47897 16384 | hqdb | 3274 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.080941+01 | 2011-02-08 15:51:20.076983+01 | 127.0.0.1 | 47898 16384 | hqdb | 3275 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.08741+01 | 2011-02-08 15:51:20.083697+01 | 127.0.0.1 | 47899 16384 | hqdb | 3276 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.093597+01 | 2011-02-08 15:51:20.089977+01 | 127.0.0.1 | 47900 16384 | hqdb | 3277 | 10 | hqadmin | <IDLE> in transaction | f | 2011-02-08 15:51:20.133974+01 | 2011-02-08 15:51:20.096149+01 | 127.0.0.1 | 47901 16384 | hqdb | 3308 | 10 | hqadmin | <IDLE> | f | 2011-02-09 10:49:27.402197+01 | 2011-02-08 15:51:29.826321+01 | 127.0.0.1 | 47902 16384 | hqdb | 3309 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.572395+01 | 2011-02-08 15:51:29.865243+01 | 127.0.0.1 | 47903 16384 | hqdb | 3310 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.586273+01 | 2011-02-08 15:51:29.874346+01 | 127.0.0.1 | 47904 16384 | hqdb | 3311 | 10 | hqadmin | <IDLE> | f | 2011-02-09 10:10:03.024088+01 | 2011-02-08 15:51:29.883598+01 | 127.0.0.1 | 47905 16384 | hqdb | 3312 | 10 | hqadmin | <IDLE> in transaction | f | 2011-02-08 15:51:35.804457+01 | 2011-02-08 15:51:29.892925+01 | 127.0.0.1 | 47906 16384 | hqdb | 3418 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.580207+01 | 2011-02-08 15:51:55.56911+01 | 127.0.0.1 | 47910 16384 | hqdb | 3419 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.59781+01 | 2011-02-08 15:51:55.588609+01 | 127.0.0.1 | 47911 16384 | hqdb | 3422 | 10 | hqadmin | <IDLE> | f | 2011-02-09 10:10:02.668836+01 | 2011-02-08 15:51:55.603076+01 | 127.0.0.1 | 47914 16384 | hqdb | 3421 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.770427+01 | 2011-02-08 15:51:55.603086+01 | 127.0.0.1 | 47913 16384 | hqdb | 3420 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.680785+01 | 2011-02-08 15:51:55.637058+01 | 127.0.0.1 | 47912 16384 | hqdb | 18233 | 10 | hqadmin | SELECT * FROM pg_stat_activity; | f | 2011-02-09 10:49:29.688949+01 | 2011-02-09 10:48:13.031475+01 | | -1 (21 rows)

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