JSF view scope with multiple tabs using JavaScript and sessionStorage

JSF applications often use view scope for server-side state. While that is convenient it can be problematic to handle multiple browser tabs. The backend has no way of knowing which tab the user is working with. The session cookie is global for the entire browser and any URL
parameters or hidden parameters will be copied to new tabs. What to do? And even worse, what to do when you have to live with an ancient version of JSF (2.0 in our case)?

It is fairly easy to write a custom view scope on the server side, been there done that a few times. In order to pick the right beans it needs to identify the active tab. The browser will not help us, so as usual these days we must resort to JavaScript.

My first idea was to use the sessionStorage attribute. Add a script to the top of every page that looks for a window id in the sessionStorage. If it is found, add a hidden field with it to all forms in the loaded page, register a click handler that prevents navigation from links and sets the location manually with the id appended as a query parameter, override window.open in order to append the id as a query parameter in that case as well and finally register a ajaxPrefilter handler with jQuery in order to intercept AJAX requests and add the id as a request header. Oh, and rewrite the history in order to remove the id from the browser’s address line as well.

Combined with a servlet filter that denies requests that lack the window id by returning a short JavaScript function that sets it and retries this worked well – in Chrome. Internet Explorer was not as helpful. According to the specification:

When a new top-level browsing context is created by cloning an existing browsing context, the new browsing context must start with the same session storage areas as the original, but the two sets must from that point on be considered separate, not affecting each other in any way.

The major browsers diverge in how they interpret that. To cut things short my cunning plan failed.

Fortunately there is an attribute that is unique across browser tabs: window.name! I changed the code to store the unique id in window.name instead and voila! It worked.

Lesson learned – as usual the browsers can be trusted not to be consistent. Take care with sessionStorage.

Categories: Java

Oracle AQ JMS Performance


AQ is Oracle’s message queue implementation. Well, one of them. It supports JMS 1.1 and is included in all versions of the Oracle database, even the free version. It has been battle-tested for twenty years and last time I checked (don’t take my word for it) it required no extra license. What’s not to like?

AQ uses normal database constructs such as tables and SQL commands. That has several advantages, not least that the normal database performance tuning tools and methods can be used. Plus it makes it very easy to instrument and manipulate queues programmatically.

For the most part, tuning AQ is nothing special. However, there are a few dark corners. Stay tuned.

Basic configuration

First of all we need a user with AQ privileges and quota on a tablespace:

create user aqtest identified by whatever
  quota unlimited on users default tablespace users;
grant aq_administrator_role to aqtest;
grant create session to aqtest;

With a user in place we can create a queue table and a queue (as aqtest):

    queue_table        => 'test_qtab',
    queue_payload_type => 'sys.aq$_jms_message',
    storage_clause     =>
    'lob (user_data.bytes_lob) store as securefile ' ||
    '(retention none cache) ' ||
    'lob (user_data.text_lob) store as securefile  ' ||
    '(retention none cache) '  ||
    'opaque type user_prop store as securefile ' ||
    'lob (retention none cache)');
    queue_name             => 'test_queue',
    queue_table            => 'test_qtab',
    max_retries            => 1,
    retry_delay            => 30,
    retention_time         => 0);
  dbms_aqadm.start_queue (queue_name => 'test_queue');

Note the storage clause. It uses securefile (after all we’re in 2016 now), retention none as a message is read exactly once most of the time and cache as most messages are read and deleted almost immediately when posted. Keeping the data in memory makes sense.

Block sizes

Oracle can use many different block sizes. The default block size is typically 8k. Write-heavy applications can often benefit from smaller block sizes, as that reduces contention. The smallest reasonable block size for AQ is 4k:

alter system set db_4k_cache_size=100M scope=both;
create tablespace users4k datafile '/oradata/orcl/users4k.dbf'
  size 100M autoextend on next 5M extent management local
  segment space management auto;
alter user aqtest quota unlimited on users4k;

Recreate the queue:

  dbms_aqadm.stop_queue (queue_name => 'test_queue');
  dbms_aqadm.drop_queue (queue_name => 'test_queue');
  dbms_aqadm.drop_queue_table (queue_table => 'test_qtab');

    queue_table        => 'test_qtab',
    queue_payload_type => 'sys.aq$_jms_message',
    storage_clause     => 'tablespace users4k ' ||
    'lob (user_data.bytes_lob) store as securefile ' ||
    '(retention none cache) ' ||
    'lob (user_data.text_lob) store as securefile  ' ||
    '(retention none cache) '  ||
    'opaque type user_prop store as securefile ' ||
    'lob (retention none cache)');
    queue_name             => 'test_queue',
    queue_table            => 'test_qtab',
    max_retries            => 1,
    retry_delay            => 30,
    retention_time         => 0);
  dbms_aqadm.start_queue (queue_name => 'test_queue');

AQ can store the message payload inline (i.e. in the same row as the metadata) or separately. However, it will only be stored inline if the size of the payload is less than about 4000 bytes. All message queue implementations work best with small messages, but here a small difference in size can theoretically have a significant impact. In practice I haven’t seen any major effects though, up to 3% in some benchmarks and none in others. As usual in engineering it all depends – where is the bottleneck?

If the application uses larger messages, the tablespace for the payload can use a block size optimized for the average message size, making it more likely that the whole message fits into a single block.

Block compression

It is possible to compress the payload and the user properties and by all means the metadata. That costs CPU, but on the other hand it reduces the storage requirements and perhaps it makes the difference between using one or two blocks for a message?

It is very easy to add. Simply tack on a compress clause when creating the queue table:

  queue_table        => 'test_qtab',
  queue_payload_type => 'sys.aq$_jms_message',
  storage_clause     => 'tablespace users4k '    ||
  'lob (user_data.bytes_lob) store as securefile '  ||
  '  (retention none cache compress low) '          ||
  'lob (user_data.text_lob) store as securefile '   ||
  '  (retention none cache compress low) '          ||
  'opaque type user_prop store as securefile lob '  ||
  '  (retention none cache compress low)');

Note that this almost certainly requires an extra license.

Driver versions

AQ JMS uses aqapi.jar and Oracle’s JDBC driver. While most versions are likely to work there can be a tremendous difference between the ancient versions and the latest ones. Make sure that the drivers are current and preferably matched (i.e. for the same target database)!

The JDBC driver can be downloaded from Oracle, but as far as I know aqapi.jar ships with the database and with Oracle’s application servers. Note that the database ships with two versions, one that can be used standalone and one that is intended for WebLogic or Oracle Application Server! Use the correct one.

Receive timeout

The way AQ JMS handles a receive timeout can be a real killer for applications that need to use many threads processing messages from the same queue. That equates to most Java EE applications.

When a client calls receive a single SELECT is issued in order to check if there are any messages on the queue. If there are no messages the client goes to sleep. If a message arrives the client wakes up, issues the same SELECT again and ideally consumes the message.

Unfortunately if there are 100 clients waiting for a message on a queue Oracle wakes them all when a message arrives. They will compete for it. One will succeed, the other 99 will fail. In this case the database needs to process 100 simultaneous SELECT statements and only one actually returns any data. The other 99 represent wasted resources. This can really kill the database, as it consumes large amounts of CPU.

A good test case for this is to spin up 100 consumer threads and one producer thread; then watch the load on the database and the top SQL. There should be an easy to find query similar to:

select  /*+ INDEX(TAB AQ$_TEST_QUEUE_TAB_I) */   tab.rowid, tab.msgid, tab.corrid, tab.priority, tab.delay,   tab.expiration ,tab.retry_count, tab.exception_qschema,   tab.exception_queue, tab.chain_no, tab.local_order_no, tab.enq_time,   tab.time_manager_info, tab.state, tab.enq_tid, tab.step_no,   tab.sender_name, tab.sender_address, tab.sender_protocol,   tab.dequeue_msgid, tab.user_prop, tab.user_data   from "AQTEST"."TEST_QUEUE_TAB" tab  where q_name = :1 and (state = :2  )  order by q_name, state, enq_time, step_no, chain_no, local_order_no for update skip locked

Check the statistics in Enterprise Manager or with SQL:

select rows_processed / executions rows_per_execution
from v$sqlarea where sql_text like
'select  /*+ INDEX(TAB AQ$_TEST_QUEUE_TAB_I) */   tab.rowid,%';

The number of rows returned per execution is very low, less than 0.01. The CPU load on the other hand is substantial.

The simple solution is to keep the number of threads down, but that is seldom possible. A more realistic alternative is to sleep on the client side. Use receiveNoWait instead of receive and if the method returns without a message, sleep for a short time in Java before the next attempt. At peak load all threads will get messages so no time is wasted sleeping and when there is less work available most threads will spend their time sleeping in the application server, not performing DOS attacks on the database. Ideally the number of listeners should ramp up and down based on traffic as well.

Dynamic destinations

In JMS a destination can be looked up with JNDI, or it can be created dynamically. For example:

Destination testQueue = session.createQueue("test_queue");

This is convenient, but there is a price to pay. Every time the method is called it issues a SELECT in order to find the destination. An application that creates a dynamic destination when it posts a message will do it for every single message. That adds up.

See https://www.javacodegeeks.com/2013/04/jms-and-spring-small-things-sometimes-matter.html for a more in-depth discussion.

What to do? Fortunately Destination is thread-safe, so it can be cached. With Spring the JndiDestinationResolver can be used with cache=true and fallbackToDynamicDestination=true. It will fail to make a JNDI lookup, do the dynamic lookup and then cache the result. Without Spring, use an application cache, for example a ConcurrentHashMap.

Ordered delivery

An application server that processes messages from the same queue in parallel using multiple threads really can’t guarantee that the messages are processed in order. Even if they are delivered in the same order as they were sent, they will be processed in parallel and will complete in non-deterministic order. However, by default Oracle ensures that messages on a queue are delivered in order. That can be a bit expensive, in particular if the application is using selectors.

Set the system property oracle.jms.orderWithSelector=false in order to cut corners here. This is unlikely to give a large boost, but unless ordered delivery is required it is a quick hit.

Final words

Tuning AQ is mostly about tuning the database. It is also about tuning the Java side and finding where the bottlenecks are. In other words it is fun! Oracle 12c comes with support for sharded queues – I haven’t had the opportunity to test them in a real-world scenario yet, but I look forward to that as they offer horizontal scaling for a single queue with RAC. I’ll be back.

Categories: Java, Oracle, Performance

VirtualBox intermittent network outages

For about a year I have had terrible problems with short but very frequent network outages between Linux guests running in VirtualBox and the world at large at a customer site. At home it works, but there the connection is lost and then restored every few minutes. Very frustrating. Today I finally found a solution. It appears that it is a known bug that goes way back, see ticket 13839. Sure enough, changing the virtual network card to PCnet-PCI II solved the issue! No more outages.

Categories: Networking

Oracle JDBC memory settings

Many applications use ancient versions of Oracle’s JDBC drivers, as they are downloaded manually and seldom upgraded. That is a pity as the newer drivers offer much better performance. However, some of the performance gains are bought with increased memory consumption and that can be a problem.

We ran into an issue with the connection pool in JBoss. It uses connections in round-robin, so as long as there is some load the pool tends to stay at peak size. Each connection normally keeps a buffer cache and it may cache other things as well. The size of the connections would grow over time, eventually consuming most of the heap.

A JBoss-specific workaround is to use the cli and close the idle connections (off-peak):


DS is the name of the data source. A better approach may be to set the system property oracle.jdbc.useThreadLocalBufferCache to true. That moves the buffer cache from the connections to thread locals. Depending on how the application behaves that may be better as the memory is reclaimed when a thread dies. Plus a given thread may be more likely to issue the same SQL multiple times and can thus benefit more from the cache.

It may also be useful to limit the maximum buffer size with oracle.jdbc.maxCachedBufferSize. The implicit statement cache is normally off, but if it is enabled the size can be controlled using oracle.jdbc.implicitStatementCacheSize and oracle.jdbc.freeMemoryOnEnterImplicitCache can force buffers allocated for a statement to be released when it is put into the cache. In most cases it is best to leave those options alone.

See Oracle JDBC Memory Management for the whole story!

Categories: Java, Oracle, Performance

Microsoft finally got something right, bash on Windows!

I’m still on Windows 7. It was a great OS. It is becoming less great with nagging and spying upgrades from Microsoft, but it works well and I hated Windows 8. Windows 10 seems a bit better, but it is still inferior to Windows 7 in my view. Or was until now! Finally Microsoft seems to have found a killing feature: native bash on Windows, supported by Canonical (Ubuntu). Read more in this blog. Perhaps it is time to upgrade?

Categories: Windows

JBoss EAP6 JGroups MPING fails with invalid argument

What to do if JBoss EAP6 fails to discover other cluster members with the following error from JGroups?

[org.jgroups.protocols.MPING] failed sending discovery request:
  java.io.IOException: Invalid argument
  at java.net.PlainDatagramSocketImpl.send(Native Method)
  at java.net.DatagramSocket.send(DatagramSocket.java:693)
  at org.jgroups.protocols.MPING.sendMcastDiscoveryRequest(MPING.java:300)

In my case the solution was simple. Add -Djava.net.preferIPv4Stack=true to jbossctl.sh. Apparently the multicast code doesn’t work with IPv6 on my Linux version.

Categories: Java, Networking

Beware of OpenJPA when upgrading Java

People often ask me if we should upgrade to the latest Java version and I always answer the same thing. Of course we should, what could be wrong about better performance and the latest security updates? I always amend something about trying it in a test environment first, though. Over the years I have seldom seen any problems with a Java SE update, the platform is remarkably backwards compatible. But then magic entered the stage.

The magic in this case is the build time byte code enhancement performed by OpenJPA. Apparently the byte code verifier was changed a bit in Java SE 7, so the classes are no longer valid. Oracle’s JVM complains. IBM’s Java 7 implementation crashes spectacularly. With Java 6 it just works.

Crash with IBM Java 7

OpenJPA has always been my least favourite JPA implementation and this did nothing to change that. Oh, they have fixed the byte code enhancer in newer versions (but if you are stuck with an old application server that might not help), but a similar problem exists for Java SE 8 and who wants to bet on everything playing out with Java 9 and 10? Not me.

It also says something about IBM’s JDK. It really shouldn’t crash on an invalid class, simply reject it and move on.

Nevertheless, my recommendation is still to use the latest Java version, but again – test it first and if you are using OpenJPA and/or anything but Oracle’s mainstream JVM, test it well!

Categories: Java