Use a Single Connection in Oracle- Developing Successful Oracle Applications-2

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If you use bind variables, then everyone who submits the same exact query that references the same object will use the compiled plan from the pool. You will compile your subroutine once and use it over and over again. This is very efficient and is the way the database intends you to work. Not only will you use fewer resources (a soft parse is much less resource-intensive), but also you will hold latches for less time and need them less frequently. This increases your performance and greatly increases your scalability.

Just to give you a tiny idea of how huge a difference this can make performance-wise, you only need to run a very small test. In this test, we’ll just be inserting some rows into a table; the simple table we will use is

SQL> drop table t purge;

SQL> create table t ( x int );

Table created.

Now we’ll create two very simple stored procedures. They both will insert the numbers 1 through 10,000 into this table; however, the first procedure uses a single SQL statement with a bind variable:

Procedure created.

The second procedure constructs a unique SQL statement for each row to be inserted:

SQL> create or replace procedure proc2 as

Now, the only difference between the two is that one uses a bind variable and the other does not. Both are using dynamic SQL and the logic is otherwise identical. The only difference is the use of a bind variable in the first.

Note For details on runstats and other utilities, see the “Setting Up Your Environment” section at the beginning of this book. You may not observe exactly the same values for CPU or any metric. Differences are caused by different Oracle versions, different operating systems, and different hardware platforms. The idea will be the same, but the exact numbers will undoubtedly be marginally different.

We are ready to evaluate the two approaches, and we’ll use runstats, a simple tool
I’ve developed, to compare the two in detail:

Now, the preceding result clearly shows that based on CPU time (measured in hundredths of seconds), it took significantly longer and significantly more resources to insert 10,000 rows without bind variables than it did with them. In fact, it took more than a magnitude more CPU time to insert the rows without bind variables. For every insert without bind variables, we spent the vast preponderance of the time to execute the statement simply parsing the statement! But it gets worse. When we look at other information, we can see a significant difference in the resources utilized by each approach:

The runstats utility produces a report that shows differences in latch utilization as well as differences in statistics. Here, I asked runstats to print out anything with a difference greater than 9500. You can see that we hard parsed 3 times in the first approach using bind variables and that we hard parsed 10,000 times without bind variables (once for each of the inserts). But that difference in hard parsing is just the tip of the iceberg. You can see here that we used an order of magnitude as many “latches” in the nonbind variable approach as we did with bind variables. That difference might beg the question “What is a latch?”

Let’s answer that question. A latch is a type of lock that is used to serialize access to shared data structures used by Oracle. The shared pool is an example; it’s a big, shared data structure found in the System Global Area (SGA), and this is where Oracle stores parsed, compiled SQL. When you modify anything in this shared structure, you must take care to allow only one process in at a time. (It is very bad if two processes or threads attempt to update the same in-memory data structure simultaneously— corruption would abound.) So, Oracle employs a latching mechanism, a lightweight locking method to serialize access. Don’t be fooled by the word lightweight. Latches are serialization devices, allowing access (to a memory structure) one process at a time. The latches used by the hard parsing implementation are some of the most used latches out there. These include the latches for the shared pool and for the library cache. Those are “big time” latches that people compete for frequently. What all this means is that as we increase the number of users attempting to hard parse statements simultaneously, our performance gets progressively worse over time. The more people parsing, the more people waiting in line to latch the shared pool, the longer the queues, the longer the wait.

Use a Single Connection in Oracle- Developing Successful Oracle Applications-1

Posted on by Julianne prior

Now, in SQL Server it is a very common practice to open a connection to the database for each concurrent statement you want to execute. If you are going to do five queries, you might well see five connections in SQL Server. In Oracle, on the other hand, if you want to do 5 queries or 500, the maximum number of connections you want to open is one. So, a practice that is common in SQL Server is something that is not only not encouraged in Oracle, it is actively discouraged; having multiple connections to the database (when you can use just one) is just something you don’t want to do.

But do it they did. A simple web-based application would open 5, 10, 15, or more connections per web page, meaning that their server could support only 1/5, 1/10, or 1/15 the number of concurrent users that it should have been able to. My recommendation to them was to re-architect the application to allow it to take advantage of the connection to generate a page, not somewhere between 5 and 15 connections.

This is the only solution that would actually solve the problem. As you can imagine, this is not an “OK, we’ll do that this afternoon” sort of solution. It is a nontrivial solution to a problem that could have most easily been corrected during the database port phase while you were in the code poking around and changing things in the first place. Furthermore, a simple test to scale before rolling out to production would have caught such issues prior to the end users feeling the pain.

Use Bind Variables

If I were to write a book about how to build nonscalable Oracle applications, “Don’t Use Bind Variables” would be the first and last chapter. Not using bind variables is a major cause of performance issues and a major inhibitor of scalability—not to mention a security risk of huge proportions. The way the Oracle shared pool (a very important shared memory data structure) operates is predicated on developers using bind variables in most cases. If you want to make a transactional Oracle implementation run slowly, even grind to a total halt, just refuse to use them.

A bind variable is a placeholder in a query. For example, to retrieve the record for employee 123, I can query

SQL> select * from emp where empno = 123;

Alternatively, I can query

SQL> select * from emp where empno = :empno;

In a typical system, you would query up employee 123 maybe once or twice and then never again for a long period of time. Later, you would query up employee 456, then 789, and so on. Or, foregoing SELECT statements, if you do not use bind variables in your insert statements, your primary key values will be hard-coded in them, and I know for a fact that these insert statements can’t ever be reused later!!! If you use literals (constants) in the query, then every query is a brand-new query, never before seen by the database. It will have to be parsed, qualified (names resolved), security-checked, optimized, and so on. In short, each and every unique statement you execute will have to be compiled every time it is executed.

The second query uses a bind variable, :empno, the value of which is supplied at query execution time. This query is compiled once, and then the query plan is stored in a shared pool (the library cache), from which it can be retrieved and reused. The difference between the two in terms of performance and scalability is huge, dramatic even.

From the preceding description, it should be fairly obvious that parsing unique statements with hard-coded variables (called a hard parse) will take longer and consume many more resources than reusing an already parsed query plan (called a soft parse).

What may not be so obvious is the extent to which the former will reduce the number of users your system can support. Obviously, this is due in part to the increased resource consumption, but an even more significant factor arises due to the latching mechanisms for the library cache. When you hard parse a query, the database will spend more time holding certain low-level serialization devices called latches (see Chapter 6 for more details).

These latches protect the data structures in Oracle’s shared memory from concurrent modifications by two sessions (otherwise, Oracle would end up with corrupt data structures) and from someone reading a data structure while it is being modified. The longer and more frequently you have to latch these data structures, the longer the queue to get these latches will become.

You will start to monopolize scarce resources. Your machine may appear to be underutilized at times, and yet everything in the database is running very slowly. The likelihood is that someone is holding one of these serialization mechanisms and a line is forming—you are not able to run at top speed.

It only takes one ill-behaved application in your database to dramatically affect the performance of every other application. A single, small application that does not use bind variables will cause the relevant SQL of other well-tuned applications to get discarded from the shared pool over time. You only need one bad apple to spoil the entire barrel.

How (and How Not) to Develop Database Applications- Developing Successful Oracle Applications

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That’s enough hypothesizing, for now at least. In the remainder of this chapter, I will take a more empirical approach, discussing why knowledge of the database and its workings will definitely go a long way toward a successful implementation (without having to write the application twice!).

Some problems are simple to fix as long as you understand how to find them. Others require drastic rewrites. One of the goals of this book is to help you avoid the problems in the first place.

Note In the following sections, I will discuss certain core Oracle features without delving into exactly what these features are and all of the ramifications of using them. I will refer you either to a subsequent chapter in this book or to the relevant Oracle documentation for more information.

Understanding Oracle Architecture

I have worked with many customers running large production applications—applications that had been “ported” from another database (e.g., SQL Server) to Oracle. I quote “ported” simply because most ports I see reflect a “what is the least change we can make to have our SQL Server code compile and execute on Oracle” perspective.

The applications that result from that line of thought are frankly the ones I see most often, because they are the ones that need the most help. I want to make clear, however, that I am not bashing SQL Server in this respect—the opposite is true! Taking an Oracle application and just plopping it down on top of SQL Server with as few changes as possible results in the same poorly performing code in reverse; the problem goes both ways.

In one particular case, however, the SQL Server architecture and how you use SQL Server really impacted the Oracle implementation. The stated goal was to scale up, but these folks did not want to really port to another database. They wanted to port with as little work as humanly possible, so they kept the architecture basically the same in the client and database layers. This decision had two important ramifications:

•\ The connection architecture was the same in Oracle as it had been inSQL Server.

•\ The developers used literal (nonbound) SQL.

These two ramifications resulted in a system that could not support the required user load (the database server simply ran out of available memory) and in a system that had abysmal performance.

The Black Box Approach- Developing Successful Oracle Applications-5

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Now, if we use two different transactions, we can see that both get different records. We also see that both get different records concurrently (using autonomous transactions once again to demonstrate the concurrency issues):
SQL> set serverout on

Now, in Oracle 11g and above, we can achieve the preceding logic using the SKIP LOCKED clause. In the following example, we’ll do two concurrent transactions again, observing that they each find and lock separate records concurrently:
SQL> declare

Both of the preceding “solutions” would help to solve the second serialization problem my client was having when processing messages. But how much easier would the solution have been if my client had just used Advanced Queuing and invoked DBMS_AQ.DEQUEUE? To fix the serialization issue for the message producer, we had to implement a function-based index. To fix the serialization issue for the consumer, we had to use that function-based index to retrieve the records and write code. So we fixed their major problem, caused by not fully understanding the tools they were using and found only after lots of looking and study since the system was not nicely instrumented.

What we hadn’t fixed yet were the following issues:
•\ The application was built without a single consideration for scaling at the database level.
•\ The application was performing functionality (the queue table) that the database already supplied in a highly concurrent and scalable fashion. I’m referring to the Advance Queuing (AQ) software that is burned into the database, functionality they were trying to reinvent.
•\ Experience shows that 80 to 90 percent (or more!) of all tuning should be done at the application level (typically the interface code reading and writing to the database), not at the database level.
•\ The developers had no idea what the beans did in the database or where to look for potential problems.


This was hardly the end of the problems on this project. We also had to figure out the following:
•\ How to tune SQL without changing the SQL. In general, that is very hard to do. We can accomplish this magic feat to some degree with SQL Profiles (this option requires a license for the Oracle Tuning Pack), with extended statistics, and with adaptive query optimization. But inefficient SQL will remain inefficient SQL.
•\ How to measure performance.
•\ How to see where the bottlenecks were.
•\ How and what to index. And so on.

At the end of the week, the developers, who had been insulated from the database, were amazed at what the database could actually provide for them and how easy it was to get that information. Most importantly, they saw how big of a difference taking advantage of database features could make to the performance of their application. In the end, they were successful—just behind schedule by a couple of weeks.

My point about the power of database features is not a criticism of tools or technologies like Hibernate, EJBs, and container-managed persistence. It is a criticism of purposely remaining ignorant of the database and how it works and how to use it. The technologies used in this case worked well—after the developers got some insight into the database itself.

The bottom line is that the database is typically the cornerstone of your application. If it does not work well, nothing else really matters. If you have a black box and it does not work, what are you going to do about it? About the only thing you can do is look at it and wonder why it is not working very well. You can’t fix it; you can’t tune it. Quite simply, you do not understand how it works—and you made the decision to be in this position. The alternative is the approach that I advocate: understand your database, know how it works, know what it can do for you, and use it to its fullest potential.

The Black Box Approach- Developing Successful Oracle Applications-4

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When we created the index, we had to choose between the following approaches:
•\ Just create an index on the processed-flag column.
•\ Create an index only on the processed-flag column when the processed flag is N, that is, only index the values of interest. We typically don’t want to use an index when the processed flag is Y since the vast majority of the records in the table have the value Y. Notice that I did not say “We never want to use….” You might want to very frequently count the number of processed records for some reason, and then an index on the processed records might well come in very handy.

In Chapter 11 on indexing, we’ll go into more detail on both types. In the end, we created a very small index on just the records where the processed flag was N. Access to those records was extremely fast, and the vast majority of Y records did not contribute to this index at all. We used a function-based index on a function decode( processed_ flag, ‘N’, ‘N’ ) to return either N or NULL—since an entirely NULL key is not placed into a conventional B*Tree index, we ended up only indexing the N records.

Note There is more information on NULLs and indexing in Chapter 11.

Was that the end of the story? No, not at all. My client still had a less than optimal solution on its hands. They still had to serialize on the “dequeue” of an unprocessed record. We could easily find the first unprocessed record—quickly—using select * from queue_table where decode( processed_flag, ‘N’, ‘N’) = ‘N’ FOR UPDATE, but only one session at a time could perform that operation. The project was using Oracle 10g and therefore could not yet make use of the relatively new SKIP LOCKED feature added in Oracle 11g. SKIP LOCKED would permit many sessions to concurrently find the first unlocked, unprocessed record, lock that record, and process it. Instead, we had to implement code to find the first unlocked record and lock it manually. Such code would generally look like the following in Oracle 10g and before. We begin by creating a table with the requisite index described earlier and populate it with some data, as follows:

SQL> drop table t purge;

Then we basically need to find any and all unprocessed records. One by one we ask the database “Is this row locked already? If not, then lock it and give it to me.” That code would look like this:

SQL> create or replace

Note In the preceding code, I ran some DDL—the CREATE OR REPLACE FUNCTION. Right before DDL runs, it automatically commits, so there was an implicit COMMIT in there. The rows we’ve inserted are committed in the database—and that fact is necessary for the following examples to work correctly. In general, I’ll use that fact in the remainder of the book. If you run these examples without performing the CREATE OR REPLACE, make sure to COMMIT first!

The Black Box Approach- Developing Successful Oracle Applications-3

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Here, I will use an autonomous transaction in the database to have two concurrent transactions in a single session. An autonomous transaction starts a “subtransaction” separate and distinct from any already established transaction in the session. The autonomous transaction behaves as if it were in an entirely different session—for all intents and purposes, the parent transaction is suspended. The autonomous transaction can be blocked by the parent transaction (as we’ll see), and, further, the autonomous transaction can’t see uncommitted modifications made by the parent transaction. For example, connecting to my pluggable database PDB1:

$ sqlplus eoda/foo@PDB1

Note I will use autonomous transactions throughout this book to demonstrate locking, blocking, and concurrency issues. It is my firm belief that autonomous transactions are a feature that Oracle should not have exposed to developers—for the simple reason that most developers do not know when and how to use them properly. The improper use of an autonomous transaction can and will lead to logical data integrity corruption issues. Beyond using them as a demonstration tool, autonomous transactions have exactly one other use—as an error-logging mechanism. If you wish to log an error in an exception block, you need to log that error into a table and commit it—without committing anything else. That would be a valid use of an autonomous transaction. If you find yourself using an autonomous transaction outside the scope of logging an error or demonstrating a concept, you are almost surely doing something very wrong.

Since I used an autonomous transaction and created a subtransaction, I received a deadlock—meaning my second insert was blocked by my first insert. Had I used two separate sessions, no deadlock would have occurred. Instead, the second insert would have been blocked and waited for the first transaction to commit or roll back. This symptom is exactly what the project in question was facing—the blocking, serialization issue.

So we had an issue whereby not understanding the database feature (bitmap indexes) and how it worked doomed the database to poor scalability from the start. To further compound the problem, there was no reason for the queuing code to ever have been written. The Oracle database has built-in queuing capabilities. This built-in queuing feature gives you the ability to have many producers (the sessions that insert the N, the unprocessed records) concurrently put messages into an inbound queue and have many consumers (the sessions that look for N records to process) concurrently receive these messages. That is, no special code should have been written in order to implement a queue in the database. The developers should have used the built-in feature. And they might have, except they were completely unaware of it.

Fortunately, once this issue was discovered, correcting the problem was easy. We did need an index on the processed-flag column, just not a bitmap index. We needed a conventional B*Tree index. It took a bit of convincing to get one created. No one wanted to believe that conventionally indexing a column with two distinct values was a good idea. But after setting up a simulation (I am very much into simulations, testing, and experimenting), we were able to prove it was not only the correct approach but also that it would work very nicely.

Note We create indexes, indexes of any type, typically to find a small number of rows in a large set of data. In this case, the number of rows we wanted to find via an index was one. We needed to find one unprocessed record. One is a very small number of rows; therefore, an index is appropriate. An index of any type would be appropriate. The B*Tree index was very useful in finding a single record out of a large set of records.

The Black Box Approach- Developing Successful Oracle Applications-2

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The very idea that developers building a database application should be shielded from the database is amazing to me, but that attitude persists. Many people still insist that developers can’t take the time to get trained in the database and, basically, that they shouldn’t have to know anything about the database. Why? Well, more than once I’ve heard “but Oracle is the most scalable database in the world, my people don’t have to learn about it, it’ll just work.” That’s true; Oracle is the most scalable database in the world. However, I can write bad code that does not scale in Oracle as easily—if not more easily—as I can write good, scalable code in Oracle. You can replace Oracle with any piece of software and the same is true. This is a fact: it is easier to write applications that perform poorly than it is to write applications that perform well. It is sometimes too easy to build a single-user system in the world’s most scalable database if you don’t know what you are doing. The database is a tool, and the improper use of any tool can lead to disaster. Would you take a nutcracker and smash walnuts with it as if it were a hammer? You could, but it wouldn’t be a proper use of that tool and the result would be a mess (and probably some seriously hurt fingers). Similar effects can be achieved by remaining ignorant of your database.

I was called into a project that was in trouble. The developers were experiencing massive performance issues—it seemed their system was serializing many transactions, that is to say—so instead of many people working concurrently, everyone was getting into a really long line and waiting for everyone in front of them to complete. The application architects walked me through the architecture of their system—the classic three-tier approach. They would have a web browser talk to a middle tier application server running Java Server Pages (JSPs). The JSPs would in turn utilize another layer— Enterprise JavaBeans (EJBs)—that did all of the SQL. The SQL in the EJBs was generated by a third-party tool and was done in a database-independent fashion.

Now, in this system it was very hard to diagnose anything, as none of the code was instrumented or traceable. Instrumenting code is the fine art of making every other line of developed code be debug code of some sort—so when you are faced with performance or capacity or even logic issues, you can track down exactly where the problem is. In this case, we could only locate the problem somewhere between the browser and the database—in other words, the entire system was suspect. The Oracle database is heavily instrumented, but the application needs to be able to turn the instrumentation on and off at appropriate points—something it was not designed to do.

So, we were faced with trying to diagnose a performance issue with not too many details, just what we could glean from the database itself. Fortunately, in this case it was fairly easy. When someone who knew the Oracle V$ tables (the V$ tables are one way Oracle exposes its instrumentation, its statistics, to us) reviewed them, it became apparent that the major contention was around a single table—a queue table of sorts.

The application would place records into this table, while another set of processes would pull the records out of this table and process them. Digging deeper, we found a bitmap index on a column in this table (See Chapter 11 on indexing for more information about bitmapped indexes). The reasoning was that this column, the processed-flag column, had only two values—Y and N. As records were inserted, they would have a value of N for not processed. As the other processes read and processed the record, they would update the N to Y to indicate that processing was done. The developers needed to find the N records rapidly and hence knew they wanted to index that column. They had read somewhere that bitmap indexes are for low-cardinality columns—columns that have but a few distinct values—so it seemed a natural fit. (Go ahead, use Google to search for when to use bitmap indexes; low cardinality will be there over and over. Fortunately, there are also many articles refuting that too simple concept today.)

But that bitmap index was the cause of all of their problems. In a bitmap index, a single key entry points to many rows, hundreds or more of them. If you update a bitmap index key (and thus locking it), the hundreds of records that key points to are effectively locked as well. So, someone inserting the new record with N would lock the N record in the bitmap index, effectively locking hundreds of other N records as well. Meanwhile, the process trying to read this table and process the records would be prevented from modifying some N record to be a Y (processed) record, because in order for it to update this column from N to Y, it would need to lock that same bitmap index key. In fact, other sessions just trying to insert a new record into this table would be blocked as well, as they would be attempting to lock the same bitmap key entry. In short, the developers had created a table that at most one person would be able to insert or update against at a time! We can see this easily using a simple scenario.

Note If you haven’t done so already, visit the “Setting Up Your Environment” section of the front matter of this book. This section contains the code to create the EODA and SCOTT users. These users are used extensively in the examples in this book. The “Setting Up Your Environment” section also contains the source code for many of the utilities used throughout this book. For your convenience, the setup source code can also be downloaded/cloned from the GitHub site.

The Black Box Approach- Developing Successful Oracle Applications-1

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I have an idea, borne out by first-hand personal experience (meaning I made the mistake myself), as to why database-backed software development efforts so frequently fail.

Let me be clear that I’m including here those projects that may not be documented as failures, but nevertheless take much longer to roll out and deploy than originally planned because of the need to perform a major rewrite, re-architecture, or tuning effort. Personally, I call such delayed projects failures: more often than not they could have been completed on schedule (or even faster).

The single most common reason for failure is a lack of practical knowledge of the database—a basic lack of understanding of the fundamental tool that is being used. The black box approach involves a conscious decision to protect the developers from the database. They are actually encouraged not to learn anything about it! In many cases, they are prevented from exploiting it. The reasons for this approach appear to be FUD related (Fear, Uncertainty, and Doubt). Developers have heard that databases are “hard,” that SQL, transactions, and data integrity are “hard.” The solution: don’t make anyone do anything hard. They treat the database as a black box and have some software tool generate all of the code. They try to insulate themselves with many layers of protection so that they don’t have to touch this “hard” database.

This is an approach to database development that I’ve never been able to understand, in part because, for me, learning Java and C was a lot harder than learning the concepts behind the database. I’m now pretty good at Java and C, but it took a lot more hands-on experience for me to become competent using them than it did to become competent using the database. With the database, you need to be aware of how it works, but you don’t have to know everything inside and out. When programming in C or Java/J2EE, you do need to know everything inside and out—and these are huge languages.

If you are building a database application, the most important piece of software is the database. A successful development team will appreciate this and will want its people to know about it, to concentrate on it. Many times I’ve walked into a project where almost the opposite was true. A typical scenario would be as follows:

•\ The developers were fully trained in the GUI tool or the language they were using to build the front end (such as Java). In many cases, they had had weeks if not months of training in it.

•\ The team had zero hours of Oracle training and zero hours of Oracle experience. Most had no database experience whatsoever. They would also have a mandate to be “database independent”—a mandate (edict from management or learned through theoretical academic instruction) they couldn’t hope to follow for many reasons. The most obvious one is they didn’t know enough about what databases are or what they do to even find the lowest common denominator among them.

•\ The developers encountered massive performance problems, data integrity problems, hanging issues, and the like (but very pretty screens).

As a result of the inevitable performance problems, I now get called in to help solve the difficulties (in the past, as a learning developer I was sometimes the cause of such issues).

Note Even today, I often find that the developers of database applications have spent no time reading the documentation. On my website, asktom.oracle. com, I frequently get questions along the lines of “what is the syntax for…” coupled with “we don’t have the documentation so please just tell us.” I refuse to directly answer many of those questions, but rather point them to the online documentation freely available to anyone, anywhere in the world. In the last 15 years, the excuses like “We don’t have documentation,” or “We don’t have access to resources,” have disappeared. Sites like www.oracle.com/technical-resources (Oracle Technical Resources, formerly known as the Oracle Technology Network) make it inexcusable to not have a full set of documentation at your fingertips! Today, everyone has access to all of the documentation; they just have to read it or—even easier—Google it.

REST and ORDS- Data Management

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Oracle REST Data Services provides a way to manage APIs for the data in the database without direct access to the database.
The credentials and privileges are all managed in the configuration of enabling the API.
This is not just a cloud service; this is available in on-premises databases and a fantastic tool for providing the needed data to applications.

To configure ORDS in the database, on the database system you install ORDS and enable it. Then you manage the views or tables of where you enable ORDS and the REST APIs. ORDS is available for download on the same site where you can get SQL Developer Web and other useful tools for REST services and database management APIs (oracle. com/ords).
You can also use yum to install it:
$ sudo yum install ords
$ ords –config /etc/ords/config install

REST endpoints start with http://localhost:8080/ords normally followed by the schema name and objects. Tables and views will need to be enabled:

SQL> begin ords.enable_object( p_enabled => TRUE, p_schema => ‘MMALCHER’, p_object => ‘ITEMS’, p_object_type => ‘TABLE’,p_object_alias => ‘items’); commit;end;

In the various tools, you can also use options to enable REST on a database object. Figure 18-6 shows how to use the menu to right-click an object to REST enable a table.

Figure 18–6. Enabling REST on table

Figure 18-7 provides the URL if you want to configure authentication for the API and the roles that are configured.

Figure 18–7. Configuring REST

To access the data, there are credentials that will be needed if configured, and then you can test the REST endpoint with the URL http://localhost:8080/ords/ mmalcher/items/.

It seems simple enough and is a powerful data management tool to use data for applications. We are just highlighting the ways to get started here with your databases, and of course there are more ways to configure the services on-premises and in the cloud.

Since we have been looking at Autonomous in this chapter as a tool for data management, let’s go back to Database Actions and look at the REST tool provided. Figure 18-8 shows the REST overview from Database Actions.

Figure18-8.REST overview

After enabling an object, you will be able to access the API with the REST endpoint URL. You can also pull up the menu on the table or view, which will allow you to edit, get a curl command, and open the data, as shown in Figure 18-9.

Figure 18–9. AutoREST, edit

One more option here to look at with REST tools is security. As shown in

Figure 18-10, you can manage the privileges of the REST APIs and OAuth clients and roles. This allows for the separation of administrators and managers of the REST services to grant the needed roles and access.

Figure18-10.SecurityforREST

ORDS is an easy way to provide a data service for access to data for applications, integrations, and data management needs. There are tools that will allow you to configure ORDS in Autonomous and SQL Developer for on-premises databases. Database Actions again is a tool set in the cloud to manage data services. The DBMS_CLOUD package is also available without the interface to perform these steps and configurations for data loading, privileges, and data services.

Tip Oracle offers a great way to try these tools and experiment. It is called Oracle LiveLabs, and there are workshops that you can do for free for each of these areas. Be sure to check it out at https://developer.oracle.com/livelabs.

Detecting and Resolving Locking Issues – Automation and Troubleshooting

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Sometimes, a developer or application user will report that a process that normally takes seconds to run is now taking several minutes and does not appear to be doing anything. In these situations, the problem is usually one of the following:

Space-related issue (e.g., the archive redo destination is full and has suspended all transactions).

A process has a lock on a table row and is not committing or rolling back, thus preventing another session from modifying the same row.

Oracle 23c has automated the process of aborting a low-priority transaction that holds a row lock and is blocking higher priority transactions. The priority is set to high, medium, or low for a user transaction. Users can also configure a maximum time a transaction will wait.

First check the alert log to see if there are any obvious issues that have occurred recently such as a wait on tablespace to extend. If there is nothing obvious in the alert log file, run a SQL query to look for locking issues.

SQL> set lines 80
SQL> col blkg_user form a10
SQL> col blkg_machine form a10
SQL> col blkg_sid form 99999999
SQL> col wait_user form a10

This situation is typical when applications do not explicitly issue a commit or rollback at appropriate times in the code. Oracle 23c does provide a way to prioritize the transactions to know how long a transaction will wait on the lock being released before aborting the lower-priority transaction.

You can also manually kill one of the sessions. Keep in mind that terminating a session may have unforeseen effects, and using the new features will allow for the transactions to roll back.

Resolving Open-Cursor Issues

The OPEN_CURSORS initialization parameter determines the maximum number of cursors a session can have open. This setting is per session. The default value of 50 is usually too low for any application. When an application exceeds the number of open cursors allowed, the following error is thrown:

ORA-01000: maximum open cursors exceeded

Usually, the prior error is encountered when

•     OPEN_CURSORS initialization parameter is set too low

•     Developers write code that does not close cursors properly

To investigate this issue, first determine the current setting of the parameter:

SQL> show parameter open_cursors;

If the value is less than 300, consider setting it higher. It is typical to set this value to 1,000 for busy OLTP systems. You can dynamically modify the value while your database is open, as shown here:

SQL> alter system set open_cursors=1000;

If you are using an spfile, consider making the change both in memory and in the spfile, at the same time:

SQL> alter system set open_cursors=1000 scope=both;

After setting OPEN_CURSORS to a higher value, if the application continues to exceed the maximum value, you probably have an issue with code that is not properly closing cursors.

If you work in an environment that has thousands of connections to the database, you may want to view only the top cursor-consuming sessions. The following query uses an inline view and the pseudocolumn ROWNUM to display the top 20 values:

SQL> select * from (select a.value, c.username, c.machine, c.sid, c.serial# from v$sesstat  a,v$statname b ,v$session     cwhere a.statistic# = b.statistic#and     c.sid and b.name and  a.value
= a.sid= ‘opened cursors current’ != 0and     c.username IS NOT NULL order by 1 desc,2) where rownum < 21;

If a single session has more than 1,000 open cursors, then the code is probably written such that the cursors are not closing. When the limit is reached, somebody should inspect the application code to determine whether a cursor is not being closed.

Tip It is recommended that you query V$SESSION instead of V$OPEN_CURSOR to determine the number of open cursors. V$SESSION provides a more accurate count of the cursors currently open.