In this chapter of Principles of Transaction Processing, you'll learn about updating data during a transaction via a two-phase commit (2PC) and see examples of how the two-phase commit protocol can help ensure transaction atomicity. Also, you'll learn about transaction processing performance, including benchmarks from the Transaction Processing Performance Council, and get insight into the five types of transaction processing.
Table of contents
Making business transaction processing and applications work
Transaction processing system examples and SOA approaches
Key benchmarks for measuring transaction processing performance
Understanding and comparing six types of processing systems
1.4 Two-Phase Commit
When a transaction updates data on two or more database systems, we still have to ensure the atomicity property, namely, that either both database systems durably install the updates or neither does. This is challenging, because the database systems can independently fail and recover. This is certainly a problem when the database systems reside on different nodes of a distributed system. But it can even be a problem on a single machine if the database systems run as server processes with private storage since the processes can fail independently. The solution is a protocol called two-phase commit (2PC), which is executed by a module called the transaction manager.
The crux of the problem is that a transaction can commit its updates on one database system, but a second database system can fail before the transaction commits there too. In this case, when the failed system recovers, it must be able to commit the transaction. To commit the transaction, the recovering system must have a copy of the transaction's updates that executed there. Since a system can lose the contents of main memory when it fails, it must store a durable copy of the transaction's updates before it fails, so it will have them after it recovers. This line of reasoning leads to the essence of two-phase commit: Each database system accessed by a transaction must durably store its portion of the transaction's updates before the transaction commits anywhere. That way, if a system S fails after the transaction commits at another system S _ but before the transaction commits at S , then the transaction can commit at S after S recovers (see Figure 1.7 ).
FIGURE 1.7 How Two-Phase Commit Ensures Atomicity. With two-phase commit, each system durably stores its updates before the transaction commits, so it can commit the transaction when it recovers.
FIGURE 1.8 X/Open Transaction Model (XA). The transaction manager processes Start, Commit, and Abort. It talks to resource managers to run two-phase commit.
To understand two-phase commit, it helps to visualize the overall architecture in which the transaction manager operates. The standard model, shown in Figure 1.8 , was introduced by IBM's CICS and popularized by Oracle's Tuxedo and X/Open (now part of The Open Group, see Chapter 10). In this model, the transaction manager talks to applications, resource managers, and other transaction managers. The concept of "resource" includes databases, queues, files, messages, and other shared objects that can be accessed within a transaction. Each resource manager offers operations that must execute only if the transaction that called the operations commits.
The transaction manager processes the basic transaction operations for applications: Start, Commit, and Abort. An application calls Start to begin executing a new transaction. It calls Commit to ask the transaction manager to commit the transaction. It calls Abort to tell the transaction manager to abort the transaction.
The transaction manager is primarily a bookkeeper that keeps track of transactions in order to ensure atomicity when more than one resource is involved. Typically, there's one transaction manager on each node of a distributed computer system. When an application issues a Start operation, the transaction manager dispenses a unique ID for the transaction called a transaction identifier. During the execution of the transaction, it keeps track of all the resource managers that the transaction accesses. This requires some cooperation with the application, resource managers, and communication system. Whenever the transaction accesses a new resource manager, somebody has to tell the transaction manager. This is important because when it comes time to commit the transaction, the transaction manager has to know all the resource managers to talk to in order to execute the two-phase commit protocol.
When a transaction program finishes execution and issues the commit operation, that commit operation goes to the transaction manager, which processes the operation by executing the two-phase commit protocol. Similarly, if the transaction manager receives an abort operation, it tells the resource managers to undo all the transaction's updates; that is, to abort the transaction at each resource manager. Thus, each resource manager must understand the concept of transaction, in the sense that it undoes or permanently installs the transaction's updates depending on whether the transaction aborts or commits.
When running two-phase commit, the transaction manager sends out two rounds of messages — one for each phase of the commitment activity. In the first round of messages it tells all the resource managers to prepare to commit by writing a copy of the results of the transaction to stable storage, but not actually to commit the transaction. At this point, the resource managers are said to be prepared to commit. When the transaction manager gets acknowledgments back from all the resource managers, it knows that the whole transaction has been prepared. That is, it knows that all resource managers stored a durable copy of the transaction's updates but none of them have committed the transaction. So it sends a second round of messages to tell the resource managers to actually commit. Figure 1.9 gives an example execution of two-phase commit with two resource managers involved.
FIGURE 1.9 The Two-Phase Commit Protocol. In Phase One, every resource manager durably saves the transaction's updates before replying " I am Prepared. " Thus, all resource managers have durably stored the transaction's updates before any of them commits in phase two.
Two -phase commit avoids the problem in Figure 1.7(a) because all resource managers have a durable copy of the transaction's updates before any of them commit. Therefore, even if a system fails during the commitment activity, as the London system did in the figure, it can commit the transaction after it recovers. However, to make this all work, the protocol must handle every possible failure and recovery scenario. For example, in Figure 1.7(b) , it must tell the London system to commit the transaction. The details of how two-phase commit handles all these scenarios is described in Chapter 8.
Two -phase commit is required whenever a transaction accesses two or more resource managers. Thus, one key question that designers of TP applications must answer is whether or not to distribute their transaction programs among multiple resources. Using two-phase commit adds overhead (due to two-phase commit messages), but the option to distribute can provide better scalability (adding more systems to increase capacity) and availability (since one system can fail while others remain operational).
1.5 Transaction Processing Performance
Performance is a critical aspect of TP systems. No one likes waiting more than a few seconds for an automated teller machine to dispense cash or for a hotel web site to accept a reservation request. So response time to end-users is one important measure of TP system performance. Companies that rely on TP systems, such as banks, airlines, and commercial web sites, also want to get the most transaction throughput for the money they invest in a TP system. They also care about system scalability; that is, how much they can grow their system as their business grows.
It's very challenging to configure a TP system to meet response time and throughput requirements at minimum cost. It requires choosing the number of systems, how much storage capacity they'll have, which processing and database functions are assigned to each system, and how the systems are connected to displays and to each other. Even if you know the performance of the component products being assembled, it's hard to predict how the overall system will perform. Therefore, users and vendors implement benchmarks to obtain guidance on how to configure systems and to compare competing products.
Vendor benchmarks are defined by an independent consortium called the Transaction Processing Performance Council (TPC; www.tpc.org). The benchmarks enable apples-to-apples comparisons of different vendors' hardware and software products. Each TPC benchmark defines standard transaction programs and characterizes a system's performance by the throughput that the system can process under certain workload conditions, database size, response time guarantees, and so on. Published results must be accompanied by a full disclosure report, which allows other vendors to review benchmark compliance and gives users more detailed performance information beyond the summary performance measures.
The benchmarks use two main measures of a system's performance, throughput, and cost-per-throughput-unit. Throughput is the maximum throughput it can attain, measured in transactions per second (tps) or transactions per minute (tpm). Each benchmark defines a response time requirement for each transaction type (typically 1 – 5 seconds). The throughput can be measured only when 90% of the transactions meet their response time requirements and when the average of all transaction response times is less than their response time requirement. The latter ensures that all transactions execute within an acceptable period of time.
As an aside, Internet web sites usually measure 90% and 99% response times. Even if the average performance is fast, it's bad if one in a hundred transactions is too slow. Since customers often run multiple transactions, that translates into several percent of customers receiving poor service. Many such customers don't return.
The benchmarks' cost-per-throughput-unit is measured in dollars per tps or tpm. The cost is calculated as the list purchase price of the hardware and software, plus three years' vendor-supplied maintenance on that hardware and software (called the cost of ownership).
The definitions of TPC benchmarks are worth understanding to enable one to interpret TPC performance reports. Each of these reports, published on the TPC web site, is the result of a system benchmark evaluation performed by a system vendor and subsequently validated by an independent auditor. Although their main purpose is to allow customers to compare TP system products, these reports are also worth browsing for educational reasons, to give one a feel for the performance range of state-of-the-art systems. They are also useful as guidance for the design and presentation of a custom benchmark study for a particular user application.
The TPC-A and TPC-B Benchmarks
The first two benchmarks promoted by TPC, called TPC-A and TPC-B, model an ATM application that debits or credits a checking account. When TPC-A/B were introduced, around 1989, they were carefully crafted to exercise the main bottlenecks customers were experiencing in TP systems. The benchmark was so successful in encouraging vendors to eliminate these bottlenecks that within a few years nearly all database systems performed very well on TPC-A/B. Therefore, the benchmarks were retired and replaced by TPC-C in 1995. Still, it's instructive to look at the bottlenecks the benchmarks were designed to exercise, since these bottlenecks can still arise today on a poorly designed system or application.
Both benchmarks run the same transaction program. The only difference is that TPC-A includes terminals and a network in the overall system, while TPC-B does not. In both cases, the transaction program performs the sequence of operations shown in Figure 1.10 (except that TPC-B does not perform the read/write terminal operations).
In TPC-A/B, the database consists of:
- Account records, one record for each customer's account (total of 100,000 accounts)
- A teller record for each teller, which stores the amount of money in the teller's cash drawer (total of 10 tellers)
- One record for each bank branch (one branch minimum), which contains the sum of all the accounts at that branch
- A history file, which records a description of each transaction that actually executes
FIGURE 1.10 TPC-A/B Transaction Program. The program models a debit/credit transaction for a bank.
Start Read message from terminal (100 bytes)
Read and write account record (random access)
Write history record (sequential access)
Read and write teller record (random access)
Read and write branch record (random access)
Write message to terminal (200 bytes)
The transaction reads a 100-byte input message, including the account number and amount of money to withdraw or deposit. The transaction uses that input to find the account record and update it appropriately. It updates the history file to indicate that this transaction has executed. It updates the teller and bank branch records to indicate the amount of money deposited or withdrawn at that teller and bank branch, respectively. Finally, for TPC-A, it sends a message back to the display device to confirm the completion of the transaction.
The benchmark exercises several potential bottlenecks on a TP system:
- There's a large number of account records. The system must have 100,000 account records for each transaction per second it can perform. To randomly access so many records, the database must be indexed.
- The end of the history file can be a bottleneck, because every transaction has to write to it and therefore to lock and synchronize against it. This synchronization can delay transactions.
- Similarly, the branch record can be a bottleneck, because all of the tellers at each branch are reading and writing it. However, TPC-A/B minimizes this effect by requiring a teller to execute a transaction only every 10 seconds.
Given a fixed configuration, the performance and price/performance of any TP application depends on the amount of computer resources needed to execute it: the number of processor instructions, I/Os to stable storage, and communications messages. Thus, an important step in understanding the performance of any TP application is to count the resources required for each transaction. In TPC-A/B, for each transaction a high performance implementation uses a few hundred thousand instructions, two or three I/Os to stable storage, and two interactions with the display. When running these benchmarks, a typical system spends more than half of the processor instructions inside the database system and maybe another third of the instructions in message communications between the parts of the application. Only a small fraction of the processor directly executes the transaction program. This isn't very surprising, because the transaction program mostly just sends messages and initiates database operations. The transaction program itself does very little, which is typical of many TP applications.
The TPC-C Benchmark
The TPC-C benchmark was introduced in 1992. It is based on an order-entry application for a wholesale supplier. Compared to TPC-A/B, it includes a wider variety of transactions, some "heavy weight" transactions (which do a lot of work), and a more complex database.
The database centers around a warehouse, which tracks the stock of items that it supplies to customers within a sales district, and tracks those customers' orders, which consist of order-lines . The database size is proportional to the number of warehouses (see Table 1.1 ).
Table 1.1 Database for the TPC-C Benchmark. The database consists of the tables in the left column, which support an order-entry application
|Table Name||Number of Rows per Warehouse||Bytes-per-Row||Size of Table (in bytes) per Warehouse|
There are five types of transactions:
- New-Order: To enter a new order, first retrieve the records describing the given warehouse, customer, and district, and then update the district (increment the next available order number). Insert a record in the Order and New-Order tables. For each of the 5 to 15 (average 10) items ordered, retrieve the item record (abort if it doesn't exist), retrieve and update the stock record, and insert an order-line record.
- Payment: To enter a payment, first retrieve and update the records describing the given warehouse, district, and customer, and then insert a history record. If the customer is identified by name, rather than id number, then additional customer records (average of two) must be retrieved to find the right customer.
- Order-Status: To determine the status of a given customer's latest order, retrieve the given customer record (or records, if identified by name, as in Payment), and retrieve the customer's latest order and corresponding order-lines.
- Delivery: To process a new order for each of a warehouse's 10 districts, get the oldest new-order record in each district, delete it, retrieve and update the corresponding customer record, order record, and the order's corresponding order-line records. This can be done as one transaction or 10 transactions.
- Stock-Level: To determine, in a warehouse's district, the number of recently sold items whose stock level is below a given threshold, retrieve the record describing the given district (which has the next order number). Retrieve order lines for the previous 20 orders in that district, and for each item ordered, determine if the given threshold exceeds the amount in stock.
The transaction rate metric is the number of New-Order transactions per minute, denoted tpmC, given that all the other constraints are met. The New-Order, Payment, and Order-Status transactions have a response time requirement of five seconds. The Stock-Level transaction has a response time of 20 seconds and has relaxed consistency requirements. The Delivery transaction runs as a periodic batch. The workload requires executing an equal number of New-Order and Payment transactions, and one Order-Status, Delivery, and Stock-Level transaction for every 10 New-Orders.
Table 1.2 TPC-E Transaction Types
|Transaction Type||Percent of Transactions||Database Tables Accessed||Description|
|Trade Order||10.1%||17||Buy or sell a security|
|Trade Result||10%||15||Complete the execution of a buy or sell order|
|Trade Status||19%||6||Get the status of an order|
|Trade Update||2%||6||Make corrections to a set of trades|
|Customer Position||13%||7||Get the value of a customer's assets|
|Market Feed||1%||2||Process an update of current market activity (e.g., ticker tape)|
|Market Watch||18%||4||Track market trends (e.g., for a customer's "watch list ")|
|Security Detail||14%||12||Get a detailed data about a security|
|Trade Lookup||8%||6||Get information about a set of trades|
|Broker Volume||4.9%||6||Get a summary of the volume and value of pending orders of a set of brokers|
The TPC-C workload is many times heavier per transaction than TPC-A/B and exhibits higher contention for shared data. Moreover, it exercises a wider variety of performance-sensitive functions, such as deferred transaction execution, access via secondary keys, and transaction aborts. It is regarded as a more realistic workload than TPC-A/B, which is why it replaced TPC-A/B as the standard TP systems benchmark.
The TPC-E Benchmark
The TPC-E benchmark was introduced in 2007. Compared to TPC-C, it represents larger and more complex databases and transaction workloads that are more representative of current TP applications. And it uses a storage configuration that is less expensive to test and run. It is based on a stock trading application for a brokerage firm where transactions are related to stock trades, customer inquiries, activity feeds from markets, and market analysis by brokers. Unlike previous benchmarks, TPC-E does not include transactional middleware components and solely measures database performance.
TPC-E includes 10 transaction types, summarized in Table 1.2 , which are a mix of read-only and read-write transactions. For each type, the table shows the percentage of transactions of that type and the number of database tables it accesses, which give a feeling for the execution cost of the type.
There are various parameters that introduce variation into the workload. For example, trade requests are split 50-50 between buy and sell and 60-40 between market order and limit order. In addition, customers are assigned to one of three tiers, depending on how often they trade securities — the higher the tier, the more accounts per customer and trades per customer.
The database schema has 33 tables divided into four sets: market data (11 tables), customer data (9 tables), broker data (9 tables), and static reference data (4 tables). Most tables have fewer than six columns and less than 100 bytes per row. At the extremes, the Customer table has 23 columns, and several tables store text information with hundreds of bytes per row (or even more for the News Item table).
A driver program generates the transactions and their inputs, submits them to a test system, and measures the rate of completed transactions. The result is the measured transactions per second (tpsE), which is the number of Trade Result transactions executed per second, given the mix of the other transaction types. Each transaction type has a response time limit of one to three seconds, depending on transaction type. In contrast to TPC-C, application functions related to front-end programs are excluded. Thus, the results measure the serverside database management system. Like previous TPC benchmarks, TPC-E includes a measure for the cost per transaction per second ($/tpsE).
TPC-E provides data generation code to initialize the database with the result of 300 days of initial trading, daily market closing price information for five years, and quarterly company report data for five years. Beyond that, the database size scales up as a function of the nominal tpsE, which is the transaction rate the benchmark sponsor is aiming for . The measured tpsE must be within 80 to 102% of the nominal tpsE. The database must have 500 customers for each nominal tpsE. Other database tables scale relative to the number of customer rows. For example, for each 1000 Customers, there must be 685 Securities and 500 Companies. Some tables include a row describing each trade and therefore grow quite large for a given run.
Compared to TPC-C, TPC-E is a more complex workload. It makes heavier use of SQL database features, such as referential integrity and transaction isolation levels (to be discussed in Chapter 6). It uses a more complex SQL schema. Transactions execute more complex SQL statements and several of them have to make multiple calls to the database, which cannot be batched in one round-trip. And there is no trivial partitioning of the database that will enable scalability (to be discussed in Section 2.6). Despite all this newly introduced complexity, the benchmark generates a much lower I/O load than TPC-C for a comparable transaction rate. This makes the benchmark cheaper to run, which is important to vendors when they run high-end scalability tests where large machine configurations are needed.
In addition to its TP benchmarks, the TPC publishes a widely used benchmark for decision support systems, TPC-H. It also periodically considers new TP benchmark proposals. Consult the TPC web site, www.tpc.org, for current details. More on transaction management and operations:
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