NoSQL and Transaction Processing - Joe Celko's Complete Guide to NoSQL: What Every SQL Professional Needs to Know about NonRelational Databases (2014)

Joe Celko's Complete Guide to NoSQL: What Every SQL Professional Needs to Know about NonRelational Databases (2014)

Chapter 1. NoSQL and Transaction Processing


This chapter discusses traditional batch and transaction processing. A queue of jobs being read into a mainframe computer is how the bulk of commercial data processing is still done. Even transaction processing models finish with a batch job to load the databases with their new ETL tools. We need to understand both of these models and how they can be used with new technologies, and this chapter discusses these details.


NoSQL; transaction processing


This chapter discusses traditional batch and transaction processing. A queue of jobs being read into a mainframe computer is still how the bulk of commercial data processing is done. Even transaction processing models finish with a batch job to load the databases with their new ETL (extract, transform, load) tools. We need to understand both of these models and how they can be used with new technologies.

In the beginning, computer systems did monoprocessing, by which I mean they did one job from start to finish in a sequence. Later, more than one job could share the machine and we had multiprocessing. Each job was still independent of the others and waited in a queue for its turn at the hardware.

This evolved into a transaction model and became the client–server architecture we use in SQL databases. The goal of a transactional system is to assure particular kinds of data integrity are in place at the end of a transaction. NoSQL does not work that way.

1.1 Databases Transaction Processing in the Batch Processing World

Let’s start with a historical look at data and how it changed. Before there was Big Data there was “Big Iron”—that is, the mainframe computers, which used batch processing. Each program ran with its own unshared data and unshared processor; there was no conflict with other users or resources. A magnetic tape or deck of punch cards could be read by only one job at a time.

Scheduling batch jobs was an external activity. You submitted a job, it went into a queue, and a scheduler decided when it would be run. The system told an operator (yes, this is a separate job!) which tapes to hang on, what paper forms to load into a printer, and all the physical details. When a job was finished, the scheduler had to release the resources so following jobs could use them.

The scheduler had to assure that every job in the queue could finish. A job might not finish if other jobs were constantly assigned higher priority in the queue. This is called a live-lock problem. Think of the runt of a litter of pigs always being pushed away from its mother by the other piglets. One solution is to decrease the priority number of a job when it has been waiting for n seconds in the queue until it eventually gets to the first position.

For example, if two jobs, J1 and J2, both want to use resources A and B, we can get a dead-lock situation. Job J1 grabs resource A and waits for resource B; job J2 grabs resource B and waits for resource A. They sit and wait forever, unless one or both of the jobs releases its resource or we can find another copy of one of the resources.

This is still how most commercial data processing is done, but the tape drives have been swapped for disk drives.

1.2 Transaction Processing in the Disk Processing World

The world changed when disk drives were invented. At first, they were treated like fast tape drives and were mounted and dismounted and assigned to a single job. But the point of a database is that it is a common resource with multiple jobs (sessions) running at the same time.

There is no queue in this model. A user logs on in a session, which is connected to the entire database. Tables are not files, and the user does not connect to a particular table. The Data Control Language (DCL) inside the SQL engine decides what tables are accessible to which users.

If the batch systems were like a doorman at a fancy nightclub, deciding who gets inside, then a database system is like a waiter handling a room full of tables (sorry, had to do that pun) that are concurrently doing their own things.

In this world, the amount of data available to a user session is huge compared to a magnetic tape being read record by record. There can be many sessions running at the same time. Handling that traffic is a major conceptual and physical change.

1.3 ACID

The late Jim Gray really invented modern transaction processing in the 1970s and put it in the classic paper “The Transaction Concept: Virtues and Limitations” in June 1981. This is where the ACID (atomicity, consistency, isolation, and durability) acronym started. Gray’s paper discussed atomicity, consistency, and durability; isolation was added later. Bruce Lindsay and colleagues wrote the paper “Notes on Distributed Databases” in 1979 that built upon Gray’s work, and laid down the fundamentals for achieving consistency and the primary standards for database replication. In 1983, Andreas Reuter and Theo Härder published the paper “Principles of Transaction-Oriented Database Recovery” and coined the term ACID.

The terms in ACID mean:

Atomicity. Either the task (or all tasks) within a transaction are performed or none of them are. This is the all-or-none principle. If one element of a transaction fails, the entire transaction fails. SQL carries this principle internally. An INSERT statement inserts an entire set of rows into a table; a DELETE statement deletes an entire set of rows from a table; an UPDATE statement deletes and inserts entire sets.

Consistency. The transaction must meet all protocols or rules defined by the system at all times. The transaction does not violate those protocols and the database must remain in a consistent state at the beginning and end of a transaction. In SQL this means that all constraints are TRUE at the end of a transaction. This can be because the new state of the system is valid, or because the system was rolled back to its initial consistent state.

Isolation. No transaction has access to any other transaction that is in an intermediate or unfinished state. Thus, each transaction is independent unto itself. This is required for both performance and consistency of transactions within a database. This is not true in SQL; we have a concept of levels of isolation. A session can see uncommitted data at certain levels of isolation. This uncommitted data can be rolled back by its session, so in one sense, it never existed.

Durability. Once the transaction is complete, it will persist as complete and cannot be undone; it will survive system failure, power loss, and other types of system breakdowns. This is a hardware problem and we have done a good job of this. We just do not let data sit in volatile storage before we persist it.

This has been done with various locking schemes in most SQL databases. A lock says how other sessions can use a resource, such as reading only committed rows, or allowing them to read uncommitted rows, etc. This is called a pessimistic concurrency model. The underlying assumption is that you have to protect yourself from other people and that conflict is the normal situation.

The other popular concurrency model is called optimistic concurrency. If you have worked with microfilm, you know this model. Everyone gets a copy of the data to do with it as they wish in their session. In microfilm systems, the records manager would make copies of a document from the film and hand them out. Each employee would mark up his or her copy and turn it into central records.

The assumptions in this model are:

◆ Queries are much more common than database changes. Design for them.

◆ Conflicts are rare when there are database changes. Treat them as exceptions.

◆ When you do have conflicts, the sessions involved can be rolled back or you can set up rules for resolutions. Wait for things to get back to normal, and do not panic.

In case of microfilm systems, most of the requests were for information and the data was never changed. The requests that did make changes were usually separated in time so they did not conflict. When one or more employees made the same change, there was no conflict and the change was made. When two employees had a conflict, the records manager rejected both changes. Then he or she waited for another change that had no conflicts either by applying a rule or by a later change.

Optimistic concurrency depends on timestamping each row and keeping generational copies. The user can query the database at a point in time when he or she knows it is in an ACID state. In terms of the microfilm analogy, this is how central records look while waiting for the employees to return their marked-up copies. But this also means that we start with the database at time = t0, and can see it at time = t0, t1, t2, …, tn as we wish, based on the timestamps. Insertions, deletes, and updates do not interfere with queries as locking can. Optimistic concurrency is useful in situations where there is a constant inflow of data that has to be queried, such as stock and commodity trading.

The details of optimistic concurrency will be discussed in Section 5.1.1 on streaming databases. This method is best suited for databases that have to deal with constantly changing data, but have to maintain data integrity and present a consistent view of the data at a point in time.

Notice what has not changed: central control of data!

1.4 Pessimistic Concurrency in Detail

Pessimistic concurrency control assumes that conflict is the expected condition and we have to guard against it. The most popular models in a relational database management system (RDBMS) have been based on locking. A lock is a device that gives one user session access to a resource while keeping or restricting other sessions from that resource. Each session can get a lock on resources, make changes and then COMMIT or ROLLBACK the work in the database. A COMMIT statement will persist the changes, and a ROLLBACKstatement will restore the database to the state it was in before the session. The system can also do a ROLLBACK if there is a problem with the changes. At this point, the locks are released and other sessions can get to the tables or other resources.

There are variants of locking, but the basic SQL model has the following ways that one transaction can affect another:

P0 (dirty write). Transaction T1 modifies a data item. Another transaction, T2, then further modifies that data item before T1 performs a COMMIT or ROLLBACK. If T1 or T2 then performs a ROLLBACK, it is unclear what the correct data value should be. One reason why dirty writes are bad is that they can violate database consistency. Assume there is a constraint between x and y (e.g., x = y), and T1 and T2 each maintain the consistency of the constraint if run alone. However, the constraint can easily be violated if the two transactions write x and y in different orders, which can only happen if there are dirty writes.

P1 (dirty read). Transaction T1 modifies a row. Transaction T2 then reads that row before T1 performs a COMMIT. If T1 then performs a ROLLBACK, T2 will have read a row that was never committed and that may thus be considered to have never existed.

P2 (nonrepeatable read). Transaction T1 reads a row. Transaction T2 then modifies or deletes that row and performs a COMMIT. If T1 then attempts to reread the row, it may receive the modified value or discover that the row has been deleted.

P3 (phantom). Transaction T1 reads the set of rows N that satisfy some search condition. Transaction T2 then executes statements that generate one or more rows that satisfy the search condition used by transaction T1. If transaction T1 then repeats the initial read with the same search condition, it obtains a different collection of rows.

P4 (lost update). The lost update anomaly occurs when transaction T1 reads a data item, T2 updates the data item (possibly based on a previous read), and then T1 (based on its earlier read value) updates the data item and performs a COMMIT.

These phenomena are not always bad things. If the database is being used only for queries, without any changes being made during the workday, then none of these problems will occur. The database system will run much faster if you do not have to try to protect yourself from these problems. They are also acceptable when changes are being made under certain circumstances.

Imagine that I have a table of all the cars in the world. I want to execute a query to find the average age of drivers of red sport cars. This query will take some time to run, and during that time, cars will be crashed, bought, and sold; new cars will be built; and so forth. But I accept a situation with the three phenomena (P1–P3) because the average age will not change that much from the time I start the query to the time it finishes. Changes after the second decimal place really do not matter.

You can prevent any of these phenomena by setting the transaction isolation levels. This is how the system will use locks. The original ANSI model included only P1, P2, and P3. The other definitions first appeared in Microsoft Research Technical Report MSR-TR-95-51: “A Critique of ANSI SQL Isolation Levels” by Hal Berenson and colleagues (1995).

1.4.1 Isolation Levels

In standard SQL, the user gets to set the isolation level of the transactions in his or her session. The isolation level avoids some of the phenomena we just talked about and gives other information to the database. The following is the syntax for the SET TRANSACTIONstatement:

SET TRANSACTION < transaction mode list >

< transaction mode > ::= < isolation level> | < transaction access mode> | < diagnostics size >

< diagnostics size > ::= DIAGNOSTICS SIZE < number of conditions >

< transaction access mode > ::= READ ONLY | READ WRITE

< isolation level > ::= ISOLATION LEVEL < level of isolation >

< level of isolation > ::=





The optional < diagnostics size > clause tells the database to set up a list for error messages of a given size. This is a standard SQL feature, so you might not have it in your particular product. The reason is that a single statement can have several errors in it and the engine is supposed to find them all and report them in the diagnostics area via a GET DIAGNOSTICS statement in the host program.

The < transaction access mode > clause explains itself. The READ ONLY option means that this is a query and lets the SQL engine know that it can relax a bit. The READ WRITE option lets the SQL engine know that rows might be changed, and that it has to watch out for the three phenomena.

The important clause, which is implemented in most current SQL products, is < isolation level >. The isolation level of a transaction defines the degree to which the operations of one transaction are affected by concurrent transactions. The isolation level of a transaction is SERIALIZABLE by default, but the user can explicitly set it in the SET TRANSACTION statement.

The isolation levels each guarantee that each transaction will be executed completely or not at all, and that no updates will be lost. The SQL engine, when it detects the inability to guarantee the serializability of two or more concurrent transactions or when it detects unrecoverable errors, may initiate a ROLLBACK statement on its own.

Let’s take a look at Table 1.1, which shows the isolation levels and the three phenomena. A Yes means that the phenomena are possible under that isolation level.

Table 1.1

Isolation Levels and the Three Phenomena


In the table:

◆ The SERIALIZABLE isolation level is guaranteed to produce the same results as the concurrent transactions would have had if they had been done in some serial order. A serial execution is one in which each transaction executes to completion before the next transaction begins. The users act as if they are standing in a line waiting to get complete access to the database.

◆ A REPEATABLE READ isolation level is guaranteed to maintain the same image of the database to the user during his or her session.

◆ A READ COMMITTED isolation level will let transactions in this session see rows that other transactions commit while this session is running.

◆ A READ UNCOMMITTED isolation level will let transactions in this session see rows that other transactions create without necessarily committing while this session is running.

Regardless of the isolation level of the transaction, phenomena P1, P2, and P3 shall not occur during the implied reading of schema definitions performed on behalf of executing a statement, the checking of integrity constraints, and the execution of referential actions associated with referential constraints. We do not want the schema itself changing on users.

1.4.2 Proprietary Isolation Levels

We have discussed the ANSI/ISO model, but vendors often implement proprietary isolation levels. You will need to know how those work to use your product. ANSI/ISO sets its levels at the session level for the entire schema. Proprietary models might allow the programmer to assign locks at the table level with additional syntax. Microsoft has a list of hints that use the syntax:

SELECT.. FROM < base table > WITH (< hint list >)

The model can apply row or table level locks. If they are applied at the table, you can get ANSI/ISO conformance. For example, WITH (HOLDLOCK) is equivalent to SERIALIZABLE, but it applies only to the table or view for which it is specified and only for the duration of the transaction defined by the statement that it is used in.

The easiest way to explain the various schemes is with the concept of readers and writers. The names explain themselves.

In Oracle, writers block writers, The data will remain locked until you either COMMIT, ROLLBACK or stop the session without saving. When two users try to edit the same data at the same time, the data locks when the first user completes an operation. The lock continues to be held, even as this user is working on other data.

Readers do not block writers: Users reading the database do not prevent other users from modifying the same data at any isolation level.

DB2 and Informix are little different. Writers block writers, like Oracle. But in DB2 and Informix, writers prevent other users from reading the same data at any isolation level above UNCOMMITTED READ. At these higher isolation levels, locking data until edits are saved or rolled back can cause concurrency problems; while you're working on an edit session, nobody else can read the data you have locked. editing.

Readers block writers: In DB2 and Informix, readers can prevent other users from modifying the same data at any isolation level above UNCOMMITTED READ. Readers can only truly block writers in an application that opens a cursor in the DBMS, fetches one row at a time, and iterates through the result set as it processes the data. In this case, DB2 and Informix start acquiring and holding locks as the result set is processed.

In PostgreSQL, a row cannot be updated until the first transaction that made a change to the row is either committed to the database or rolled back. When two users try to edit the same data at the same time, the first user blocks other updates on that row. Other users cannot edit that row until this user either saves, thereby committing the changes to the database, or stops the edit session without saving, which rolls back all the edits performed in that edit session. If you use PostgreSQL’s multiversion concurrency control (MVCC), which is the default and recommended behavior for the database, user transactions that write to the database do not block readers from querying the database. This is true whether you use the default isolation level of READ COMMITTED in the database or set the isolation level to SERIALIZABLE. Readers do not block writers: No matter which isolation level you set in the database, readers do not lock data.

1.5 CAP Theorem

In 2000, Eric Brewer presented his keynote speech at the ACM Symposium on the Principles of Distributed Computing and introduced the CAP or Brewer’s theorem. It was later revised and altered through the work of Seth Gilbert and Nancy Lynch of MIT in 2002, plus many others since.

This theorem is for distributed computing systems while traditional concurrency models assume a central concurrency manager. The pessimistic model had a traffic cop and the optimistic model had a head waiter. CAP stands for consistency, availability, and partition tolerance:

Consistency is the same idea as we had in ACID. Does the system reliably follow the established rules for its data content? Do all nodes within a cluster see all the data they are supposed to? Do not think that this is so elementary that no database would violate it. There are security databases that actively lie to certain users! For example, when you and I log on to the Daily Plant database, we are told that Clark Kent is a mild-mannered reporter for a great metropolitan newspaper. But if you are Lois Lane, you are told that Clark Kent is Superman, a strange visitor from another planet.

Availability means that the service or system is available when requested. Does each request get a response outside of failure or success? Can you log on and attach your session to the database?

Partition tolerance or robustness means that a given system continues to operate even with data loss or system failure. A single node failure should not cause the entire system to collapse. I am looking at my three-legged cat—she is partition tolerant. If she was a horse, we would have to shoot her.

Distributed systems can only guarantee two of the features, not all three. If you need availability and partition tolerance, you might have to let consistency slip and forget about ACID. Essentially, the system says “I will get you to a node, but I do not know how good the data you find there will be” or “I can be available and the data I show will be good, but not complete.” This is like the old joke about software projects: you have it on time, in budget, or correct—pick two.

Why would we want to lose the previous advantages? We would love to have them, but “Big Iron” has been beaten out by Big Data and it is spread all over the world. There is no central computer; every enterprise has to deal with hundreds, thousands, or tens of thousands of data sources and users on networks today.

We have always had Big Data in the sense of a volume that is pushing the limitations of the hardware. The old joke in the glory days of the mainframe was that all you needed to do was buy more disks to solve any problem. Today, the data volume uses terms that did not exist in the old days. The SI prefixes peta (1015) and exa (1018) were approved in 1975 at the 15th Conférence Générale des Poids et Mesures (CGPM).

1.6 BASE

The world is now full of huge distributed computing systems, such as Google’s BigTable, Amazon’s Dynamo, and Facebook’s Cassandra. Here is where we get to BASE, a deliberately cute acronym that is short for:

Basically available. This means the system guarantees the availability of the data as per the CAP theorem. But the response can be “failure,” “unreliable” because the requested data is in an inconsistent or changing state. Have you ever used a magic eight ball?

Soft state. The state of the system could change over time, so even during times without input there may be changes going on due to “eventual consistency,” thus the system is always assumed to be soft as opposed to hard, where the data is certain. Part of the system can have hard data, such as a table of constants like geographical locations.

Eventual consistency. The system will eventually become consistent once it stops receiving input. This gives us a window of inconsistency that is acceptably short. The term acceptably short window is a bit vague. A data warehouse doing noncritical computations can wait, but an online order-taking system has to respond in time to keep the customers happy (less than one minute). At the other extreme, real-time control systems must respond instantly. The domain name system (DNS) is the most commonly known system that uses eventual consistency. Updates to a domain name are passed around with protocols and time-controlled caches; eventually, all clients will see the update. But it is far from instantaneous or centralized. This model requires a global timestamp so that each node knows which data item is the most recent version.

Like the ACID model, the eventual consistency model has variations:

Causal consistency. If process A has sent an update to process B, then a subsequent access by process B will return the updated value, and a write is guaranteed to supersede the earlier write. Access by process C that has no causal relationship to process A is subject to the normal eventual consistency rules. This was also called a buddy system in early network systems. If a node could not get to the definitive data source, it would ask a buddy if it had gotten the new data and trust its data.

Read-your-writes consistency. Process A, after it has updated a data item, always accesses the updated value and will never see an older value. This is a special case of the causal consistency model.

Session consistency. This is a practical version of the previous model, where a process accesses the storage system in a session. As long as the session exists, the system guarantees read-your-writes consistency. If the session terminates because of a failure, a new session will be created and processing will resume with a guarantee that it will not overlap the prior sessions.

Monotonic read consistency. A process returns only the most recent data values; it never returns any previous values.

Monotonic write consistency. In this case the system guarantees to serialize the writes by the same process. Systems that do not guarantee this level of consistency are notoriously hard to program. Think of it as a local queue at a node in the network.

A number of these properties can be combined. For example, one can get monotonic reads combined with session-level consistency. From a practical point of view monotonic reads and read-your-writes properties are most desirable in an eventual consistency system, but not always required. These two properties make it simpler for developers to build applications, while allowing the storage system to relax consistency and provide high availability.

Eventual consistency has been part of the backup systems in RDBMS products and in synchronous and asynchronous replication techniques. In synchronous mode the replica update is part of the transaction. In asynchronous mode the updates are delayed by log shipping. If the database crashes before the logs are shipped, the backup data can be out of date or inconsistent. Basically, the inconsistency window depends on the frequency of the log shipping.

1.7 Server-side Consistency

On the server side we will have the same data in several, not necessarily all, nodes. If all n nodes agree on a data value, then we are sure of it. Life is good.

But when we are in the process of establishing a consensus on an update, we need to know how many nodes have acknowledged the receipt of the update so far out of the nodes that are on the mailing list. We are looking for a quorum rule that accounts for node failures and incomplete replication. These rules will vary with the application. Large bank transfers will probably want complete agreement on all nodes. An abandoned website shopping-cart application can be satisfied if the customer returns to any node to continue shopping with some version of his or her cart, even one with some missing items. You just have to be sure that when the user hits the “checkout” key the other nodes know to delete their local copy of that cart.

What we do not want is sudden emergency restarts of nodes as a default action. This was how early file systems worked. Many decades ago, my wife worked for an insurance company that processed social security data. A single bad punch card would abort the entire batch and issue a useless error message.

We want a system designed for graceful degradation. The Sabre airline reservation system expects a small number of duplicate reservations. It does not matter if somebody has two conflicting or redundant reservations in the system. Since the passenger cannot be in two places at once or in one place twice, the problem will be resolved by a human being or the physical logic of the problem.

When one node is overloaded, you will tolerate the reduced performance and move some of the load to other nodes until the first system can be repaired. The best example of that is redundant array of independent disks (RAID) systems. When a disk fails, it is physically removed from the array and a new unit is plugged in to replace it. During the reconstruction of the failed disk, performance for accesses will take a hit. The data has to be copied from the alternative array blocks while the system keeps running its usual tasks.

1.8 Error Handling

There are two broad classes of error messages. We can have an anticipated problem, like an invalid password, which can have a standard response or process. We all have gotten an invalid password, and then been locked out if we fail to get it right in some number of tries.

The second class of error message tells us what happened, perhaps in painful detail. This invites some action on the part of the user or lets the user know why he or she is stuck.

But with NoSQL and the eventual consistency model, things might not be comfortable. Things stop or lock and you have no idea why, what to do, or how long it will take to resolve (if ever). As of 2012, Twitter has been trying to move from MySQL to Cassandra for more than a year. There are people (i.e., tweeters) who want instant feedback on the people they follow and any delay becomes an eternity. In August 2011, Foursquare reported an 11-hour downtime because of a failure of MongoDB.

1.9 Why SQL Does Not Work Here

To summarize why you might want to break away from SQL and the traditional RDBMS model:

◆ You do not have the data in one machine or even one network.

◆ Lots of it is not your data at all.

◆ It is so big you cannot put it in one place.

◆ It is uncoordinated in time as well as space.

◆ It is not always nice, structured data that SQL was meant to handle. We will spend the next few chapters on the new flavors of data and the special tools they need.

Concluding Thoughts

You need to know the basics of the old technology to understand the new technology.


1. Gray J. The Transaction Concept: Virtues and Limitations. 1981;; 1981; Cupertino CA.

2. Berenson H, et al. Microsoft Research Technical Report MSR-TR-95-51: “A critique of ANSI SQL isolation levels”. 1995; Redmond, WA.