Quelea is our eventually consistent data store with an associated programming framework intended to simplify programming under eventual consistency. In this post, I describe how various applications written in Quelea employ a combination of highly available and serializable transactions to enforce application integrity. Three applications participate in this survey:

  • BankAccount: A highly available concurrent bank account application.
  • Microblog: A twitter-like microblogging site, modeled after Twissandra. The application allows adding new users, adding and replying to tweets, following, unfollowing and blocking users, and fetching a user’s timeline, userline, followers and following.
  • Rubis: An eBay-like auction site. The application allows users to browse items, bid for items on sale, and pay for items from a wallet modeled after a bank account.

First let me define what I mean by a transaction:

What is a transaction?

For all practical purposes, a transaction can be defined as a set of operations on some data, which, when executed, appear to have been executed simultaneously. We say that the set of operations has taken effect atomically. Atomicity implies that the set of effects generated by a transaction T is visible to operations in other transaction T' in its entirety, or it is not visible at all. From the perspective of the other transaction T', an operation op' in T' either sees all the effects of a transaction T, or it sees none of them. Note that this is the requirement of Read Committed (RC) isolation level. Hence, atomicity being a defining property of a transaction means that every transaction automatically experiences RC isolation level. Note that a set of effects that offers atomicity, but with stronger isolation properties than RC is also a transaction.

Transactions are used for in various applications towards different ends. Below, I describe some of the common purposes served by transactions in some sample applications:

To perform atomic updates

Transactions are primarily used in practice when we want to perform a series of actions on the data taking it through some intermediary states, where the integrity of data may be violated. In order to not expose these intermediary states, we want to wrap these actions inside a transaction so that, for observers, it appears as if all actions have been committed atomically. Typically, these actions perform updates to multiple tables or materialized views, or multiple rows in the same table. Some usecases:

  1. BankAccount:
  2. When a user saves money, then withdraw operation on the checking account, and deposit operation on the savings account should appear to have happened atomically. Intermediary state (after withdraw, but before deposit) may violate app-specific integrity constraints (e.g: user shown incorrect total bal, or user incorrectly penalized for insufficient bal etc).
  3. Microblog (Twitter):
  4. When a user (B) unfollows another user (A), then B has to be removed from A’s follower list, and A has to be removed from B’s following list. Both updates should appear to have happened atomically. Intermediate state violates app-specific integrity constraint that follows is a symmetric relation.
  5. When a user tweets, the tweet should be added to the table of all tweets, and its Id should be added to the userline materialized view against tweeter’s userId, and timeline materialized view against the userIds of all the followers. All insertions should appear to have happened simultaneously. Intermediary states may violate (a) app-specific integrity constraints (e.g: user B sees a tweet by A in his timeline, but doesn’t find the tweet in A’s userline), and (b) referential integrity (happens if data store can reorder operations, like in the case of EC stores).
  6. When user (A) blocks user (B), then B should be forced to unfollow A (the unfollow operation needs to be performed in the same way as above). Furthermore, to prevent B from re-following A, B needs to be added to A’s Blocks list, and A needs to be added to B’s IsBlockedBy list, both in the user table itself. All changes must commit atomically.
  7. Rubis (Auction site):
  8. When a user (A) bids for an item (I), then following updates need to happen atomically:
    • The bid information needs to be added to the Bids table against a new bidId.
    • bidId needs to be added against I’s itemId in ItemBids materialized view.
    • bidId needs to be added against A’s userId in UserBids materialized view.
    • I’s maxBid needs to be updated against I’s itemId in the Item table.
      The intermediate states may violate (a) app-specific integrity constraints (e.g: bid appears as top bid on the item page, but doesn’t appear in the list of bids by the bidder), and (b) referential integrity (happens if data store can reorder operations, like in the case of EC stores).
  9. When a user cancels his bid, all the above insertions need to be deleted atomically. Intermediate states may violate referential integrity (under reordering of operations).
  10. When a user (A) offers an item (I) for auction, then I needs to be added to the Items table, and its itemId against A’s userId in the UserItems table/materialized view, simultaneously.
  11. When the auction concludes, the above insertions need to be deleted atomically. Intermediate states may violate referential integrity (under reordering of operations).

To ensure consistent reads

Atomicity only guarantees that a transaction’s effects, if visible, are visible in their entirety. It does not prevent effects from becoming visible to only a subset of operations in a different transaction. Therefore, in many cases, atomicity of a write transaction itself is not sufficient to ensure that all reads witness consistent version of data; we need certain isolation gurantees on reads. Applications use transactions to achieve isolation. Usecases:

  1. BankAccount:
  2. When a user (A) saves money (an atomic transfer from checking to savings account), and immediately checks the total balance in her accounts by issuing two getBalance operations, one on each of her accounts, she might see an inconsistent balance. This can happen if first getBalance witnesses the effects of save transaction, but second does not, or vice versa. To prevent this from happening, both getBalance operations should be wrapped inside a transactions, which needs to be executed in isolation with respect to a consisten snapshot of the database.
  3. Microblog:
  4. A read operation on user A’s followers list might tell us that user B follows A, but a read of user B’s following list might return an inconsistent result. This happens if first read witnessed the followUser transaction whereas second read did not. This situation can be prevented by wrapping both reads in a transaction and insisting that this transaction be executed in isolation with respect to a consistent version of the database.
  5. When retrieving a user(A)’s profile using username, we perform two reads - one to fetch user’s uuid from his username, and other to fetch the profile details indexed by uuid. When the user A tries to view her profile immediately after registering, the first read may succeed but second read may fail due to the absence of relevant record. This happens if the first read witnesses the effects of addUser transactions, whereas the second read does not. This situation can also be avoided by wrapping both reads in a transaction and running it under an appropriate isolation.
  6. Rubis:
  7. When displaying the list of all bids on an item, we might encounter an instance of referential integrity violation, although none actually exists in the data. This can happen if a read operation on ItemBids table reads latest version of the table, whereas the subsequent read on the Bids table reads an earlier version, which may not contain certain bids. Fix for this case is same as above.

To perform consistent (integrity-respecting) updates

Making atomic updates to the data in complete isolation can still leave the data in an invalid state. This happens when multiple such atomic updates succeed in complete isolation, but the merging resultant states results in an inconsistent state. Applications use serializable transactions to preempt the possibility of a concurrent conflicting update. Usecases:

  1. BankAccount
  2. A transfer transaction from account A to account B has to be serializable with respect to all other transfer transactions from account A to gaurantee application invariant of non-zero balance.
  3. Microblog:
  4. When deactivating the account of user (A), the userId has to be removed from the following list of all her followers, and subsequently from the user table. All operations should appear to have happened simultaneously, so they have to be wrapped in a transaction. Furthermore, to prevent violation of referential integrity, the transaction has to be serializable with respect to all addFollower transactions adding followers to A.
  5. Rubis:
  6. concludeAuction and cancelBid transactions both can independently succeed possibly resulting in a state, where a bid is simultaneously a winning bid and a canceled bid. To avoid this inconsistency, cancelBid transaction needs to be serializable with concludeAuction transaction.


  • Circular referencing.
  • Let a transaction T contain an SC operation. If a transaction T' requests RR isolation w.r.t T, then T and T' are automatically serializable.