Report Data Management in the Cloud: Limitations and Opportunities

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1 Report Data Management in the Cloud: Limitations and Opportunities Article by Daniel J. Abadi [1] Report by Lukas Probst January 4, 2013 In this report I want to summarize Daniel J. Abadi's article [1] and further present some open questions, points of criticism and recent research results. The report is structured in the following way: The sections 1-3 contain the content of the article itself plus some little additional comments of mine. 1 Section 4 lists some important questions, which the article left open and for which there were no solutions at the time the article was published. Section 5 concentrates on the point of criticism, that an untrusted host is not always a problem for OLTP applications and thus, unlike the article concludes, an OLTP application could be deployed in the cloud. Finally section 6 presents some new research results, which were published in the meantime: The NoDB approach [2] and Google's ready-to-use cloud products [3, 4]. 1 Introduction Daniel J. Abadi denes cloud computing as "a general shift of computer processing, storage, and software delivery away from the desktop and local servers, across the network, and into next generation data centers hosted by large infrastructure companies" 2. For many companies (especially for start-ups) the pay-as-you-go computing model a cloud provides, is a perfect match. Therefore Abadi's article [1] explores if also database management applications can be deployed in the cloud. 2 Data Management in the Cloud 2.1 Cloud Characteristics The goal of section 2 is to decide which data management applications can be deployed in the cloud. For this purpose Abadi rst presents the three most important characteristics of a cloud computing environment "Compute power is elastic, but only if workload is parallelizable" As already mentioned in the introduction, the major benet of cloud computing, is its elasticity. With the pay-as-you-go model a company can prevent both, an under utilization of the existing capacity (see gure 1(a)) and a lost revenue due to insucient capacity (see gure 1(b)), by always allocating as much capacity as needed (see gure 1(c)). But the desired behavior is only achievable, if the workload is parallelizable. That is due to the fact that allocating more capacity does not mean getting a better server but getting more nodes (e.g., Amazon EC2 instances). If the workload cannot be distributed among the nodes, new nodes will not be benecial. For example as one can easily see in gure 2, parallel reads are easy to implement, while parallel writes are a hard task. In general, one can say that shared-nothing architectures are the best to parallelize. 1 Please note that except of some restructuring the sections 1-3 are mainly a repetition of Daniel J. Abadi's work [1]. 2 Daniel J. Abadi's denition of cloud computing [1] 1

2 (a) Under utilization (b) Lost revenue (c) Pay-as-you-go cloud computing model Figure 1: Static capacity vs. the pay-as-you-go cloud computing model 3 (a) Parallel read (b) Parallel write Figure 2: Illustrations of parallel reads and writes "Data is stored at an untrusted host" If a company stores sensitive data, the company cannot exclude the possibility that the host company access data without permission and for example steals or sells sensitive data (e.g., credit card numbers) even if this scenario sounds very unlikely. Moreover, since the data have to be stored physically in any country, it is governed by the laws of this country. For example Abadi mentions in his article [1], that "the USA PATRIOT Act allows the US government to demand access to the data stored on any computer". For some companies these two points can be a problem "Data is replicated, often across large geographic distances" Since cloud providers often own data centers all over the world, they can provide the highest possible degree of fault tolerance, by automatically replicating the data across large geographic distances. For example data is stored very safely in respect to availability and durability, if it is stored at the same time in the USA, Europe and Australia. 2.2 Data Management Applications in the Cloud OLTP vs. OLAP After dening the most important cloud characteristics, the article checks if the two data management applications - transactional data managament (OLTP) and analytical data management (OLAP) - can be deployed in the cloud Transactional Data Management (OLTP) The common applications using transactional data management (OLTP) need ACID guarantees and further include many write operations. Since the requested data is typically distributed on several sites and thus transactions cannot be limited to access only data on one site, it would neccessitate "complex distibuted locking and commit protocols" to implement transactional data management systems with a shared-nothing architecture. 3 Source: Uni Basel, Departement of Mathematics and Computer Science, cs341 Distributed Information Systems (Fall Semester 2012) lecture slides, Chapter 7: Cloud Computing & NoSQL, slides 28-30, fileadmin/lectures/hs2012/cs341/slides/07-cs341-hs12-cloud_computing-nosql.pdf 2

3 For this reason, none of the 4 big players (Oracle, IBM DB2, Microsoft SQL Server and Sybase) has a shared-nothing transactional database. Furthermore it is hard to maintain ACID guarantees in the cloud. The CAP theorem shows, that one can only choose two out of three properties: consistency, availability and tolerance to partitions. Because partitions cannot be excluded, one always needs tolerance to partitions. Hence one typically decides to disregard the consistency (C from ACID), to gain a good availability. Moreover OLTP databases typically contain all the data, i.e, also the sensitive information such as credit card numbers. Hence, Abadi argues that it is an enormous risk to store transactional data on an untrusted host and that this risk is typically unacceptable and therefore transactional data cannot be stored at an untrusted host. Due to these observations, Abadi concludes that OLTP applications are not well-suited for cloud deployment Analytical Data Management (OLAP) Abadi argues in his article [1] that since the shared-nothing architecture scales the best and due to the huge amount of data scalability is very important for OLAP systems, this architecture is a good match. Furthermore the fact that the data analysis workloads tend to be read-only with only "infrequent writes" leads to two additional advantages: Firstly data analysis workloads are easy to parallelize across nodes (see section 2.1.3) and secondly there is no need for "complex distributed locking and commit protocols". Moreover since small inconsistencies are not problematic for analytical queries (e.g., computing the average customer age), the consistency trado (CAP theorem) is no problem for OLAP applications. Finally there are multiple possibilities to handle sensitive data for the analysis on an untrusted host. Abadi proposes in his article [1] that the sensitive data can be left out, anonymized or encrypted. Furthermore he suggests the possibility to store only aggregated data (e.g. averages, sums,...). Thus untrusted hosts can be used for storing analytical data. Because of these facts, Abadi concludes that OLAP applications, in contrast to OLTP applications, are well-suited for cloud deployment. 3 Data Analysis in the Cloud The rest of the article concentrates on how to perform data analysis (OLAP) in the cloud. Thereby the Abadi focuses in his article [1] on two classes of software solutions: "MapReduce-like software" and "commercially available shared-nothing parallel databases". 3.1 Cloud DBMS Wish List Before taking a closer look at MapReduce and shared-nothing parallel DBs the article [1] lists some properties that a good solution should provide: 1. Eciency: If one only pays for what one uses, the price increases linearly with the used resources. Hence one wants to use the most ecient OLAP software solution, because more ecient software is cheaper to use. 2. Fault Tolerance: Fault tolerance in terms of read-only (OLAP) queries means, that a query does not have to be restarted if a single node involved in the query fails. The problem is, that in a cloud, where the many involved nodes (e.g., Amazon EC2 instances) have a high failure rate (customer electronic), the probability of a failure on a single node during a long query is very high. Thus the system must be able to handle single failures without restarting the whole query. 3. Ability to run in a heterogeneous environment: Due to sometimes occurring hardware failures (e.g., a failing core) cloud computing nodes are unfortunately not as homogeneous as they should be. If the work is equally distributed to all nodes, the time to complete the query will be equal to the time the slowest node needs to complete its task. Because of this a system should have the ability to handle heterogeneous environments. 4. Ability to interface with business intelligence products: Since business analysts are typically no computer scientists there are many so called "business intelligence products" which helps them to generate queries and visualize results. If the database software wants to support these tools, it has to accept SQL queries over ODBC or JDBC connections. 3

4 (a) Fault tolerance (b) Heteregeneous Figure 3: MapReduce's ability to handle faults and slow nodes 5. Ability to operate on encrypted data: As already mentioned, a possibility to solve the untrusted host problem is storing only encrypted data. Abadi [1] argues that because providing the cloud application the possibility to encrypt the data would destroy the protection and transferring the data for encryption would be to bandwidth intensive, the system should be able to operate directly on encrypted data. 3.2 MapReduce vs. Shared-Nothing parallel DBs After presenting the desired properties, Abadi checks in his article [1] how good the two available solutions satisfy these properties: "MapReduce-like software" and "commercially available shared-nothing parallel databases". Although some people say, that comparing MapReduce to database systems is like "apples-to-oranges", I agree with Abadi's position that it is warranted, because in my opinion it is justiable to compare how two approaches solve the same problem Eciency Analytical queries perform much slower in MapReduce than in alternative systems like shared-nothing parallel DBs. Abadi argues in his article [1] that the reason for that is, that MapReduce was designed for working on unstructured data for which its "brute force scan strategy" is a good idea (e.g., creating web indexes). But in analytical data stores, where the data is structured, the shared-nothing parallel databases with their typical helper structures like indexes or dimensions outperform MapReduce. Some people say, that it is a feature that MapReduce does not have such helper structures because they need time be created when data is loaded, but usually the long-time benet outweighed these creation costs. I do not agree with Abadi's opinion that MapReduce's performance is a matter of debate, because what we wanted to check is only how MapReduce performs for analytical queries on large data stores and for this application MapReduce is very inecent Fault Tolerance While MapReduce is designed to be fault tolerant, the most parallel database systems are not. MapReduces can handle a single node failure by simply reassigning the data split (task) to a new worker node (see gure 3(a)). In contrast, shared-nothing parallel DBs are designed to run on special hardware, where failures are uncommon. Consequently they are not fault tolerant and restart a query if a single node fails Ability to run in a heterogeneous environment MapReduce can also handle heterogeneous environments with some slow nodes with nearly the same mechanism. For that purpose it simply has to reassign the split assigned to the slow worker to a second worker node if the most worker nodes already nished their tasks (see gure 3(b)). Conversely, sharednothing parallel DBs cannot handle heterogeneous nodes, because, like already mentioned, they are designed to run on special hardware. Due to this a single slow node can have a huge impact to the total query execution time. 4

5 Property MapReduce Shared-nothing parallel DB 1. Eciency 2. Fault Tolerance 3. Heterogeneous environment 4. Business intelligence products 5. Encrypted data Table 1: Overview which properties are fullled by the two software solutions Ability to interface with business intelligence products While in shared-nothing parallel DBs the ability to interface with business intelligence products comes for free, MapReduce is not SQL compatible and therefore it is not easy to use existing business intelligence products with MapReduce systems Ability to operate on encrypted data None of the both software solutions, has a native ability to operate directly on encrypted data. In MapReduce the only possibility is to provide user-dened code. Similarly, if more advanced operations than moving or copying encrypted data should be performed in shared-nothing parallel DBs, user-dened functions are required. 3.3 Conclusion A call for a hybrid solution As one can easily see in table 1, neither MapReduce nor shared-nothinig parallel DBs can fulll all properties. But except of the ability to operate on encrypted data, each property is fullled by one of the two solutions. Hence Abadi proposes that a hybrid solution would be the perfect solution. There is already some recent work done, which Abadi presents in his article [1], but regrettably the recent work only focuses on language and interface issues using SQL in MapReduce and using MapReduce functions in parallel databases. Finally Abadi presents in his article [1] two research questions and his ideas how to solve these. The rst question is, how to combine MapReduce's ability to directly work with the data and the performance increase through using helper data structures. His idea how to solve this problem is an incremantal algorithm which makes progress creating helper data structures each time the data is accessed. The second problem is that fault tolerance needs saving intermediate results and this costs performance. So the question is how to balance between fault tolerance and eciency. Abadi's idea to solve this problem is to build a system which autonomous self-adjusts the level of fault-tolerance based on the observed failure rate. 4 Open Questions Daniel J. Abadi's article [1] concentrates on these two questions: What can we do in the cloud? What solutions do we want for that? Although the article discusses and answers these two questions very detailed, there are still some open questions which have to be answered before one can deploy OLAP applications in the cloud. For example: How can we use the cloud today for data warehousing? Are there any useful products today we can use? How can we implement the hybrid solution? In this section, I will take a second look on the three proposed software solutions MapReduce, sharednothing parallel DBs and the hybrid solution and present some still unsolved problems. 5

6 Figure 4: MapReduce in the cloud 4.1 MapReduce Let us assume that we decided to run a MapReduce-like software in the cloud to support the OLAP applications. If one takes a look at gure 4 which illustrates this scenario, one can see that in this case we are faced with two questions. Firstly, we need many worker nodes to compute the map and the reduce step and another node to collecting the results. The question is, what kind of server instances (or other cloud products) should be used as nodes to gain the best performance. My suggestion would be to use an Amazon EC2 instance for each node, but the article presents no evaluations which could show that this would be a good or a bad decision. And even if this rst problem is solved, there is still the problem where to store the data in the cloud. There are reams of dierent cloud products to store data (e.g., Amazon S3), but the article does not provide any recommendation which to use. 4.2 Shared-nothing parallel databases In the second example scenario (illustrated in gure 5), we assume that our company currently owns many data warehouses and now wants to use only one giant shared-nothing parallel data warehouse in a cloud. So the rst question is, whether there is any existent shared-nothing parallel data warehouse product in any cloud we can use. If this is not the case we cannot solve the task unless our company wants to implement the product on its own. But even if we assume that there is such a product, we still have to solve the problem, how to integrate the data from the local data warehouses to the new cloud product. Additional to the typical schema integration problems, we are faced with another problem: Since typically the amount of data stored in data warehouses contain several petabytes, it is a big problem how to transfer the data from the local data warehouses to the cloud. Because with a common internet connection the integration task would take too long, there have to be another solution, which the article does not provide. 4.3 Hybrid solution As a conclusion of his article [1], Daniel J. Abadi proposes an hybrid as the perfect solution for data analysis in the cloud. Furthermore he presented some ideas how to solve the remaining research questions. Although the idea of having an incremental algorithm and an autonomous self-adjusting system sounds quite nice, Abadi does not mention if there are any sophisticated concepts implemented or at least presented yet. 5 Critique: "Untrusted hosts" are usable for OLTP Daniel J. Abadi argues in his article [1], that it is an enormous risk to store transactional data on an untrusted host, because OLTP data includes sensitive data. In the discussion after my workshop 6

7 Figure 5: Shared-Nothing parallel databases in the cloud and integration of existing data warehouses presentation we came to the conclusion, that this is not totally true. There are two possibilities for unauthorized data access in the cloud listed in the article. The rst is that the cloud provider itself steals or sells the data. As Abadi already mentioned in his article [1] this is very unlikely because in this case the cloud provider would eventually lose all its business customers. As a second risk Abadi argues that the USA PATRIOT Act gives the US government the right to access data on all computers located in the US and therefore also the right to access data stored in a cloud provider's data center in the US. In our discussion we gured out, that rstly it is not the idea of the US government to spy companies and secondly Abadi's point is simply not true. The USA PATRIOT Act only says, that internet providers have to disclose their data, i.e., the US government has the possibility to monitor and access data while the data is traveling through the internet. Hence if the sensitive data (e.g., credit card numbers) is only stored in the cloud and not transferred through the internet, no company has to fear that its sensitive data will be accessed without its knowledge. Since additionally writing a shared-nothing OLTP system is only hard but not impossible and other workshop topics presented solutions for the ACID problem, in my opinion it should be possible to deploy OLTP applications in the cloud. 6 Latest research results Since Daniel J. Abadi's article [1] was published in 2009, there were some new research results presented in the meantime. In this section I want to present the NoDB approach [2] as well as Google's ready-to-use cloud solutions [3, 4]. 6.1 NoDB In the NoDB article [2] they argue, that the major problem for applications, which have to handle giant amounts of data (e.g., social networks), is that in state-of-the-art OLAP database systems this giant amount of data has to be fully loaded and initialized before any data can be accessed (see also section 3.2.1). To handle this the NoDB approach [2] introduces the idea of "adaptive data loads" which is very similar to the incremental algorithm idea by Daniel J. Abadi [1]. In the article [2] they could furthermore show that their NoDB implementation (PostgresRaw) can compete with traditional DBMSs like PostgreSQL, i.e., the TPC-H performance was equivalent or faster. 6.2 Google's Cloud Solutions Google presented ready-to-use solutions for both OLTP and OLAP applications in its own cloud. Google BigQuery [3] is Google's OLAP solution. In Google BigQuery a customer can run analytical select queries in short time but he cannot run any update or delete queries. If a customer wants to have an OLTP database in the cloud, he can use Google Cloud SQL [4] instead. 4 4 This paragraph is only a short summary. For a more detailed comparison take a look at https://developers.google. com/bigquery/docs/overview 7

8 References [1] D. J. Abadi, Data Management in the Cloud: Limitations and Opportunities, IEEE Data(base) Engineering Bulletin, vol. 32, pp. 312, [2] I. Alagiannis, R. Borovica, M. Branco, S. Idreos, and A. Ailamaki, NoDB: ecient query execution on raw data les, pp , [3] Google BigQuery. https://developers.google.com/bigquery/, December [4] Google Cloud SQL. https://developers.google.com/cloud-sql/?hl=de, December

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