Batch-processing & Interactive Analytics for Big Data – the Role of in-Memory

In this blog post I will discuss various aspects of in-memory technologies and describe how various Big Data technologies fit into this context.

Especially, I will focus on the difference between in-memory batch analytics and interactive in-memory analytics. Additionally, I will illustrate when in-memory technology is really beneficial. In-memory technology leverages the fast main memory and processor caches to deliver superior performance.

While initially the deployment of in-memory technology seems to be attractive, companies have to carefully design how they use the scarce resource memory for big data sets, because the amount of data tends to grow when the company masters successfully Big Data technologies. For instance, they need to think about the issue of memory fragmentation, scheduling, capacity management, the decision how the data should be structured in-memory or making the decision about what data should be represented in-memory.

I will explain that some paradigms introduced for non-in-memory analytics, such as the paradigm that it is better if you do not need to read data than reading it all, is still very valid for in-memory technologies.

Finally, I will give an outlook on current Big Data technologies and their strength and weaknesses with respect to in-memory batch analytics and interactive in-memory analytics.

The Concept of In-Memory

The concept of in-memory became more and more popular around 2007/2008, although the fundamental concepts behind it exist since decades. It was marketed quite heavily by SAP and its HANA in-memory database at this time.

Around the same time, a different paradigm appeared, the concept of distributed Big Data platforms.

In the beginning, both were rather disconnected, where in-memory technologies relied on one “big” machine and distributed data platforms consisted out of a huge set of different more commodity-like machines. In-memory was at this time often associated with interactive queries with fast responses for comparable small datasets fitting in-memory on machine and Big Data platforms for long-running analytics queries crunching large volumes of data scattered over several nodes.

This changed recently. Particularly, in-memory techniques have been brought to long-running analytics queries and distributed Big Data platforms to interactive analytics. The assumed benefit for both cases is that more data can be handled in more complex ways in a shorter time.

However, you need to carefully look what kind of business benefits you can gain from doing faster or more analytics.

Public sector organizations over various domains have significant benefits, because their “ROI” is usually measured in non-monetary terms as benefits for society. A faster, fair and more transparent or scientifically correct analysis can be one example of such a benefit. Additionally, supervision of the private sector need to be on the same level as the private sector.

Traditional private sector organizations on the other hand will have to invent new business models and convince the customer. Here, new machine learning algorithms on large data volumes are more beneficial in comparison of traditional data warehouse reports. Internet Industries including the Internet of Things and autonomous robots obviously have some benefits let it be the processing of large data volumes and/or the need to react quickly to events in the real world.

The Difference between in-memory batch processing and interactive analytics

Often people wonder why there is still a difference between batch processing and interactive analytics when using in-memory. In order to answer this question let us quickly recap the difference between the two:

  • Distributed big data processes: They are long-running because they need to query data residing on several nodes and/or calculations are very complex requiring a lot of computing power. Usually they make calculation/processed data available in a suitable format for interactive analytics. Usually, these processes are planned and scheduled in advance.
  • Interactive analytics: These are often ad-hoc queries from low to very high complexity. Usually it is expected that they return results within seconds or minutes. However, they can also take much longer and are then candidate for distributed big data processes, so that results are precomputed and stored for interactive analytics. Interactive analytics go beyond standard tables to return results faster.

The results of them can be either used by humans or by other applications, e.g. applications that require prediction to provide an automated service to human beings. Basically both approaches fit to the Lambda architecture paradigm.

In-memory technologies can basically speed up both approaches. However, you need to carefully evaluate your business case for this. For example, it make sense to speed up your Big Data batch processes to finish before your people start working or to have more time to do perform additional processes on the same resources – This is particularly interesting if you have a plethora of different large datasets where different analytics can make sense. With respect to interactive analytics, you benefit most if you have specific analytics algorithms that benefit from memory locality, e.g. iterative machine learning algorithms.

If you have people working on large tables using aggregations then you should make them aware that it make more sense to work with samples, in-memory indexes and data structures as well as high parallelism. Aggregating data of a large table in-memory is very costly and the speed difference to tables on disk is most likely not much. The core paradigm should be here: do not read what is needed.

To make it short: Be aware of your priorities to leverage speed-ups by using in-memory technology. Not everything has to be in-memory.

Nevertheless, you should first leverage all possible optimizations without using in-memory technology. An inefficient data structure on-disk is not a better structure if it is in-memory. Additionally, you should think about how much data you need and how precise your results need to be. As I wrote in a previous blog post, this can save you a lot of time that you can use to perform further analytic tasks.

We will in the following describe some challenges with in-memory that you need to tackle to be successful with in-memory technologies.

Challenges with in-memory

Memory fragmentation


Memory fragmentation does not only occur with in-memory technologies, but on any storage. You can have internal fragmentation, where you allocate more memory to an application than needed or external fragmentation, where you deallocate memory, but new data does not fit into the deallocated memory and you have to use additional memory.

However, it can be rather problematic with in-memory technologies because main memory is usually the smallest storage available. In the context of Big Data, where there can be a huge diversity of different data sets that grow from time to time as well as different algorithms that use memory in a different way, this becomes much quicker apparent as if there would be just one static data set that does not change and is always processed the same way.

The issue here with memory fragmentation is that you have less memory than physically available – potentially a lot less. This leads to unexpected performance degradation and the need to spill over to slower disk space to continue the computation, which may lead to thrashing.

You cannot avoid memory fragmentation, because one cannot look into the future when which data set is loaded and what computation is needed.


As a first step to handle memory fragmentation is to have a list of processes and interactive queries that are regularly executed and to look at them to see any potential issues with memory fragmentation. This can be used during monitoring to be aware of memory fragmentation. One indicator can be that the available memory does not match the memory that should be consumed. Another indicator can be a lot of spills to disk.

There are several strategies to handle identified memory fragmentation. In case of in-memory batch processes, one should release all the memory after the batch process have been executed. Furthermore, one should use distributed Big Data technologies, which usually work with fixed block sizes from the distributed file system layer (e.g. HDFS). In this case you can partially avoid external fragmentation. You can avoid it only partially, because many algorithms have some temporary data or temporary relevant data which needs to be taken into account as well.

If you have interactive analytics, a very common recommendation even by vendors of popular memcache solutions is to restart the cache from time to time and thereby forcing to reload the data in an ordered manner into cache avoiding fragmentation. Of course, once you add, modify, remove data you have again some fragmentation, which will grow over time.

Another similar approach is called compaction, which exist in traditional relational databases and big data systems. Compactation reduces fragmentation that occurs due to updates, deletion and insertion of new data. The key here is that you can gain performance benefits for your users if you schedule it to time where the system is not used. Surprisingly, often people do not look at compaction, although it has significant impact on performance and memory usage. Instead they rely only on non-optimal default settings, which usually not for large scale analytics, but smaller scale OLTP usage. For instance, it can make sense for large scale analytics to schedule compaction after loading all the data and no new data is arriving before the next execution of a batch processing process.

What data should be in-memory? About temperature of data…

The Concept

It is not realistic to have all your data in-memory. This is not only due to memory fragmentation, but also costs for memory, fault-tolerance, backups and many others. Hence, you need an approach to decide which data should be in-memory.

As already described before it is important to know your data priorities. Quite often these priorities change, e.g. new data is introduced, or data simply becomes outdated. Usually it is reasonable to expect that data that is several months or years old will not often be touched, expect for research purposes. Here is where the temperature of data, i.e. hot, warm and cold data comes into play.

Hot data has been used recently quiet frequently and is likely to be used quiet frequently in the near future.

Warm data has been used recently not as frequently as hot data and it is NOT likely to be used frequently in the near future.

Cold data has not been used recently and is not likely to be used in the near future.

Usually hot data resides on CPU caches and mainly on main memory. Warm data resides mainly on local disk drives and only a small fraction in main memory. Cold data resides mostly on external slow storage potentially accessed via the network or in the cloud.

Managing Temperature of Data

The concept of temperature of data applies to batch processes and interactive analytics equally. However, you need to think about what data needs to be kept hot, warm and cold. Ideally this happens automatically. For example many common in-memory system provide the strategy LRU (last recently used) to automatically move hot data to warm data and eventually to cold data and the other way around. For instance, Memcached or SAP HANA support this as a default strategy.

This seems to be a good default strategy, especially if you cannot or do not want to look into more detail about the temperature of data. Indeed, it has also some sound assumptions, since it is based on the principal of locality, which is also key to distributed Big Data processes and many machine learning algorithms.

However, there are alternative strategies to LRU that you may want to think about:

  • Most recently used (MRU): The most recently used memory element is moved to warm and eventually to cold storage. This assumes that there is stable data that is more relevant than having the newest data.
  • Least frequently used (LFU): The data which has been least frequently used will be moved to warm storage and eventually to cold storage. The advantage here is that recently used data that has been only accessed once is quickly moved as well as data which has been accessed quiet frequently, but not in the near past, will stay in-memory.
  • Most frequently used (MFU): The data which has been most frequently used in the past will be moved from warm storage and eventually to cold storage. The idea here is that the more data has been used the less valuable it will be and hence will be accessed much less in the near-future.
  • Any combination of the above

Obviously, the most perfect strategy would predict what data would least be used in the future (“Clearvoyant algorithms”) and move data accordingly to hot, warm, and cold storage. This is of course not exactly possible, but a sound understanding on how people use your Big Data platform can come pretty close to that ideal.

Of course, you can implement also more sophisticated machine learning algorithms that take into account the environment to predict what data and computation will be required in the future given a new task (cf. here for an approach for scheduling multimedia tasks in the cloud based on machine learning algorithms – the use case is different but the general idea the same). Unfortunately, most of the popular Big Data and in-memory solutions do not implement such an approach yet.

How should the data be structured?

Many people, including business people, have only the traditional world of tables, consisting of rows or columns, in mind when using data. In fact, a lot of analysis is based on this assumption. However, while tables are simple they might not be the most efficient way to store data in-memory or even to process it.

In fact, depending on your analysis different formats make sense, such as:

  • Use the correct data type: If you have numbers use a data types that support numbers, such as integer or double. Dates can often be represented as integers. This requires less storage and the cpu can read an integer represented as integer in magnitudes faster than an integer represented as a string. Similarly even with integer, you should select an appropriate size. If your numbers fit into a 32-bit integer then you should prefer storing it as 32-bit instead of 64-bit. This will increase your performance significantly. However, the key message here is store the data with the right data type and use the available ones and understand their advantages as well as limitations.
  • Column-based: Data is stored in columns instead of rows. This is usually beneficial if you need to access one or more full columns of a given data set. Furthermore, it enables one to avoid reading data that is not relevant using storage indexes (min/max) or bloom filters.
  • Graph-based: Data is stored as so-called Adjacency lists or sometimes as Adjacency matrices. This shows much more performance than row-based or column-based storage with respect to graph algorithms, such as strongly connected components, shortest path etc. These algorithms are useful for social network analytics, financial risks of assets sold by different organizations, dependencies between financial assets etc.
  • Tree-based: Data is stored in a tree structure. Trees can be searched usually comparable fast and is often used for database indexes to find out in which data block a row is stored.
  • Search indexes for terms in unstructured text. This is usually useful for matching data objects, which are similar, but do not have unique identifiers. Alternatively, they can be used for sentiment analysis.Traditional database technology shows, for example, a terrible performance for these use cases – even in-memory.
  • Hash-Clustering Indexes: This can be used in columns stores by generating a hash out of the values of several columns for one row. This hash is stored as another column. It can be used for quickly searching for several criteria at the same time by using only one column. This reduces the amount of data to be processed at the expense of additional storage needed.

Furthermore, the same data might be stored in different formats on warm or cold storage, meaning that you have to decide if you want to have redundant data or generate each time from scratch the optimal storage of data for a given computation.

Compression can make sense for data in-memory, because it enables storing more data in-memory instead of slower disk drives.

Unfortunately, contrary to the strategies to manage data temperature, there are currently no mature strategies to support you automatically how to store the data. This is a manual decision and thus requires good knowledge how your Big Data platform is used.

Do we always need in-memory?

With the need for processing large data sets some things became apparent: It is even with new technologies, such as in-memory or Big Data platforms, sometimes very inefficient to process data by looking at all of the data – it is better not to read data at all!

Of course, this means you should not read not-relevant data. For instance, it was very common in traditional databases to read all the rows to find out matching rows according to a query. Even when using indexes, some irrelevant data is read when scanning the index, although storing the index as a tree structure increased search performance already a lot.

More modern solutions use storage indexes and/or bloom filters to decide which rows they need to read. This means they can skip blocks of data where the rows not matching to a query are not contained (cf. here for implementation in Apache Hive).

Similarly, probablistic data structures, such as Hyperloglog or data based on sampling (cf. here) enables one to avoid reading all the data again or at all. In fact, here you can even skip “relevant” data – as long as you read enough data to provide correct results within a small error margin.

Hence, even with in-memory technologies it is always better to avoid reading data. Even if the data is already in-memory, the CPU needs more time the more data it has to process – a simple but often overlooked fact.

The impact of Scheduling: Pre-emption

Once your Big data platform or in-memory platform grows, you will not only get more data, but also more user working on it in parallel. This means if they use interactive queries or schedule Big Data processes then they need to share resources, including memory and CPU. Especially when taking into account speculative execution. As described before, you ideally have a general big picture on what will happen, especially with main memory. However, in peak times, but for some Big Data deployments also most of the time, the resources are not enough, because of cost or other reasons.

This means you need to introduce scheduling according to scheduling policies. We briefly touched this topic before, because the concept of temperature of data implies some kind of scheduling. However, if you have a lot of users the bottleneck is merely the number of processors that process data. Hence, sometimes some analytics by some users are partially interrupted to make some resources free for other users. These users may potentially use different data sets meaning that some data might be moved also from main memory to disk drives. After the interrupted tasks are resumed they may need to reload data from disk drives to memory.

This can make performance experience sometimes unpredictable and you should be aware of it so you can react properly to incidents created by users or do a more informed capacity management.

Big Data technologies for in-memory

In-memory batch processing

There are several in-memory batch processing technologies for Big Data platforms. For example, Apache Spark or Apache Flink. In the beginning, these platform especially Spark, had some drawbacks by representing everything as Java-Objects in memory. This would mean, for instance, a 10 character String would consume 6 times more memory then representing it as an array of bytes.

Luckily this changed and data is now stored in-memory in a columnar fashion supporting also to skip data on disk that is not relevant (via predicate pushdown and an appropriate disk storage format, such as ORC or Parquet).

Additionally, both support graph batch processing and processing of continuous streams in-memory. However, both rely on a common abstraction for a data structure in which they represent other data structures, such as graphs. For example, in case of Spark it is Resilient Distributed Datasets (RDD)/dataframes). This means they have not as much performance as a highly specialized graph engine, but they are more generic and it is possible to integrate them with other data structures. For most of the current use cases it is sufficient.

Additionally, different processing algorithms, mainly in the area of machine learning are supported.

Sometimes you will see that they are also advertised as interactive platforms. However, this is not their core strength, because they do not support, for example, the concept of data temperature automatically, i.e. the developer is fully responsible to take into account hot, warm, cold data or to implement a strategy as described above. Additionally, they do not provide index support for data in-memory, because this is usually much less relevant for batch processes. Hence, if you want to use these technologies for inter-active analysis you have to develop some common IT components and strategies how to address temperature of data and the do not read irrelevant data paradigm.

In any case you have to think about scheduling strategies to optimize your resource usage of your available infrastructure.

Depending on your requirements, in-memory batch processing is not needed in all cases and your big data platform should support both: in-memory batch processes, but also non in-memory batch processes to be efficient. Especially, if your batch process only loads as well processes once the data without re-reading parts of the data then you won’t benefit a lot from in-memory.

Interactive in-memory analytics

There are several technologies enabling interactive in-memory analytics. One of the older – but still highly relevant – ones is memcached for databases. Its original use case was to speed up web applications accessing the database with many user accessing, i.e. writing and reading, in parallel the same data. Similar technologies are also used for Master Data Management (MDM) systems, because they need to deliver and receive data from a lot of sources to different systems as well as business processes with many users. This would be difficult if one relies only on databases.

Other technologies focus on the emerging Big Data Platforms based on Hadoop, but also augment in-memory batch processing engines, such as Spark. For instance, Apache Ignite provides functionality similar to memcached, but also supporting Big Data platforms and in-memory batch processing engines. For example, you can create shared RDDs for Spark. Furthermore, you can cache Hive tables or partitions in-memory. Alternatively, you can use the Ignite DataGrid to cache selected queries in-memory.

These technologies support advanced in-memory indexes (keep in mind: it is always better not to read data!) and automated data temperature management. Another example is Apache Tachyon.

There are also very specialized interactive in-memory analytics engines, such as TitanDB for graphs. TitanDB is based on the Tinkerpop graph stack including the interactive Graph query (or graph traversal) language Gremlin. SAP HANA is a specific in-memory column database for OLTP, OLAP, text-analytics and graph applications. It has been extended to a full application stack cloud platform based on in-memory technology.

Taking into account scheduling is much more tricky with interactive analytics, because one does not know what the users exactly will do and prediction engines of user behavior for interactive analytics are currently nearly non-existing.

However, you can define different categories of interactive analytics (e.g. simple queries, complex queries, machine learning, graphs ….) and determine your infrastructure as well as its configuration based on these categories.


It makes sense to distinguish between in-memory batch processes and in-memory analytics. In-memory batch processes can be planned and scheduled easier in advance. Additionally, one can better optimize resources for this. They also are more focused towards processing all data. Specific technologies for distributed in-memory Big Data exists and are complementary to technologies for interactive in-memory analytics. The main difference are additional indexes and automated support for the concept of data temperature.

Even for in-memory technology the key concept of Big Data to not read data that is not relevant is of high importance. Processing terabytes of data in-memory even though only a subset is relevant is a waste of resources and particularly time. This is specially difficult to handle for interactive in-memory analytics where the user can do what they want. Hence, automated and intelligent mechanisms to support this are highly desirable. They should be preferred to manual developing the right data model and structures.

Another key concept is to have the right data structure in-memory for optimal processing. For instance, graph structures show much more performance in comparison to relational row or column-based structures that need to be joined very often to perform graph algorithms. Furthermore, probabilistic data structures and probabilistic sampling queries have a growing importance. Depending on your needs you might have the same data represented redundant in different data structures for different analysis purposes.

Finally, distinguishing interactive analytics and batch processing is not always that straight forward. For instance, you can have a batch process running 5 minutes, but the results are queried 1000 times and thus avoiding each time 5 minutes run time can be very beneficial. On the other hand you can have an interactive query by one user which takes 60 minutes, but it is only needed by one user once. This may also change over time, so it is important that even after development of a solution that you monitor and audit it regularly by business users and technical users to check if the current approach still make sense or another approach makes more sense. This requires a regular dialogue even after go-live of a Big Data application.

The convergence of both concepts requires more predictive algorithms for managing data in-memory and for queries. These can be seen only in their first stages, but I expect much more over the coming years.