A NUMA-Aware Query Evaluation Framework for the Many-Core Age
With modern computer architecture evolving, two problems conspire against the state-of-the-art approaches in parallel query execution: (i) to take advantage of many-cores, all query work must be distributed evenly among (soon) hundreds of threads in order to achieve good speedup, yet (ii) dividing the work evenly is difficult even with accurate data statistics due to the complexity of modern out-of-order cores. As a result, the existing approaches for “plandriven” parallelism run into load balancing and context-switching bottlenecks, and therefore no longer scale. A third problem faced by many-core architectures is the decentralization of memory controllers, which leads to Non-Uniform Memory Access (NUMA).

1 . Morsel-Driven Parallelism: A NUMA-Aware Query Evaluation Framework for the Many-Core Age Viktor Leis∗ Peter Boncz† Alfons Kemper∗ Thomas Neumann∗ ∗ † Technische Universität München CWI ∗ † {leis,kemper,neumann}@in.tum.de p.boncz@cwi.nl ABSTRACT HT(T) HT(S) B C A B With modern computer architecture evolving, two problems con- Result probe(8) 16 8 8 v R spire against the state-of-the-art approaches in parallel query exe- Z A B C probe(16) store 33 x probe(10) A Z a 16 8 v 18 33 cution: (i) to take advantage of many-cores, all query work must 10 y 16 a ... ... ... ... 27 10 be distributed evenly among (soon) hundreds of threads in order to ... ... ... ... 7 c store probe(27) achieve good speedup, yet (ii) dividing the work evenly is difficult 5 5 10 i Z A B C 5 z 27 b even with accurate data statistics due to the complexity of modern b 27 10 y 7 23 18 e out-of-order cores. As a result, the existing approaches for “plan- ... ... ... ... 23 u 5 j driven” parallelism run into load balancing and context-switching ... ... ... ... 7 d bottlenecks, and therefore no longer scale. A third problem faced morsel 5 f by many-core architectures is the decentralization of memory con- ... ... trollers, which leads to Non-Uniform Memory Access (NUMA). ... ... Dispatcher morsel ... ... In response, we present the “morsel-driven” query execution ... ... framework, where scheduling becomes a fine-grained run-time task ... ... that is NUMA-aware. Morsel-driven query processing takes small fragments of input data (“morsels”) and schedules these to worker Figure 1: Idea of morsel-driven parallelism: R ✶A S ✶B T threads that run entire operator pipelines until the next pipeline breaker. The degree of parallelism is not baked into the plan but can Intel’s forthcoming mainstream server Ivy Bridge EX, which can elastically change during query execution, so the dispatcher can re- run 120 concurrent threads, will be available. We use the term act to execution speed of different morsels but also adjust resources many-core for such architectures with tens or hundreds of cores. dynamically in response to newly arriving queries in the workload. At the same time, increasing main memory capacities of up to Further, the dispatcher is aware of data locality of the NUMA-local several TB per server have led to the development of main-memory morsels and operator state, such that the great majority of execu- database systems. In these systems query processing is no longer tions takes place on NUMA-local memory. Our evaluation on the I/O bound, and the huge parallel compute resources of many-cores TPC-H and SSB benchmarks shows extremely high absolute per- can be truly exploited. Unfortunately, the trend to move memory formance and an average speedup of over 30 with 32 cores. controllers into the chip and hence the decentralization of mem- ory access, which was needed to scale throughput to huge mem- Categories and Subject Descriptors ories, leads to non-uniform memory access (NUMA). In essence, H.2.4 [Systems]: Query processing the computer has become a network in itself as the access costs of data items varies depending on which chip the data and the access- ing thread are located. Therefore, many-core parallelization needs Keywords to take RAM and cache hierarchies into account. In particular, the Morsel-driven parallelism; NUMA-awareness NUMA division of the RAM has to be considered carefully to en- sure that threads work (mostly) on NUMA-local data. 1. INTRODUCTION Abundant research in the 1990s into parallel processing led the The main impetus of hardware performance improvement nowa- majority of database systems to adopt a form of parallelism in- days comes from increasing multi-core parallelism rather than from spired by the Volcano [12] model, where operators are kept largely speeding up single-threaded performance [2]. By SIGMOD 2014 unaware of parallelism. Parallelism is encapsulated by so-called “exchange” operators that route tuple streams between multiple threads each executing identical pipelined segments of the query Permission to make digital or hard copies of part or all of this work for personal or plan. Such implementations of the Volcano model can be called classroom use is granted without fee provided that copies are not made or distributed plan-driven: the optimizer statically determines at query compile- for profit or commercial advantage, and that copies bear this notice and the full ci- time how many threads should run, instantiates one query operator tation on the first page. Copyrights for third-party components of this work must be plan for each thread, and connects these with exchange operators. honored. For all other uses, contact the owner/author(s). Copyright is held by the In this paper we present the adaptive morsel-driven query execu- author/owner(s). tion framework, which we designed for our main-memory database SIGMOD’14, June 22–27, 2014, Snowbird, UT, USA. ACM 978-1-4503-2376-5/14/06. system HyPer [16]. Our approach is sketched in Figure 1 for the http://dx.doi.org/10.1145/2588555.2610507 . three-way-join query R ✶A S ✶B T . Parallelism is achieved 743

2 .by processing each pipeline on different cores in parallel, as indi- • Morsel-driven query execution is a new parallel query eval- cated by the two (upper/red and lower/blue) pipelines in the fig- uation framework that fundamentally differs from the tra- ure. The core idea is a scheduling mechanism (the “dispatcher”) ditional Volcano model in that it distributes work between that allows flexible parallel execution of an operator pipeline, that threads dynamically using work-stealing. This prevents un- can change the parallelism degree even during query execution. A used CPU resources due to load imbalances, and allows for query is divided into segments, and each executing segment takes elasticity, i.e., CPU resources can be reassigned between dif- a morsel (e.g, 100,000) of input tuples and executes these, mate- ferent queries at any time. rializing results in the next pipeline breaker. The morsel frame- • A set of fast parallel algorithms for the most important re- work enables NUMA local processing as indicated by the color lational operators. coding in the figure: A thread operates on NUMA-local input and • A systematic approach to integrating NUMA-awareness into writes its result into a NUMA-local storage area. Our dispatcher database systems. runs a fixed, machine-dependent number of threads, such that even if new queries arrive there is no resource over-subscription, and The remainder of this paper is organized as follows. Section 2 is these threads are pinned to the cores, such that no unexpected loss devoted to a detailed discussion of pipeline parallelization and the of NUMA locality can occur due to the OS moving a thread to a fragmentation of the data into morsels. In Section 3 we discuss the different core. dispatcher, which assigns tasks (pipeline jobs) and morsels (data The crucial feature of morsel-driven scheduling is that task dis- fragments) to the worker threads. The dispatcher enables the full tribution is done at run-time and is thus fully elastic. This allows elasticity which allows to vary the number of parallel threads work- to achieve perfect load balancing, even in the face of uncertain size ing on a particular query at any time. Section 4 discusses algorith- distributions of intermediate results, as well as the hard-to-predict mic and synchronization details of the parallel join, aggregation, performance of modern CPU cores that varies even if the amount and sort operators. The virtues of the query engine are evaluated in of work they get is the same. It is elastic in the sense that it can Section 5 by way of the entire TPC-H query suite. After discussing handle workloads that change at run-time (by reducing or increas- related work in order to point out the novelty of our parallel query ing the parallelism of already executing queries in-flight) and can engine architecture in Section 6, we conclude the paper. easily integrate a mechanism to run queries at different priorities. The morsel-driven idea extends from just scheduling into a com- 2. MORSEL-DRIVEN EXECUTION plete query execution framework in that all physical query opera- Adapted from the motivating query of the introduction, we will tors must be able to execute morsel-wise in parallel in all their ex- demonstrate our parallel pipeline query execution on the following ecution stages (e.g., both hash-build and probe), a crucial need for example query plan: achieving many-core scalability in the light of Amdahl’s law. An important part of the morsel-wise framework is awareness of data σ... (R) ✶A σ... (S) ✶B σ... (T ) locality. This starts from the locality of the input morsels and ma- Assuming that R is the largest table (after filtering) the optimizer terialized output buffers, but extends to the state (data structures, would choose R as probe input and build (team) hash tables of the such as hash tables) possibly created and accessed by the opera- other two, S and T . The resulting algebraic query plan (as obtained tors. This state is shared data that can potentially be accessed by by a cost-based optimizer) consists of the three pipelines illustrated any core, but does have a high degree of NUMA locality. Thus on the left-hand side of Figure 2: morsel-wise scheduling is flexible, but strongly favors scheduling choices that maximize NUMA-local execution. This means that re- 1. Scanning, filtering and building the hash table HT (T ) of mote NUMA access only happens when processing a few morsels base relation T , per query, in order to achieve load balance. By accessing local 2. Scanning, filtering and building the hash table HT (S) of ar- RAM mainly, memory latency is optimized and cross-socket mem- gument S, ory traffic, which can slow other threads down, is minimized. In a pure Volcano-based parallel framework, parallelism is hid- 3. Scanning, filtering R and probing the hash table HT (S) of den from operators and shared state is avoided, which leads to plans S and probing the hash table HT (T ) of T and storing the doing on-the-fly data partitioning in the exchange operators. We result tuples. argue that this does not always lead to the optimal plan (as parti- HyPer uses Just-In-Time (JIT) compilation to generate highly tioning effort does not always pay off), while the locality achieved efficient machine code. Each pipeline segment, including all oper- by on-the-fly partitioning can be achieved by our locality-aware ators, is compiled into one code fragment. This achieves very high dispatcher. Other systems have advocated per-operator paralleliza- raw performance, since interpretation overhead as experienced by tion [21] to achieve flexibility in execution, but this leads to need- traditional query evaluators, is eliminated. Further, the operators less synchronization between operators in one pipeline segment. in the pipelines do not even materialize their intermediate results, Nevertheless, we are convinced that the morsel-wise framework which is still done by the already much more efficient vector-at-a- can be integrated in many existing systems, e.g., by changing the time evaluation engine of Vectorwise [34]. implementation of exchange operators to encapsulate morsel-wise The morsel-driven execution of the algebraic plan is controlled scheduling, and introduce e.g., hash-table sharing. Our framework by a so called QEPobject which transfers executable pipelines to a also fits systems using Just-In-Time (JIT) code compilation [19, 25] dispatcher – cf. Section 3. It is the QEPobject’s responsibility to as the generated code for each pipeline occurring in the plan, can observe data dependencies. In our example query, the third (probe) subsequently be scheduled morsel-wise. In fact, our HyPer system pipeline can only be executed after the two hash tables have been uses this JIT approach [25]. built, i.e., after the first two pipelines have been fully executed. In this paper we present a number of related ideas that enable For each pipeline the QEPobject allocates the temporary storage efficient, scalable, and elastic parallel processing. The main contri- areas into which the parallel threads executing the pipeline write bution is an architectural blueprint for a query engine incorporating their results. After completion of the entire pipeline the tempo- the following: rary storage areas are logically re-fragmented into equally sized 744

3 . Probe HT(T) Probe HT(T) Probe HT(S) BB Probe HT(T) Probe HT(S) Probe HT(T) Probe HT(S) Build HT(S) s... Probe HT(S) Build HT(T) BA v s... s... Pipe 3 s... s s...s... ... s... s... Pipe 3 Scan R s... Pipe 3 T v v Pipe 1 Pipe 2 Pipe 2 Pipe 2 Pipe 3 Scan R Scan R Pipe 1 Scan S Pipe 1 Scan R Scan T Scan S Scan S Scan T S R Scan T Figure 2: Parallellizing the three pipelines of the sample query plan: (left) algebraic evaluation plan; (right) three- respectively four-way parallel processing of each pipeline HT(T) In our figure three parallel threads are shown, each of which op- global erates on one morsel at a time. As our base relation T is stored Phase 2: scan NUMA-local storage area Hash Table “morsel-wise” across a NUMA-organized memory, the scheduler assigns, whenever possible, a morsel located on the same socket and insert pointers into HT In where the thread is executed. This is indicated by the coloring in s er int tt o the figure: The red thread that runs on a core of the red socket is he HT po assigned the task to process a red-colored morsel, i.e., a small frag- in te r ment of the base relation T that is located on the red socket. Once, the thread has finished processing the assigned morsel it can either be delegated (dispatched) to a different task or it obtains another Storage Phase 1: process T morsel-wise and store NUMA-locally Storage Storage morsel (of the same color) as its next task. As the threads pro- scan scan area of area of area of green core blue core cess one morsel at a time the system is fully elastic. The degree of red core parallelism (MPL) can be reduced or increased at any point (more precisely, at morsel boundaries) while processing a query. The logical algebraic pipeline of (1) scanning/filtering the input v...(T) v...(T) v...(T) T and (2) building the hash table is actually broken up into two physical processing pipelines marked as phases on the left-hand side of the figure. In the first phase the filtered tuples are inserted orsel into NUMA-local storage areas, i.e., for each core there is a sep- m arate storage area in order to avoid synchronization. To preserve next NUMA-locality in further processing stages, the storage area of a morsel particular core is locally allocated on the same socket. After all base table morsels have been scanned and filtered, in the second phase these storage areas are scanned – again by threads lo- T cated on the corresponding cores – and pointers are inserted into the hash table. Segmenting the logical hash table building pipeline into two phases enables perfect sizing of the global hash table be- Figure 3: NUMA-aware processing of the build-phase cause after the first phase is complete, the exact number of “surviv- ing” objects is known. This (perfectly sized) global hash table will be probed by threads located on various sockets of a NUMA sys- morsels; this way the succeeding pipelines start with new homoge- tem; thus, to avoid contention, it should not reside in a particular neously sized morsels instead of retaining morsel boundaries across NUMA-area and is therefore is interleaved (spread) across all sock- pipelines which could easily result in skewed morsel sizes. The ets. As many parallel threads compete to insert data into this hash number of parallel threads working on any pipeline at any time is table, a lock-free implementation is essential. The implementation bounded by the number of hardware threads of the processor. In details of the hash table are described in Section 4.2. order to write NUMA-locally and to avoid synchronization while After both hash tables have been constructed, the probing pipeline writing intermediate results the QEPobject allocates a storage area can be scheduled. The detailed processing of the probe pipeline is for each such thread/core for each executable pipeline. shown in Figure 4. Again, a thread requests work from the dis- The parallel processing of the pipeline for filtering T and build- patcher which assigns a morsel in the corresponding NUMA parti- ing the hash table HT (T ) is shown in Figure 3. Let us concentrate tion. That is, a thread located on a core in the red NUMA partition on the processing of the first phase of the pipeline that filters in- is assigned a morsel of the base relation R that is located on the cor- put T and stores the “surviving” tuples in temporary storage areas. 745

4 . Storage HT(T) HT(S) area of Lock-free Data Structures of Dispatcher blue core List of pending pipeline-jobs (possibly belonging to different queries) Storage area of Dispatcher Pipeline- Job Pipeline- Job green core Code J1 J2 Storage M r1 M g1 M b1 area of r1 red core on (red J1 on morsel M M M M 0 v...(R) v...(R) v...(R) r2 g2 b2 of Core dispatch(0) M r3 M g3 M b3 el (J1, M ) mors r1 ) socket morsel next -Job (virtual) lists of morsels to be processed (colors indicates on what socket/core Pipeline the morsel is located) R Socket Socket DRAM DRAM DRAM DRAM Core0 Core Core Core Core Core Core Core Figure 4: Morsel-wise processing of the probe phase Core Core Core Core Core Core Core Core inter connect Core8 Core Core Core Core Core Core Core responding “red” NUMA socket. The result of the probe pipeline Core Core Core Core Core Core Core Core is again stored in NUMA local storage areas in order to preserve Socket Socket NUMA locality for further processing (not present in our sample query plan). Example NUMA Multi-Core Server with 4 Sockets and 32 Cores In all, morsel-driven parallelism executes multiple pipelines in parallel, which is similar to typical implementations of the Vol- Figure 5: Dispatcher assigns pipeline-jobs on morsels to cano model. Different from Volcano, however, is the fact that threads depending on the core the pipelines are not independent. That is, they share data struc- tures and the operators are aware of parallel execution and must 3. Load balancing requires that all cores participating in a query perform synchronization (through efficient lock-free mechanisms pipeline finish their work at the same time in order to prevent – see later). A further difference is that the number of threads exe- (fast) cores from waiting for other (slow) cores1 . cuting the plan is fully elastic. That is, the number may differ not only between different pipeline segments, as shown in Figure 2, but In Figure 5 the architecture of the dispatcher is sketched. It also inside the same pipeline segment during query execution – as maintains a list of pending pipeline jobs. This list only contains described in the following. pipeline jobs whose prerequisites have already been processed. E.g., for our running example query the build input pipelines are first inserted into the list of pending jobs. The probe pipeline is only 3. DISPATCHER: SCHEDULING PARALLEL inserted after these two build pipelines have been finished. As de- PIPELINE TASKS scribed before, each of the active queries is controlled by a QEPob- ject which is responsible for transferring executable pipelines to The dispatcher is controlling and assigning the compute re- the dispatcher. Thus, the dispatcher maintains only lists of pipeline sources to the parallel pipelines. This is done by assigning tasks to jobs for which all dependent pipelines were already processed. In worker threads. We (pre-)create one worker thread for each hard- general, the dispatcher queue will contain pending pipeline jobs ware thread that the machine provides and permanently bind each of different queries that are executed in parallel to accommodate worker to it. Thus, the level of parallelism of a particular query is inter-query parallelism. not controlled by creating or terminating threads, but rather by as- signing them particular tasks of possibly different queries. A task 3.1 Elasticity that is assigned to such a worker thread consists of a pipeline job The fully elastic parallelism, which is achieved by dispatching and a particular morsel on which the pipeline has to be executed. jobs “a morsel at a time”, allows for intelligent scheduling of these Preemption of a task occurs at morsel boundaries – thereby elimi- inter-query parallel pipeline jobs depending on a quality of service nating potentially costly interrupt mechanisms. We experimentally model. It enables to gracefully decrease the degree of parallelism determined that a morsel size of about 100,000 tuples yields good of, say a long-running query Ql at any stage of processing in order tradeoff between instant elasticity adjustment, load balancing and to prioritize a possibly more important interactive query Q+ . Once low maintenance overhead. the higher prioritized query Q+ is finished, the pendulum swings There are three main goals for assigning tasks to threads that run back to the long running query by dispatching all or most cores to on particular cores: tasks of the long running query Ql . In Section 5.4 we demonstrate this dynamic elasticity experimentally. In our current implemen- 1. Preserving (NUMA-)locality by assigning data morsels to tation all queries have the same priority, so threads are distributed cores on which the morsels are allocated 1 This assumes that the goal is to minimize the response time of a 2. Full elasticity concerning the level of parallelism of a partic- particular query. Of course, an idle thread could start working on ular query another query otherwise. 746

5 .equally over all active queries. A priority-based scheduling com- ponent is under development but beyond the scope of this paper. 0.8 For each pipeline job the dispatcher maintains lists of pending 0.6 morsels on which the pipeline job has still to be executed. For each time [s] core a separate list exists to ensure that a work request of, say, Core 0.4 0 returns a morsel that is allocated on the same socket as Core 0. This is indicated by different colors in our architectural sketch. As 0.2 soon as Core 0 finishes processing the assigned morsel, it requests a new task, which may or may not stem from the same pipeline job. 0.0 This depends on the prioritization of the different pipeline jobs that 100 1K 10K 100K 1M 10M originate from different queries being executed. If a high-priority morsel size query enters the system it may lead to a decreased parallelism de- gree for the current query. Morsel-wise processing allows to re- Figure 6: Effect of morsel size on query execution assign cores to different pipeline jobs without any drastic interrupt mechanism. and could therefore be executed in parallel. However, the useful- ness of this form of parallelism is limited. The number of indepen- 3.2 Implementation Overview dent pipelines is usually much smaller than the number of cores, For illustration purposes we showed a (long) linked list of morsels and the amount of work in each pipeline generally differs. Fur- for each core in Figure 5. In reality (i.e., in our implementation) we thermore, bushy parallelism can decrease performance by reducing maintain storage area boundaries for each core/socket and segment cache locality. Therefore, we currently avoid to execute multiple these large storage areas into morsels on demand; that is, when pipelines from one query in parallel; in our example, we first exe- a core requests a task from the dispatcher the next morsel of the cute pipeline T , and only after T is finished, the job for pipeline S pipeline argument’s storage area on the particular socket is “cut is added to the list of pipeline jobs. out”. Furthermore, in Figure 5 the Dispatcher appears like a sep- Besides elasticity, morsel-driven processing also allows for a arate thread. This, however, would incur two disadvantages: (1) simple and elegant implementation of query canceling. A user may the dispatcher itself would need a core to run on or might pre- have aborted her query request, an exception happened in a query empt query evaluation threads and (2) it could become a source (e.g., a numeric overflow), or the system is running out of RAM. of contention, in particular if the morsel size was configured quite If any of these events happen, the involved query is marked in the small. Therefore, the dispatcher is implemented as a lock-free data dispatcher. The marker is checked whenever a morsel of that query structure only. The dispatcher’s code is then executed by the work- is finished, therefore, very soon all worker threads will stop work- requesting query evaluation thread itself. Thus, the dispatcher is au- ing on this query. In contrast to forcing the operating system to tomatically executed on the (otherwise unused) core of this worker kill threads, this approach allows each thread to clean up (e.g., free thread. Relying on lock-free data structures (i.e., the pipeline job allocated memory). queue as well as the associated morsel queues) reduces contention even if multiple query evaluation threads request new tasks at the 3.3 Morsel Size same time. Analogously, the QEPobject that triggers the progress In contrast to systems like Vectorwise [9] and IBM’s BLU [31], of a particular query by observing data dependencies (e.g., build- which use vectors/strides to pass data between operators, there is no ing hash tables before executing the probe pipeline) is implemented performance penalty if a morsel does not fit into cache. Morsels are as a passive state machine. The code is invoked by the dispatcher used to break a large task into small, constant-sized work units to whenever a pipeline job is fully executed as observed by not being facilitate work-stealing and preemption. Consequently, the morsel able to find a new morsel upon a work request. Again, this state size is not very critical for performance, it only needs to be large machine is executed on the otherwise unused core of the worker enough to amortize scheduling overhead while providing good re- thread that originally requested a new task from the dispatcher. sponse times. To show the effect of morsel size on query per- Besides the ability to assign a core to a different query at any formance we measured the performance for the query select time – called elasticity – the morsel-wise processing also guaran- min(a) from R using 64 threads on a Nehalem EX system, tees load balancing and skew resistance. All threads working on the which is described in Section 5. This query is very simple, so it same pipeline job run to completion in a “photo finish”: they are stresses the work-stealing data structure as much as possible. Fig- guaranteed to reach the finish line within the time period it takes ure 6 shows that the morsel size should be set to the smallest pos- to process a single morsel. If, for some reason, a core finishes sible value where the overhead is negligible, in this case to a value processing all morsels on its particular socket, the dispatcher will above 10,000. The optimal setting depends on the hardware, but “steal work” from another core, i.e., it will assign morsels on a dif- can easily be determined experimentally. ferent socket. On some NUMA systems, not all sockets are directly On many-core systems, any shared data structure, even if lock- connected with each other; here it pays off to steal from closer sock- free, can eventually become a bottleneck. In the case of our work- ets first. Under normal circumstances, work-stealing from remote stealing data structure, however, there are a number of aspects that sockets happens very infrequently; nevertheless it is necessary to prevent it from becoming a scalability problem. First, in our imple- avoid idle threads. And the writing into temporary storage will be mentation the total work is initially split between all threads, such done into NUMA local storage areas anyway (that is, a red morsel that each thread temporarily owns a local range. Because we cache turns blue if it was processed by a blue core in the process of steal- line align each range, conflicts at the cache line level are unlikely. ing work from the core(s) on the red socket). Only when this local range is exhausted, a thread tries to steal work So far, we have discussed intra-pipeline parallelism. Our par- from another range. Second, if more than one query is executed allelization scheme can also support bushy parallelism, e.g., the concurrently, the pressure on the data structure is further reduced. pipelines “filtering and building the hash table of T ” and “filtering Finally, it is always possible to increase the morsel size. This re- and building the hash table of S” of our example are independent sults in fewer accesses to the work-stealing data structure. In the 747

6 .worst case a too large morsel size results in underutilized threads 16 bit tag for early filtering hashTable but does not affect throughput of the system if enough concurrent 00000100 d queries are being executed. 48 bit pointer 4. PARALLEL OPERATOR DETAILS 10000010 e In order to be able to completely parallelize each pipeline, each operator must be capable to accept tuples in parallel (e.g., by syn- chronizing shared data structures) and, for operators that start a new f pipeline, to produce tuples in parallel. In this section we discuss the 1 insert(entry) { implementation of the most important parallel operators. 2 // determine slot in hash table 3 slot = entry->hash >> hashTableShift 4.1 Hash Join 4 do { As discussed in Section 2 and shown in Figure 3, the hash table 5 old = hashTable[slot] 6 // set next to old entry without tag construction of our hash join consists of two phases. In the first 7 entry->next = removeTag(old) phase, the build input tuples are materialized into a thread-local 8 // add old and new tag storage area2 ; this requires no synchronization. Once all input tu- 9 new = entry | (old&tagMask) | tag(entry->hash) 10 // try to set new value, repeat on failure ples have been consumed that way, an empty hash table is created 11 } while (!CAS(hashTable[slot], old, new)) with the perfect size, because the input size is now known pre- 12 } cisely. This is much more efficient than dynamically growing hash tables, which incur a high overhead in a parallel setting. In the sec- Figure 7: Lock-free insertion into tagged hash table ond phase of the parallel build phase each thread scans its storage area and inserts pointers to its tuples using the atomic compare- and-swap instruction. The details are explained in Section 4.2. 4.2 Lock-Free Tagged Hash Table Outer join is a minor variation of the described algorithm. In The hash table that we use for the hash join operator has an each tuple a marker is additionally allocated that indicates if this early-filtering optimization, which improves performance of selec- tuple had a match. In the probe phase the marker is set indicating tive joins, which are quite common. The key idea is to tag a hash that a match occurred. Before setting the marker it is advantageous bucket list with a small filter into which all elements of that partic- to first check that the marker is not yet set, to avoid unnecessary ular list are “hashed” to set their 1-bit. For selective probes, i.e., contention. Semi and anti joins are implemented similarly. probes that would not find a match by traversing the list, the filter Using a number of single-operation benchmarks, Balkesen et al. usually reduces the number of cache misses to 1 by skipping the showed that a highly-optimized radix join can achieve higher per- list traversal after checking the tag. As shown in Figure 7 (top), we formance than a single-table join [5]. However, in comparison with encode a tag directly into 16 bits of each pointer in the hash table. radix join our single-table hash join This saves space and, more importantly, allows to update both the • is fully pipelined for the larger input relation, thus uses less pointer and the tag using a single atomic compare-and-swap oper- space as the probe input can be processed in place, ation. For low-cost synchronization we exploit the fact that in a join the • is a “good team player” meaning that multiple small (dimen- hash table is insert-only and lookups occur only after all inserts are sion) tables can be joined as a team by a probe pipeline of completed. Figure 7 (bottom) shows the pseudo code for inserting a the large (fact) table through all these dimension hash tables, new entry into the hash table. In line 11, the pointer to the new ele- • is very efficient if the two input cardinalities differ strongly, ment (e.g, “f” in the picture) is set using compare-and-swap (CAS). as is very often the case in practice, This pointer is augmented by the new tag, which is computed from • can benefit from skewed key distributions3 [7], the old and the new tag (line 9). If the CAS failed (because another • is insensitive to tuple size, and insert occurred simultaneously), the process is repeated. Our tagging technique has a number of advantages in compari- • has no hardware-specific parameters. son to Bloom filters, which can be used similarly and are, for ex- Because of these practical advantages, a single-table hash join is ample, used in Vectorwise [8], SQL Server [21], and BLU [31]. often preferable to radix join in complex query processing. For ex- First, a Bloom filter is an additional data structure that incurs mul- ample, in the TPC-H benchmark, 97.4% of all joined tuples arrive tiple reads. And for large tables, the Bloom filter may not fit into at the probe side, and therefore the hash table often fits into cache. cache (or only relatively slow last-level cache), as the Bloom fil- This effect is even more pronounced with the Star Schema Bench- ter size must be proportional to the hash table size to be effective. mark where 99.5% of the joined tuples arrive at the probe side. Therefore, the overhead can be quite high, although Bloom filters Therefore, we concentrated on a single-table hash join which has can certainly be a very good optimization due to their small size. the advantage of having no hardware-specific parameters and not In our approach no unnecessary memory accesses are performed, relying on query optimizer estimates while providing very good (if only a small number of cheap bitwise operations. Therefore, hash the table fits into cache) or at least decent (if the table is larger than tagging has very low overhead and can always be used, without re- cache) performance. We left the radix join implementation, which lying on the query optimizer to estimate selectivities. Besides join, is beneficial in some scenarios due to higher locality, for future en- tagging is also very beneficial during aggregation when most keys hancement of our query engine. are unique. 2 We also reserve space for a next pointer within each tuple for han- The hash table array only stores pointers, and not the tuples dling hash collisions. themselves, i.e., we do not use open addressing. There are a num- 3 One example that occurs in TPC-H is positional skew, i.e., in a ber of reasons for this: Since the tuples are usually much larger than 1:n join all join partners occur in close proximity which improves pointers, the hash table can be sized quite generously to at least cache locality. twice the size of the input. This reduces the number of collisions 748

7 . spill when ht becomes full HT without wasting too much space. Furthermore, chaining allows for K V K V tuples of variable size, which is not possible with open address- 8 9 ht Partition 0 12 ... morsel ing. Finally, due to our filter, probe misses are in fact faster with 3 2 K V (12,7) (8,3) group 13 7 8 9 (41,4) (13,7) 8 ... chaining than with open addressing. Result ptn 0 3 8 13 7 gro We use large virtual memory pages (2MB) both for the hash ta- 3 4 up 4 ... morsel 10 7 3 10 ...Partition 3 ... ble and the tuple storage areas. This has several positive effects: up gro 33 22 ht Partition 0 gro HT up The number of TLB misses is reduced, the page table is guaranteed 4 17 group K V (8,9) (4,30) 33 4 K V to fit into L1 cache, and scalability problems from too many kernel next red 4 17 morsel 8 7 (13,14) (33,5) page faults during the build phase are avoided. We allocate the hash 33 22 gro 13 ... ... ... 10 7 up table using the Unix mmap system call, if available. Modern oper- Result ptn 1 3 4 ...Partition 3 ... 33 ... ating systems do not eagerly allocate the memory immediately, but only when a particular page is first written to. This has two posi- Phase 1: local pre-aggregation tive effects. First, there is no need to manually initialize the hash table to zero in an additional phase. Second, the table is adaptively Phase 2: aggregate partition-wise distributed over the NUMA nodes, because the pages will be lo- cated on the same NUMA node as the thread that has first written Figure 8: Parallel aggregation to that page. If many threads build the hash table, it will be pseudo- randomly interleaved over all nodes. In case only threads from ce a single NUMA node construct the hash table, it will be located sort in-pla sort sort on that node – which is exactly as desired. Thus, relying on the global 1/3 global 2/3 operating system automatically takes into consideration that often multiple independent queries are being executed concurrently. 3 4.3 NUMA-Aware Table Partitioning local 1/3 a l 2/ Compute global separators loc In order to implement NUMA-local table scans, relations have to be distributed over the memory nodes. The most obvious way from the local separators to do this is round-robin assignment. A better alternative is to par- tition relations using the hash value of some “important” attribute. The benefit is that in a join between two tables that are both par- merge merge merge titioned on the join key (e.g., by the primary key of one and by the foreign key of the other relation), matching tuples usually re- side on the same socket. A typical example (from TPC-H) would be to partition orders and lineitem on the orderkey attribute. Note Figure 9: Parallel merge sort that this is more a performance hint than a hard partitioning: Work stealing or data imbalance can still lead to joins between tuples partitions. After all input data has been partitioned, the partitions from different sockets, but most join pairs will come from the same are exchanged between the threads. socket. The result is that there is less cross-socket communication, The second phase consists of each thread scanning a partition because the relations are co-located for this frequent join. This also and aggregating it into a thread-local hash table. As there are more affects the hash table array, because the same hash function used partitions than worker threads, this process is repeated until all par- for determining the hash partition is also used for the highest bits titions are finished. Whenever a partition has been fully aggregated, of the hash buckets in a hash join. Except for the choice of the its tuples are immediately pushed into the following operator before partitioning key, this scheme is completely transparent, and each processing any other partitions. As a result, the aggregated tuples partition contains approximately the same number of tuples due are likely still in cache and can be processed more efficiently. to the use of hash-based fragmentation. It should be stressed that Note that the aggregation operator is fundamentally different this co-location scheme is beneficial but not decisive for the high from join in that the results are only produced after all the input performance of morsel-driven execution, as NUMA-locality is, in has been read. Since pipelining is not possible anyway, we use either case, guaranteed for table scans, and after the first pipeline partitioning – not a single hash table as in our join operator. that materializes results NUMA locally. 4.5 Sorting 4.4 Grouping/Aggregation In main memory, hash-based algorithms are usually faster than The performance characteristics of the aggregation operator dif- sorting [4]. Therefore, we currently do not use sort-based join fers very much depending on the number of groups (distinct keys). or aggregation, and only sort to implement the order by or top-k If there are few groups, aggregation is very fast because all groups clause. In our parallel sort operator each thread first materializes fit into cache. If, however, there are many groups, many cache and sorts its input locally and in place. In the case of top-k queries, misses happen. Contention from parallel accesses can be a prob- each thread directly maintains a heap of k tuples. lem in both cases (if the key distribution is skewed). To achieve After local sort, the parallel merge phase begins, as shown in Fig- good performance and scalability in all these cases, without rely- ure 9. The difficulty lies in computing separators, so that merges are ing on query optimizer estimates, we use an approach similar to independent and can be executed in parallel without synchroniza- IBM BLU’s aggregation [31]. tion. To do this, each thread first computes local separators by pick- As indicated by Figure 8, our algorithm has two phases. In ing equidistant keys from its sorted run. Then, to handle skewed the first phase, thread-local pre-aggregation efficiently aggregates distribution and similar to the median-of-medians algorithm, the lo- heavy hitters using a thread-local, fixed-sized hash table. When this cal separators of all threads are combined, sorted, and the eventual, small pre-aggregation table becomes full, it is flushed to overflow global separator keys are computed. After determining the global 749

8 . Nehalem EX Sandy Bridge EP In this evaluation we used a classical ad-hoc TPC-H situation. DRAM DRAM DRAM DRAM This means that no hand-tuning of physical storage was used, as 25.6GB/s 51.2GB/s this way the plans used are similar (hash joins everywhere). The Vectorwise results from the TPC web site include this additional tuning, mainly clustered indexes, which allows to execute some socket 0 socket 1 socket 0 socket 1 of the larger joins with merge-join algorithms. Additionally, these 8 cores 8 cores 8 cores 8 cores indexes allow the query optimizer to propagate range restrictions 24MB L3 24MB L3 20MB L3 20MB L3 from one join side to the other [8], which greatly improves perfor- 12.8GB/s 16.0GB/s mance for a small number of queries, but does not affect the query (bidirec�onal) (bidirec�onal) processing itself very much. This tuning also does not improve the scalability of query execution; on average the speedup is below socket 3 socket 2 socket 3 socket 2 10× both with and without tuning. For completeness, we also pro- 8 cores 8 cores 8 cores 8 cores vide results for Vectorwise on Nehalem EX with the settings from 24MB L3 24MB L3 20MB L3 20MB L3 the TPC-H full disclosure report: system geo. mean sum scal. DRAM DRAM DRAM DRAM HyPer 0.45s 15.3s 28.1× Vectorwise 2.84s 93.4s 9.3× Figure 10: NUMA topologies, theoretical bandwidth Vectorwise, full-disclosure settings 1.19s 41.2s 8.4× separator keys, binary (or interpolation) search finds the indexes of In HyPer the data can be updated cheaply in-place. The two them in the data arrays. Using these indexes, the exact layout of the TPC-H refresh streams on scale factor 100 execute in less than 1 output array can be computed. Finally, the runs can be merged into second. This is in contrast to heavily read-optimized systems (e.g., the output array without any synchronization. [10]), where updates are expensive due to heavy indexing and re- ordering. Our system transparently distributes the input relations over all available NUMA sockets by partitioning each relation us- 5. EVALUATION ing the first attribute of the primary key into 64 partitions. The We integrated our parallel query evaluation framework into HyPer, execution times include memory allocation and deallocation (from a main-memory column database system that supports SQL-92 and the operating system) for intermediate results, hash tables, etc. has very good single-threaded performance, but, so far, did not use intra-query parallelism. In this evaluation we focus on ad hoc de- cision support queries, and, except for declaring primary keys, do 5.2 TPC-H not enable any additional index structures. Therefore, our results Figure 11 compares the scalability of HyPer with Vectorwise on mainly measure the performance and scalability of the table scan, the Nehalem system; both DBMSs are normalized by the single- aggregation, and join (including outer, semi, anti join) operators. threaded execution time of HyPer. Note that up to 32 threads, HyPer supports both row and column-wise storage; we used the “real” cores are used, the rest are “HyperThreads” (simultaneous column format in all experiments. multithreading). For most queries, HyPer reaches a speedup close to 30. In some cases a speedup close to or above 40 is reached due 5.1 Experimental Setup to simultaneous multithreading. Although Vectorwise has similar We used two different hardware platforms – both running Linux. single-threaded performance as HyPer, its overall performance is Unless indicated otherwise we use a 4-socket Nehalem EX (Intel severely limited by its low speedup, which is often less than 10. Xeon X7560 at 2.3GHz). Additionally, some experiments are per- One problem is load balancing: in the – trivially parallelizable – formed on a 4-socket Sandy Bridge EP (Intel Xeon E5-4650L at scan-only query 6 the slowest thread often finishes work 50% be- 2.6GHz-3.1GHz). Such systems are particularly suitable for main- fore the last. While in real-world scenarios it is usually data skew memory database systems, as they support terabytes of RAM at that challenges load balancing, this is not the case in the fully uni- reasonable cost. Although both systems have 32 cores, 64 hard- form TPC-H. These issues are related to the use of the Volcano ware threads, and almost the same amount of cache, their NUMA model for parallelizing queries in Vectorwise [3]. This approach, topology is quite different. As Figure 10 shows, each of the Sandy which is commonly followed (e.g., in Oracle and SQL Server), as Bridge CPUs has twice the theoretical per-node memory bandwidth it allows to implement parallelism without affecting existing query but is only connected to two other sockets. Consequently, some operators, bakes the parallelism into the plan at planning time by memory accesses (e.g., from socket 0 to socket 2) require two hops instantiating a set of query plans on separate plans and connecting instead of one; this increases latency and reduces memory band- then using “exchange” operators [12]. We point out that fixed work width because of cross traffic [23]. Note that the upcoming 4- division combined with lack of NUMA-awareness can lead to sig- socket Ivy Bridge platform will come in two versions, Ivy Bridge nificant performance differences between threads (Vectorwise up to EX which is fully connected like Nehalem EX, and Ivy Bridge EP version 3 is not NUMA-aware, as confirmed by our experiments in with only a single interconnect per node like Sandy Bridge EP. Section 5.3). As our main competitor we chose the official single-server Figure 11 also shows scalability results where we disabled some TPC-H leader Vectorwise. We also measured the performance of important features of our query engine. Performance is signifi- the open source row store PostgreSQL and a column store that is cantly lower when we disable explicit NUMA-awareness and rely integrated into one of the major commercial database systems. On on the operating system instead (cf. “HyPer (not NUMA aware)”). TPC-H, in comparison with HyPer, PostgreSQL was slower by a A further performance penalty can be observed, if we additionally factor of 30 on average, the commercial column store by a factor of disable adaptive morsel-wise processing and the performance en- 10. We therefore concentrate on Vectorwise (version 2.5) in further hancements introduced in this paper like hash tagging. This gives experiments, as it was much faster than the other systems. an impression of the effects of the individual techniques. But note 750

9 . 1 2 3 4 5 6 7 8 40 30 20 10 0 9 10 11 12 13 14 15 16 speedup over HyPer 40 30 20 10 0 17 18 19 20 21 22 System 40 HyPer (full-fledged) 30 HyPer (not NUMA aware) 20 HyPer (non-adaptive) 10 0 Vectorwise 1 16 32 48 64 1 16 32 48 64 1 16 32 48 64 1 16 32 48 64 1 16 32 48 64 1 16 32 48 64 threads Figure 11: TPC-H scalability on Nehalem EX (cores 1-32 are “real”, cores 33-64 are “virtual”) HyPer [%] Vectorwise [%] 5.3 NUMA Awareness TPC-H time scal. rd. wr. remote time scal. rd. wr. remote # [s] [×] [GB/s] QPI [s] [×] [GB/s] QPI Table 1 shows memory bandwidth and QPI statistics4 for each 1 0.28 32.4 82.6 0.2 1 40 1.13 30.2 12.5 0.5 74 7 of the 22 TPC-H queries. Query 1, which aggregates the largest 2 0.08 22.3 25.1 0.5 15 17 0.63 4.6 8.7 3.6 55 6 relation, for example, reads 82.6GB/s getting close to the theoreti- 3 0.66 24.7 48.1 4.4 25 34 3.83 7.3 13.5 4.6 76 9 cal bandwidth maximum of 100GB/s. The “remote” column in the 4 0.38 21.6 45.8 2.5 15 32 2.73 9.1 17.5 6.5 68 11 table shows the percentage of data being accessed though the in- 5 0.97 21.3 36.8 5.0 29 30 4.52 7.0 27.8 13.1 80 24 6 0.17 27.5 80.0 0.1 4 43 0.48 17.8 21.5 0.5 75 10 terconnects (remotely), and therefore measures the locality of each 7 0.53 32.4 43.2 4.2 39 38 3.75 8.1 19.5 7.9 70 14 query. Because of NUMA-aware processing, most data is accessed 8 0.35 31.2 34.9 2.4 15 24 4.46 7.7 10.9 6.7 39 7 locally, which results in lower latency and higher bandwidth. From 9 2.14 32.0 34.3 5.5 48 32 11.42 7.9 18.4 7.7 63 10 the “QPI” column5 , which shows the saturation of the most heavily 10 0.60 20.0 26.7 5.2 37 24 6.46 5.7 12.1 5.7 55 10 used QPI link, one can conclude that the bandwidth of the QPI links 11 0.09 37.1 21.8 2.5 25 16 0.67 3.9 6.0 2.1 57 3 is sufficient on this system. The table also shows that Vectorwise is 12 0.22 42.0 64.5 1.7 5 34 6.65 6.9 12.3 4.7 61 9 13 1.95 40.0 21.8 10.3 54 25 6.23 11.4 46.6 13.3 74 37 not NUMA optimized: most queries have high percentages of re- 14 0.19 24.8 43.0 6.6 29 34 2.42 7.3 13.7 4.7 60 8 motely accessed memory. For instance, the 75% remote accesses in 15 0.44 19.8 23.5 3.5 34 21 1.63 7.2 16.8 6.0 62 10 query 1 shows that its buffer manager is not NUMA-aware. How- 16 0.78 17.3 14.3 2.7 62 16 1.64 8.8 24.9 8.4 53 12 ever, the QPI links are utilized fairly evenly, as the database rela- 17 0.44 30.5 19.1 0.5 13 13 0.84 15.0 16.2 2.9 69 7 tions seem to be spread over all 4 NUMA nodes. This prevents a 18 2.78 24.0 24.5 12.5 40 25 14.94 6.5 26.3 8.7 66 13 single memory controller and the QPI links to it from becoming the 19 0.88 29.5 42.5 3.9 17 27 2.87 8.8 7.4 1.4 79 5 20 0.18 33.4 45.1 0.9 5 23 1.94 9.2 12.6 1.2 74 6 bottleneck. 21 0.91 28.0 40.7 4.1 16 29 12.00 9.1 18.2 6.1 67 9 Most experiments so far used our NUMA-aware storage lay- 22 0.30 25.7 35.5 1.3 75 38 3.14 4.3 7.0 2.4 66 4 out, NUMA-local scans, the NUMA-aware partitioning, which re- duces remote accesses in joins, and the fact that all operators try Table 1: TPC-H (scale factor 100) statistics on Nehalem EX to keep data NUMA-local whenever possible. To show the overall 4 These statistics were obtained using the Open Source tool “Intel Performance Counter Monitor” (www.intel.com/ that we still use highly tuned operator implementations that try to software/pcm). The “rd.” (read), “wr.” (write), and “remote” maximize locality. values are aggregated over all sockets. The “QPI” column shows Table 1 and Table 2 allow to compare the TPC-H performance of the utilization of the most-utilized QPI link (though with NUMA- the Nehalem and Sandy Bridge systems. The overall performance awareness the utilization of the links is very similar). Unfortu- nately, these statistics are not exposed on Sandy Bridge EP. is similar on both systems, because the missing interconnect links 5 The QPI links are used both for sending the actual data, as well on Sandy Bridge EP, which result in slightly lower scalability, are as for broadcasting cache coherency requests, which is unavoid- compensated by its higher clock rate. Notice that all queries com- able and happens even for local accesses. Query 1, for example, plete within 3 seconds – on a 100GB data set using ad hoc hash reads 82.6GB/s, 99% of it locally, but still uses 40% of the QPI joins and without using any index structures. link bandwidth. 751

10 . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 time [s] 0.21 0.10 0.63 0.30 0.84 0.14 0.56 0.29 2.44 0.61 0.10 0.33 2.32 0.33 0.33 0.81 0.40 1.66 0.68 0.18 0.74 0.47 scal. [×] 39.4 17.8 18.6 26.9 28.0 42.8 25.3 33.3 21.5 21.0 27.4 41.8 16.5 15.6 20.5 11.0 34.0 29.1 29.6 33.7 26.4 8.4 Table 2: TPC-H (scale factor 100) performance on Sandy Bridge EP threads per query stream 64 32 16 8 4 2 1 worker 0 worker 1 throughput [queries/s] 1.5 worker 2 1.0 worker 3 q13 start q14 start q14 finish �me 0.5 Figure 13: Illustration of morsel-wise processing and elasticity 0.0 SSB time scal. read write remote QPI 1 2 4 8 16 32 64 # [s] [×] [GB/s] [GB/s] [%] [%] number of query streams 1.1 0.10 33.0 35.8 0.4 18 29 Figure 12: Intra- vs. inter-query parallelism with 64 threads 1.2 0.04 41.7 85.6 0.1 1 44 1.3 0.04 42.6 85.6 0.1 1 44 2.1 0.11 44.2 25.6 0.7 13 17 performance benefit of NUMA-awareness we also experimented 2.2 0.15 45.1 37.2 0.1 2 19 with plausible alternatives: “OS default”, where the placement is 2.3 0.06 36.3 43.8 0.1 3 25 performed by the operating system6 , and “interleaved”, where all 3.1 0.29 30.7 24.8 1.0 37 21 memory is allocated round robin over all nodes. We report the geo- 3.2 0.09 38.3 37.3 0.4 7 22 3.3 0.06 40.7 51.0 0.1 2 27 metric mean and maximum speedup of our NUMA-aware approach 3.4 0.06 40.5 51.9 0.1 2 28 on TPC-H: 4.1 0.26 36.5 43.4 0.3 34 34 Nehalem EX Sandy Bridge EP 4.2 0.23 35.1 43.3 0.3 28 33 geo. mean max geo. mean max 4.3 0.12 44.2 39.1 0.3 5 22 OS default 1.57× 4.95× 2.40× 5.81× interleaved 1.07× 1.24× 1.58× 5.01× Table 3: Star Schema Benchmark (scale 50) on Nehalem EX Clearly, the default placement of the operating system is sub- optimal, as the memory controller of one NUMA node and the even if few streams (but many cores per stream) are used. This al- QPI links to it become the bottleneck. These results also show that lows to minimize response time for high priority queries without on Nehalem EX, simply interleaving the memory is a reasonable, sacrificing too much throughput. though not optimal strategy, whereas on Sandy Bridge EP NUMA- Figure 13 illustrates morsel-wise processing by showing an an- awareness is much more important for good performance. The rea- notated execution trace from our parallel profiler. Each color repre- son is that these two systems are quite different in their NUMA sents one pipeline stage and each block is one morsel. For graphical behavior, as can be seen from a micro benchmark that compares reasons we used only 4 threads in this experiment. We started by NUMA-local accesses with a random mix of 25% local and 75% executing TPC-H query 13, which received 4 threads; after some remote (including 25% two-hop accesses on Sandy Bridge EP) ac- time, TPC-H query 14 was started. As the trace shows, once the cesses: current morsels of worker thread 2 and 3 are finished, these threads bandwidth [GB/s] latency [ns] switch to query 14 until it is finished, and finally continue working local mix local mix on query 13. This experiment shows that it is possible to dynam- Nehalem EX 93 60 161 186 ically reassign worker threads to other queries, i.e., that our paral- Sandy Bridge EP 121 41 101 257 lelization scheme is fully elastic. On Sandy Bridge EP only a small fraction of the theoretical As mentioned in the introduction, the Volcano approach typically memory bandwidth can be reached unless most accesses are local, assigns work to threads statically. To compare with this approach, and the latency it 2.5× higher than for local accesses. On Nehalem we emulated it in our morsel-driven scheme by splitting the work EX, in contrast, these effects are much smaller, which explains why into as many chunks as there are threads, i.e., we set the morsel size the positive effect of NUMA-awareness is smaller on this system. to n/t, where n is the input size and t is the number of threads. As The importance of NUMA-awareness clearly depends on the speed long as we only execute a single TPC-H query at a time, this change and number of the cross-socket interconnects. alone does not significantly decrease performance, because the in- put data is uniformly distributed on this workload. However, if we 5.4 Elasticity add some interference from other processes, this picture changes. For example, when we ran the TPC-H queries while another, un- To demonstrate the elasticity of our approach, we performed an related single-threaded process occupied one core, query perfor- experiment where we varied the number parallel query streams. mance dropped by 36.8% with static approach, but only 4.7% with The 64 available hardware threads are distributed uniformly over dynamic morsel assignment. the streams, and each stream executes random permutations of the TPC-H queries. Figure 12 shows that the throughput stays high 5.5 Star Schema Benchmark 6 In practice, the database itself is located on a single NUMA node, Besides TPC-H, we also measured the performance and scalabil- because the data is read from disk by a single thread. Other alloca- ity of our system on the Star Schema Benchmark (SSB) [26], which tions are local to the thread that first wrote to that memory. Thus, mimics data warehousing scenarios. Table 3 shows that our paral- hash tables are distributed randomly over the nodes. lelization framework works very well on this workload, achieving 752

11 .a speedup of over 40 for most queries. The scalability is higher are maintained NUMA-locally across processing steps/pipelines. than on TPC-H, because TPC-H is a much more complex and chal- In addition, the full elasticity w.r.t. the degree of parallelism that lenging workload. TPC-H contains a very diverse set of queries: we propose was not covered. Very similar to Volcano-style paral- queries that only scan a single table, queries with complex joins, lelization, in Oracle the individual operators are largely unaware of queries with simple and with complex aggregations, etc. It is quite parallelism. [6] addresses some problems of this model, in partic- challenging to obtain good performance and scalability on such a ular reliance on query optimizer estimates, by adaptively changing workload, as all operators must be scalable and capable of effi- data distribution decisions during query execution. In an experi- ciently handling very diverse input distributions. All SSB queries, mental study Kiefer et al. [17] showed that NUMA-awareness can in contrast, join a large fact table with multiple smaller dimension improve database performance considerably. Porobic et al. inves- tables where the pipelining capabilities of our hash join algorithm tigated [29] and improved NUMA-placement in OLTP systems by are very beneficial. Most of the data comes from the large fact partitioning the data and internal data structures in a NUMA-aware table, which can be read NUMA-locally (cf. column “remote” in way [28]. Heimel et al. presented a hardware-oblivious approach Figure 3), the hash tables of the dimensions are much smaller than to parallelization that allows operators to be compiled to different the fact table, and the aggregation is quite cheap in comparison with hardware platforms like CPUs or GPUs [15]. In this paper we fo- the rest of the query. cus on classical, query-centric parallelization, i.e., parallelizing in- dividual queries in isolation. Another fruitful approach is to ex- 6. RELATED WORK ploit common work from multiple queries. This operator-centric approach is used by QPipe [14] and SharedDB [11]. This paper is related to three distinct lines of work: papers that The seminal Volcano model [12] forms the basis of most cur- focus on multi-core join or aggregation processing in isolation, full rent query evaluation engines enabling multi-core as well as dis- systems descriptions, and parallel execution frameworks, most no- tributed [13] parallelism. Note that Volcano in a non-parallel tably Volcano. context is also associated with an interpreted iterator execution The radix hash join was originally designed to increase local- paradigm where results are pulled upwards through an operator ity [24]. Kim et al. postulated it for parallel processing based on re- tree, by calling the next() method on each operator, which deliv- peatedly partitioning the input relations [18]. Blanas et al. [7] were ers the next tuple. Such a tuple-at-a-time execution model, while the first to compare the radix join with a simple, single global hash elegant in its implementation, has been shown to introduce sig- table join. Balkesen et al. [5, 4] comprehensively investigated hash- nificant interpretation overhead [25]. With the advent of high- and sort-based join algorithms. Ye et al. evaluated parallel aggrega- performance analytical query engines, systems have been moving tion algorithms on multi-core CPUs [33]. Polychroniou and Ross from this model towards vector or batch-oriented execution, where designed an aggregation algorithm to efficiently aggregate heavy each next() method works on hundreds or thousands of tuples. This hitters (frequent items) [27]. vector-wise execution model appears in Vectorwise [3], but also in A number of papers specifically focus on NUMA. In one of the the batch-mode execution offered by ColumnStore Index tables in first paper that pinpoints the relevance of NUMA-locality, Teubner SQL Server [22] (the Apollo project), as well as in stride-at-a-time and Müller [32] presented a NUMA-aware window-based stream execution in IBM’s BLU engine for DB2 [31]. In HyPer we rely on join. In another early NUMA paper, Albutiu et al. designed a a compiled query evaluation approach as first postulated by Krikel- NUMA-aware parallel sort merge join [1]. Li et al. refined this al- las et al. [19] and later refined by Neumann [25] to obtain the same, gorithm by explicitly scheduling the shuffling of the sorted runs in or even higher raw execution performance. order to avoid cross traffic in the NUMA interconnection network As far as parallelism is concerned, Volcano differentiates be- [23]. However, despite its locality-preserving nature this algorithm tween vertical parallelism, where essentially the pipeline between turned out to be less efficient than hash joins due to the high cost of two operators is transformed into an asynchronous producer/con- sorting [4, 20]. Lang et al. [20] devised a low synchronization over- sumer model, and horizontal parallelism, where one operator is head NUMA-aware hash join, which is similar to our algorithm. It parallelized by partitioning the input data and have each parallel relies on a single latch-free hash table interleaved across all NUMA thread work on one of the partitions. Most systems have imple- nodes into which all threads insert the build input. mented horizontal parallelism, since vertical and bushy parallelism Unfortunately, the conclusiveness of these single-operator stud- are less useful due to their unbalanced nature, as we observed ear- ies for full-fledged query engines is limited because the micro- lier. Examples of such horizontal Volcano parallelism are found in benchmarks used for testing usually have single simple keys (some- e.g., Microsoft SQL Server and Vectorwise [3]. times even containing hash values), and typically use very small While there may be (undisclosed) implementation differences payloads (one column only). Furthermore, each operator was ana- between these systems, morsel-driven execution differentiates it- lyzed in isolation, which ignores how data is passed between opera- self by making parallel query scheduling fine-grained, adaptive at tors and therefore, for example, ignores the different pipelining ca- run-time and NUMA-aware. The parallel query engine described pabilities of the algorithms. In our morsel-driven database system, here relies on chunking of the input data into fine-grained morsels. we have concentrated on (non-materializing) pipelined hash joins, A morsel resides completely in a single NUMA partition. The dis- since in practice, often one of the join sides is much larger than the patcher assigns the processing of a morsel to a thread running on a others. Therefore, teams of pipelined joins are often possible and core of the same socket in order to preserve NUMA locality. The effective. Further, for certain often-traversed large joins (such as morsel-wise processing also facilitates the full elasticity, meaning orders-lineitem in TPC-H), pre-partitioned data storage can achieve that the degree of parallelism can be adjusted at any time, e.g., at NUMA locality on large joins without need for materialization. mid-query processing. As soon as a morsel is finished, the thread The new IBM BLU query engine [31] and Microsoft’s Apollo can be assigned a morsel belonging to the same query pipeline or project [22] are two prominent commercial projects to exploit mod- be assigned a different task of, e.g., another more important query. ern multi-core servers for parallel query processing. IBM BLU pro- This way the dispatcher controls parallelism explicitly as opposed cesses data in “Vectorwise” fashion, a so-called stride at a time. In to the recently proposed approach by Psaroudakis et al. [30] where this respect there is some resemblance to our morsel-wise process- the number of threads is changed based on the core utilization. ing technique. However, there was no indication that the strides 753

12 .7. CONCLUSIONS AND FUTURE WORK [8] P. Boncz, T. Neumann, and O. Erling. TPC-H analyzed: Hidden messages and lessons learned from an influential benchmark. In We presented the morsel-driven query evaluation framework for TPCTC, 2013. parallel query processing. It is targeted at solving the major bot- [9] P. A. Boncz, M. Zukowski, and N. Nes. MonetDB/X100: tlenecks for analytical query performance in the many-core age, Hyper-pipelining query execution. In CIDR, 2005. which are load-balancing, thread synchronization, memory access [10] J. Dees and P. Sanders. Efficient many-core query execution in main locality, and resource elasticity. We demonstrated the good scala- memory column-stores. In ICDE, 2013. bility of this framework in HyPer on the full TPC-H and SSB query [11] G. Giannikis, G. Alonso, and D. Kossmann. SharedDB: Killing one workloads. It is important to highlight, that at the time of this writ- thousand queries with one stone. PVLDB, 5(6), 2012. ing, the presented results are by far the fastest achieved (barring [12] G. Graefe. Encapsulation of parallelism in the Volcano query the hand-written queries on a fully indexed and customized stor- processing system. In SIGMOD, 1990. age scheme [10]7 ) on a single-server architecture. This is not be- [13] G. Graefe. Query evaluation techniques for large databases. ACM Comput. Surv., 25(2), 1993. ing noted to claim a performance record – these are academic and [14] S. Harizopoulos, V. Shkapenyuk, and A. Ailamaki. QPipe: A non-audited results – but rather to underline the effectiveness of the simultaneously pipelined relational query engine. In SIGMOD, 2005. morsel-driven framework in achieving scalability. In particular, one [15] M. Heimel, M. Saecker, H. Pirk, S. Manegold, and V. Markl. needs to keep in mind that it is much easier to provide linear scal- Hardware-oblivious parallelism for in-memory column-stores. ability on computationally slow systems than it is on fast systems PVLDB, 6(9), 2013. such as HyPer. The comparison with the state-of-the-art Vectorwise [16] A. Kemper and T. Neumann. HyPer: A hybrid OLTP&OLAP main system, which uses a classical implementation of Volcano-style memory database system based on virtual memory snapshots. In parallelism [3], shows that beyond 8 cores, in many-core territory, ICDE, 2011. [17] T. Kiefer, B. Schlegel, and W. Lehner. Experimental evaluation of the morsel-driven framework speeds ahead; and we believe that its NUMA effects on database management systems. In BTW, 2013. principles in fine-grained scheduling, full operator parallelization, [18] C. Kim, E. Sedlar, J. Chhugani, T. Kaldewey, A. D. Nguyen, A. D. low-overhead synchronization and NUMA-aware scheduling can Blas, V. W. Lee, N. Satish, and P. Dubey. 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