Name
Abstract | ||
|---|---|---|
Key-Value Stores (KVS) are becoming increasingly popular because they scale up and down elastically, sustain high throughputs for get/put workloads and have low latencies. KVS owe these advantages to their simplicity. This simplicity, however, comes at a cost: It is expensive to process complex, analytical queries on top of a KVS because today's generation of KVS does not support an efficient way to scan the data. The problem is that there are conflicting goals when designing a KVS for analytical queries and for simple get/put workloads: Analytical queries require high locality and a compact representation of data whereas elastic get/put workloads require sparse indexes. This paper shows that it is possible to have it all, with reasonable compromises. We studied the KVS design space and built TellStore,a distributed KVS, that performs almost as well as state-of-the-art KVS for get/put workloads and orders of magnitude better for analytical and mixed workloads. This paper presents the results of comprehensive experiments with an extended version of the YCSB benchmark and a workload from the telecommunication industry. |
Year | Venue | Field |
|---|---|---|
2017 | PROCEEDINGS OF THE VLDB ENDOWMENT | Design space,Data mining,Locality,Workload,Computer science,Database |
DocType | Volume | Issue |
Journal | 10 | 11 |
ISSN | Citations | PageRank |
2150-8097 | 4 | 0.38 |
References | Authors | |
30 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Markus Pilman | 1 | 39 | 2.79 |
| Kevin Bocksrocker | 2 | 4 | 0.38 |
| Lucas Braun | 3 | 23 | 2.76 |
| Renato Marroquin | 4 | 10 | 3.56 |
| Donald Kossmann | 5 | 6220 | 603.55 |