In order to solve the index calculation problems of high complexity, strong completeness and low efficiency in the filed of financial foreign exchange market monitoring, a novel distributed computing framework for foreign exchange market monitoring based on Spark big data structure was proposed. Firstly, the business characteristics and existing technology framework for foreign exchange market monitoring were analyzed and summarized. Secondly, the foreign exchange business features of single-market multi-indicator and multi-market multi-indicator were considered. Finally, based on Spark's Directed Acyclic Graph (DAG) job scheduling mechanism and resource scheduling pool isolation mechanism of YARN (Yet Another Recourse Negotiator), the Market-level DAG (M-DAG) model and the market-level resource allocation strategy named M-YARN (Market-level YARN) model were proposed, respectively. The experimental results show that, the performance of the proposed distributed computing framework for foreign exchange market monitoring improves the performance by more than 80% compared to the traditional technology framework, and can effectively guarantee the completeness, accuracy and timeliness of foreign exchange market monitoring indicator calculation under the background of big data.