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Many application domains can benefit from hybrid transaction/analytical processing (HTAP) by executing queries on real-time datasets produced by concurrent transactions. However, with the increasingly speedy transactions and queries thanks to large memory and fast interconnect, commodity HTAP systems have to make a tradeoff between data freshness and performance degradation. Fortunately, we observe that the backups for high availability in modern distributed OLTP systems can be retrofitted to bridge the analytical queries and transactions in HTAP workloads. In this paper, we present Vegito , a distributed in-memory HTAP system that embraces freshness and performance with the following three techniques: (1) a lightweight gossip-style scheme to apply logs on backups consistently; (2) a block-based design for multi-version columnar backups; (3) a two-phase concurrent updating mechanism for the tree-based index of backups. They collectively make the backup fresh, columnar, and fault-tolerant, even facing millions of concurrent transactions per second. Evaluations show that Vegito can perform 1.9 million TPC-C NewOrder transactions and 24 TPC-H-equivalent queries per second simultaneously, which retain the excellent performance of specialized OLTP and OLAP counterparts (e.g., DrTM+H and MonetDB). These results outperform state-of-the-art HTAP systems by several orders of magnitude on transactional performance, while just incurring little performance slowdown (5% over pure OLTP workloads) and still enjoying data freshness for analytical queries (less than 20 ms of maximum delay) in the failure-free case. Further, Vegito can recover from cascading machine failures by using the columnar backup in less than 60 ms.