This paper proposes HRAC, a novel compressor targeting high-entropy and highresolution astronomical data of both integer and floating-point types. HRAC leverages the distinct characteristics of high-frequency astronomical data across different dimensions: it reads the data along the dimension with the lowest variability and partitions the stream into blocks. For each block, HRAC calculates the mean value of the data after excluding the maximum and minimum, then applies differential coding using this mean against all data within the block to generate a residual sequence. This residual sequence is encoded using a prefix code that combines ideas from ExpGolomb and Elias gamma coding. When compressing floating-point data, the local smoothness assumption crucial for differential prediction is violated if two close values straddle zero. HRAC addresses this by selectively moving the sign bit after the exponent bits. For decompression, several parameters are required per block. Unlike the conventional approach of storing parameters in each block header, HRAC computes the optimal parameters from the previous block and reuses them for the next, eliminating their storage overhead. Experiments on multiple datasets demonstrate that HRAC delivers superior overall performance compared with other compressors, as shown in Fig. 1.

The shattered and overlapped Level-0 data organization is the primary cause of write stall and read amplification problems in LSM-Tree-based Key-Value (KV) stores: (1) Level-0 to Level-1 compaction involves a large amount of data which induces write stalls, and (2) A point lookup needs to access multiple files in Level-0 which leads to significant read amplification. To address the problem, we propose BL-Tree by replacing the shattered Level-0 in LSM-Tree with a B+-Tree in byte-addressable Persistent Memory (PM). The sorted B+-Tree of Level-0 can accelerate the point lookup speed and reduce read/write amplification. BL-Tree further conducts the locality-aware and parallel compaction from the B+-Tree in PM (Level-0) to the lower levels of LSM-Tree in SSDs by only moving cold data downward, thus alleviating the write stalls and reducing the read/write amplification simultaneously. The extensive experiments on the prototype of BL- Tree show that it definitely avoids the write stalls and significantly reduces the read/write amplification. As a result, BL-Tree reduces the P99 tail latency by 65.2 × than LevelDB-PM and speeds up the throughput by more than 2 × under workloads with spatial locality than other KV stores.