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+# column-sketch
+
+This is the main repository of the final project of CS511 offered at Spring 2024 semester. 
+
+## Project Goals
+
+The goal of the project is to reproduce [column sketch](https://stratos.seas.harvard.edu/files/stratos/files/sketches.pdf) on top of [apache parquet](https://parquet.apache.org/) format, 
+and to explore how and if it improves performance on the workload of scanning a parquet table with a predicate to evaluate and produce a position list of rows that satisfy the predicate.
+
+### Column Sketch
+
+The essential idea of column sketch is that given an array of data (either numerical or string, but we will prototype numerical data first), we will output a compression map, which is an order-preserving map from the original data to 8-bit integer space.
+
+Assume the original dataset is denoted by $D$, the compression map is a function denoted by $S$, then by order-preserving, we mean the following:
+
+$\forall a, b \in D, a >= b \rightarrow S(a) \geq S(b)$
+
+And the useful corollary is that $S(a) < S(b) \rightarrow a < b$, which will be used for evaluating range queries on the compressed column.
+
+Concretely, we want to implement something like follows:
+
+```Rust
+pub trait CompressionMap<T> {
+  fn compress(value: T) -> u8
+}
+
+fn column_sketch<T>(data: Vec<T>) -> impl CompressionMap<T> {}
+```
+
+### Transform Parquet Tables
+
+We will also implement a tool on top of the core algorithm, which automatically implement the compression for a parquet table, which involves transforming the table and serializing the compression map.
+
+- We store compressed columns directly as additional UINT8 columns in the parquet.
+- As a first step, we will serialize the compression map into a separate file so that we need not change parquet internals. It is also possible to store the serialized compression map directly as a distinct parquet page.