Data Storage Optimization: Parquet Columnar Storage in Action

When the proxy IP hit the big data storage, this trick to help you save 80% hard disk space

Dry proxy IP this line of brothers understand, every day to deal with the massive request logs can be piled up into a mountain. Last week, an old customer complained that they collected IP quality data to the server hard disk burst, asked me if there is any black technology. Today, we will give you a practical skills - Parquet columnar storage to play around with data compression, with our ipipgo proxy service, the custody of your storage costs directly cut.

Why is your log file getting bigger the more you store it?

Traditional log storage is like stuffing clothes into a suitcase, CSV format records have to repeat each field to store. For example, 1 million proxy IP detection records, "carrier" this field may be mobile, Unicom, Telecom three values, but CSV will be honestly stored 1 million times. This time the advantages of columnar storage came out - the same data is stored only once, but also by column compression.

Here's the point:

ipipgo's dynamic IP pool generates ten million requests per day, and after storing them in Parquet format, the file size drops from 230G to 37G. Especially for IP belonging and AS number, which have high repetition rate, the compression effect is comparable to vacuum packing.

Hands-on configuration of proxy datastores

Here's a real case configuration (note the substitution of your own parameters):

Remember to add a converter to the write session when fetching data with ipipgo's API. python parties can do this:

 Pretend it's a block of code here
import pyarrow.parquet as pq
ip_data = get_ipipgo_apidata() call ipipgo interface
table = pa.Table.from_pandas(ip_data)
pq.write_table(table, 'ip_logs.parquet'.
              compression='snappy',
              version='2.6')

Three Efficiency Enhancement Techniques

1. Dynamic partitioning has to be careful
Double-layer partitioning by "date/IP location", so that irrelevant partitions can be skipped directly when querying. For example, if you check the abnormal IP in Shanghai, the system will automatically filter the data blocks of other regions.

2. Column cuts to be played 6
Read only the columns you need when querying. You want to count the percentage of IP of mobile operators? The system will only scan the data file in the "Operator" column.

3. Separation of hot and cold data
The hot data of the last three days is stored on SSD, and the historical data is transferred to a mechanical disk. ipipgo users have tested that the query response time is reduced from 8 seconds to 1.2 seconds.

Frequently Asked Questions QA

Q: Is Parquet suitable for storing real-time data?
A: It is recommended to do minute-level micro-batch processing with ipipgo's real-time interface to ensure data freshness without affecting storage efficiency.

Q: How to choose the compression algorithm?
A: GZIP compression rate is high but CPU consuming, prioritize SNAPPY. If you are storing historical archive data, you can consider ZSTD.

Q: How to migrate existing CSV data?
A: Batch conversion with Spark or Pandas, remember to clean up the dirty data first. ipipgo's technical documentation has ready-made migration scripts.

Saving is earning.

Since I put my clients on this program, they cut their server renewal fee in half. Now with ipipgo's high-quality proxy pool, with the columnar storage program, the average daily processing 200 million requests without pressure. Some brothers may ask: so the query will not become slow? Let's put it this way, the last time their CTO looked at the second response report, almost thought that the wrong database.

Finally focus: choose the right proxy service provider is the foundation, ipipgo's high purity IP resources coupled with reasonable data storage solutions, in order to let the big data project run steady and fast. Storage optimization is like changing tires for a race car, don't wait until you get a flat tire before you remember to maintain it.