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I assume it's INR 22000 pm. I also assume that you are single(most software engineers, fresher's get this salary so).I can not exactly say how to live in 22000,I will explain my story.I earn 15000 per month. According to me you can lead a better life than mine. This is how I live.1. My rented house is in Sampangiram Nagar, I work in UB city. I stay with my friends. Rent 6000/4. No bus pass, walkable distance. Saving in bus pass. Money spent 1500.2.I have education loan, I pay an EMI of 3150. Remaining money 10350.3. We cook, though I'm veggie, I totally share all the expenses. It costs up to 2300 per head. Once in a week chicken, twice double omelet, twice egg. Balance 80504.I have insured my life, every month I pay 700. Balance 73505. Emergency savings, 1000 rupees, every month. Balance 6350.6. Once in a month movie (any language, just for a change) 300 spent. Balance 6050.7. Electricity and water bill 500 per head. Balance 5550.8.Recharge (phone, TV) 400, left out 5150.9. Guests (I mean friends, my friends, my room-mates friends, family members). Mostly once in two months this comes into picture. Approximately 800, balance 435010. 1000-2000, giving to my parents. Balance 2350.11.200 every month, to ISKCONs AKSHAYA PATRA. 215012.Miscellaneous 500.13.Balance for future.I'm happy. I'm sure with 7000 more, you can lead a royal life in my terms.Strictly no alcohol, cigarettes (all four)Thank you.Edit: I wrote my way of living with what salary I get is to inspire the OP, no offense, I had gone wrong in my calculation part in point 7,I completely agree(which now I have edited). Don't take me wrong. Practically I'm living in this way.About clothes, gifts other expenditure, once in 3/4 months I will get bonus which will be roughly 2500-4000, depending on the target reached. But I will get for sure.About my room, our owner struggled through the same phase, luckily he comes from my native district, so we got it for a 1000 lesser.Thank you.

There so many ways you decide to get paid for your services. Being a Digital Marketer over the years I going by the need of the clientsfollowing are charges I am taking from the different Clients-Lead Generation Campaign- 7000 for 15 daysPPC Campaign creation - 10000E-Mail Marketing- 5000Content MARKETING - 8000Twitter Marketing - 6000Social MEDIA Optimization- 6000 (4 Channels)Google Adwords - 5000LinkedIn Marketing- 8000All above charges are monthly or quarterly basins in INR

I'll mention the differences present at the shuffle side at a very high level, as I understand it, between Apache Spark and Apache Hadoop Map reduce.Since few folks have already mentioned about difference in terms of I/O etc, I'll stick to only the shuffle difference.First Let's discuss the Map side differences:Map Side of Hadoop Map Reduce ( see left side of the image above ):Each Map task outputs the data in Key and Value pair.The output is stored in a CIRCULAR BUFFER instead of writing to disk.The size of the circular buffer is around 100 MB. If the circular buffer is 80% full by default, then the data will be spilled to disk, which are called shuffle spill files.On a particular node, many map tasks are run as a result many spill files are created. Hadoop merges all the spill files, on a particular node, into one big file which is SORTED and PARTITIONED based on number of reducers.Map side of Spark ( see right side of the image above)Initial Design:The output of map side is written to OS BUFFER CACHE.The operating system will decide if the data can stay in OS buffer cache or should it be spilled to DISK.Each map task creates as many shuffle spill files as number of reducers.SPARK doesn't merge and partition shuffle spill files into one big file, which is the case with Apache Hadoop.Example: If there are 6000 (R) reducers and 2000 (M) map tasks, there will be (M*R) 6000*2000=12 million shuffle files. This is because, in spark, each map task creates as many shuffle spill files as number of reducers. This caused performance degradation.This was the initial design of Apache Spark.Shuffle File Consolidation:Same as above.The only difference being, the map tasks which run on the same cores will be consolidated into a single file. So, each CORE will output as many shuffle files as number of reducers.Example: If there are 6000(R) and 4 (C) Cores, the number of shuffle files will be (R*C) 6000*4=24000 shuffle files. Note the huge change in the number of shuffle files.Now to the reduce side differences:Reduce side of Hadoop MR:PUSHES the intermediate files(shuffle files) created at the map side. And the data is loaded into memory.If the buffer reaches 70% of its limit, then the data will be spilled to disk.Then the spills are merged to form bigger files.Finally the reduce method gets invoked.Reduce side of Apache Spark:PULLS the intermediate files(shuffle files) to Reduce side.The data is directly written to memory.If the data doesn't fit in-memory, it will be spilled to disk from spark 0.9 on-wards. Before that, an OOM(out of memory) exception would be thrown.Finally the reducer functionality gets invoked.Sources:Page on berkeley.edu